Current Research Opportunities

Department

Applied Physics and Applied Mathematics

Project Title

Assemble the CUTE Tokamak – Columbia University Tokamak for Education (filled)

Description

A new plasma experiment – the CUTE tokamak – is being assembled at the Columbia Plasma Physics Lab. The goal of the experiment is to provide a testbed for plasma control experiments with a focus on training and education. Opportunities for student projects exist in all aspects of the assembly, including mechanical design and fabrication or procurement of structural components, electrical design of power electronic circuits to power the device, and instrumentation and control to design interfaces to power the electromagnets of the experiment. Please indicate in your application which area you feel you are most qualified for. Students will also generally assist with other Columbia Plasma Physics Lab initiatives. More information can be found at https://plasma.apam.columbia.edu ** This position and others in Prof Paz-Soldan’s group have a common application ** ** Please apply using the form https://forms.gle/viSUdEneLy66vFaZ6. Do NOT email the PI ** ** Flexibility in project choice is welcome **

Faculty Member

Carlos Paz-Soldan

Qualifications

• Variety of skills are welcome, see project description
• More

Department

Applied Physics and Applied Mathematics

Project Title

Fire and Ice – Measuring Cryogenic Pellet Ablation with Electron Beams (filled)

Description

The Columbia Plasma Physics Lab is in the process of constructing a new experiment to measure the ablation of cryogenic ice under bombardment by high energy electron beams. This experiment will provide a controlled test-bed for the interaction of cryogenic pellets with high temperature fusion plasmas. Cryogenic pellets are essential components of fusion plasma systems, since they are used to fuel the plasma and to rapidly quench its energy when needed. The student will assist in the development of the lab, the specification of components, and the construction of the experiment. Electrical, vacuum, cryogenic, and data acquisition systems will be commissioned, proceeding towards first commissioning tests of the experiment. Students will also generally assist with other Columbia Plasma Physics Lab initiatives. More information can be found at https://plasma.apam.columbia.edu** This position and others in Prof Paz-Soldan’s group have a common application **
** Please apply using the form https://forms.gle/viSUdEneLy66vFaZ6. Do NOT email the PI **
** Flexibility in project choice is welcome **

Faculty Member

Carlos Paz-Soldan

Qualifications

• Lab skills essential, instrumentation and control desirable, vacuum systems desirable
• More

Department

Applied Physics and Applied Mathematics

Project Title

Non-Planar High-Temperature Superconducting Magnets (filled)

Description

This project involves assisting in the design and fabrication of non-planar high-temperature superconducting (HTS) magnets for the Columbia Plasma Physics Lab. HTS magnets offer access to higher magnetic field at higher operating temperature, opening new experimental opportunities for plasma physics study.  Non-planar magnet geometries are needed to enable finding the best 3D shape of future fusion reactor. The student will participate in design and modeling in COMSOL and SolidWorks, and assist in fabricating test apparatus for prototype magnets. Prototypes will be 3D printed at the Columbia Makerspace. Initial prototypes will be tested to assess their performance towards the current limit at cryogenic temperatures. Students will also generally assist with other Columbia Plasma Physics Lab initiatives. More information can be found at https://plasma.apam.columbia.edu** This position and others in Prof Paz-Soldan’s group have a common application **
** Please apply using the form https://forms.gle/viSUdEneLy66vFaZ6. Do NOT email the PI **
** Flexibility in project choice is welcome **

Faculty Member

Carlos Paz-Soldan

Qualifications

• Electricity and magnetism, SolidWorks desirable, lab skills desirable
• More

Department

Applied Physics and Applied Mathematics

Project Title

Predicting the Tokamak Edge with Machine Learning (filled)

Description

The development of predictive models for plasma profiles in fusion energy systems is essential for accurate device planning and design. In this project, we will develop a neural net with this purpose in mind by analyzing and training on a large set of existing data from the DIII-D
Tokamak in San Diego. The model will be tested on selected datasets from DIII-D and other machines around the world and should be able to accurately predict the width and height of the plasma pressure pedestal from generalized inputs. Applicants should have a strong coding background, preferably in python. Students will also generally assist with other Columbia Plasma Physics Lab initiatives. More information can be found at https://plasma.apam.columbia.edu** This position and others in Prof Paz-Soldan’s group have a common application **
** Please apply using the form https://forms.gle/viSUdEneLy66vFaZ6. Do NOT email the PI **
** Flexibility in project choice is welcome **

Faculty Member

Carlos Paz-Soldan

Qualifications

• python, ideally some machine learning experience
• More

Department

Applied Physics and Applied Mathematics

Project Title

Pythonic GUI Interface for TokaMaker Software (filled)

Description

Scientists at Columbia have recently developed a flexible new modeling suite called TokaMaker, which is able to reconstruct equilibria, assess stability and optimize time-dependent control schemes for tokamak plasmas. This will be an essential tool in the design and preparation of
future fusion energy systems. In this project, we will develop a GUI interface for the new code, focusing on flexibility in use and advances in physics understanding. The GUI will be made publicly available to the fusion community, ensuring a broad and lasting impact for the code. Applicants should have a strong coding background, preferably in python. Students will also generally assist with other Columbia Plasma Physics Lab initiatives. More information can befound at https://plasma.apam.columbia.edu** This position and others in Prof Paz-Soldan’s group have a common application **
** Please apply using the form https://forms.gle/viSUdEneLy66vFaZ6. Do NOT email the PI **
** Flexibility in project choice is welcome **

Faculty Member

Carlos Paz-Soldan

Qualifications

• Python
• More

Department

Applied Physics and Applied Mathematics

Project Title

Basis Functions to Measure 3-D Magnetic Fields in Tokamak Plasmas (filled)

Description

The objectives of this work are to provide a learning opportunity for plasma physics and applied mathematics in fusion research as well as to produce an improved experimental analysis tool for the DIII-D National User Facility community. The successful applicant will be asked to contribute
to the spatial fitting of quasi-stationary non-axisymmetric magnetic perturbations using measurements from the “3D” magnetics diagnostic set in the DIII-D tokamak [E. Strait Rev. Sci.Instrum. 2006, Strait Phys. Plasmas 2015]. Tokamaks are designed to be donut shaped plasma
columns that are symmetric going (toroidally) around the donut. These fits are used to assess the growth of unstable modes as well as probe the response to forced distortions that break this symmetry of the tokamak [E. Strait Rev. Sci. Instrum. 2016]. Such mode fitting has thus far used
simple cylindrical-coordinate Fourier decomposition, which is a good choice for a donut with a circular cross-section. The DIII-D tokamak, however, is “D” shaped in cross-section. This project will look at ways of improving the basis functions of the fit to more naturally describe the expected asymmetries in this oddly shaped plasma. This student will contribute directly to magnetics analysis tools used at DIII-D, with the objective of demonstrably improving an important tool used by experts in cutting edge fusion science research. Students will also generally assist with other Columbia Plasma Physics Lab initiatives. More information can be
found at https://plasma.apam.columbia.edu
** This position and others in Prof Paz-Soldan’s group have a common application **
** Please apply using the form https://forms.gle/viSUdEneLy66vFaZ6. Do NOT email the PI **
** Flexibility in project choice is welcome **

Faculty Member

Carlos Paz-Soldan

Qualifications

• python, electromagnetic
• More

Department

Applied Physics and Applied Mathematics

Project Title

Kink Instability Limits in Distorted Tokamak Plasmas (filled)

Description

This project will implement the calculation of a new theoretical limit in the size of “3D” distortions a tokamak plasma can withstand before breaking apart. Tokamaks are designed to be donut shaped plasma columns that are symmetric going (toroidally) around the donut. Asymmetries as
small as δB/B0 ~ 1e-4 (i.e. a perturbation “error field” 10,000 times smaller than the primary tokamak magnetic field) can drive “reconnection” of field lines that destroys the otherwise good confinement of these toroidal plasmas. Before this, the plasma is simply “kinked” and maintains
concentric surfaces in which field lines lie without crossing the field lines on neighboring surfaces. A theoretical approach to determining the limit at which kinks lead to magnetic reconnection is given in [A. Boozer, Physics of Plasma 2019]. At some point, the plasma becomes exponentially sensitive to resistivity and the surfaces fall apart due to any small
resistivity. This project will be to implement this theoretic threshold calculation in the Generalized Perturbed Equilibrium Code (GPEC), and predict when this might happen for real tokamak plasmas such as the ones at the DIII-D national user facility in San Diego.Students will also generally assist with other Columbia Plasma Physics Lab initiatives. More information can be found at https://plasma.apam.columbia.edu
** This position and others in Prof Paz-Soldan’s group have a common application **
** Please apply using the form https://forms.gle/viSUdEneLy66vFaZ6. Do NOT email the PI **
** Flexibility in project choice is welcome **

Faculty Member

Carlos Paz-Soldan

Qualifications

• python, electromagnetism
• More

Department

Applied Physics and Applied Mathematics

Project Title

Machine Learning for Tokamak Three-Dimensional Field Thresholds (filled)

Description

The tokamak, a leading device design for magnetic confinement fusion energy reactors, performs well due to the capture of charged plasma particles on its toroidal flux surfaces. However, it is possible for the nice concentric flux surfaces to “tear” and open up “magnetics islands” that cause radial transport of particles and heat [YouTube Lecture on Islands, Poli IPP Thesis 2012 (Ch 1-2.1)]. Asymmetries as small as δB/B0 ~ 1e-4 (i.e. a perturbation “error field” 10,000 times smaller than the primary tokamak magnetic field) can drive a plasma instability.This project will look at a database of experiments that did this intentionally, and use that data to determine what level of asymmetry would cause tearing in a future reactor so operators might avoid it or force it as they please. Specifically, this project will concentrate on Machine Learning (ML) approaches for predicting the RMP thresholds. A second objective of the work will be to identify the most impactful new experimental observations that could be obtained within the available operating space. The ML based approach will require an investigation of the relative importance of different plasma parameters in determining this threshold, which will in turn guide what new experiments should be performed to improve our understanding.
Students will also generally assist with other Columbia Plasma Physics Lab initiatives. More information can be found at https://plasma.apam.columbia.edu
** This position and others in Prof Paz-Soldan’s group have a common application **
** Please apply using the form https://forms.gle/viSUdEneLy66vFaZ6. Do NOT email the PI **
** Flexibility in project choice is welcome ** 

Faculty Member

Carlos Paz-Soldan

Qualifications

• python, electromagnetism, ideally some experience with machine learning or data science
• More

Department

Applied Physics and Applied Mathematics

Project Title

Symplectic integration for the dynamics of fusion products

Description

Magnetic confinement fusion systems rely on the confinement of the charged fusion products, the alpha particles, for a sufficiently long time that they can deposit their heat in the hot plasma bulk. One magnetic confinement concept, the stellarator, has historically suffered from poor confinement of alpha particles. Numerical optimization algorithms have recently demonstrated the ability to obtain stellarator magnetic fields that could be candidates for a fusion energy device. This project focuses on improvements in the numerical integration schemes used for evolving the trajectories of alpha particles in a three-dimensional fusion system, enabling more efficient design and modeling of these devices. The student will perform modification of an existing open-source C++ and python library to improve the accuracy and performance of the integration routines. This will be achieved through application of symplectic algorithms, improved parallelism, and other code optimization techniques. The student must have a strong computing background, with preference for experience in high-performance computing and scientific computing.

Faculty Member

Elizabeth Paul

Qualifications

• C++, python, scientific computing, E&M (desired)
• More

Department

Applied Physics and Applied Mathematics

Project Title

Computing phonons from first principles (filled)

Description

This project involves computing phonons from the first-principles of quantum mechanics. Two common approaches used for computing phonons are finite difference based techniques and linear response. This project aims to quantitatively assess the pros and cons of each technique in a variety of different systems. Prospective students should have a background in materials physics, programming, and open source density functional theory codes.

Faculty Member

Chris Marianetti

Qualifications

• Materials science, condensed matter physics, programming in python, open source density functional theory codes
• More

Department

Applied Physics and Applied Mathematics

Project Title

Electrolyte screening for high-performance Na-K/S batteries (filled)

Description

The research position targets to develop electrolytes for Na-K/S battery for Long-duration energy storage (>10 hours, LDES), which is critical to the deep penetration of intermittent renewable energy (e.g., solar/wind). Conventional Na-S and K-S batteries are attractive for LDES due to their low cost and the use of only earth-abundance elements. However, their
deployment is severely hindered by their high operational temperature of 300-350 oC and associated degradation and safety issues. This project will develop innovative electrolytes to dissolve insoluble reaction products in Na-S and K-S batteries and advance knowledge on underlying dissolution mechanisms. Such novel electrolytes will enhance reaction kinetics so the operation temperature can be reduced to 60-120 oC, which not only enhances thermal stability but also decreases operational costs. The student will be responsible for examining solubility of sulfides and polysulides in different solvents at different temperatures, and performing cyclic voltammetry to understand electrochemical stability of these electrolytes.

Faculty Member

Yuan Yang

Qualifications

• Electrochemistry and general chemistry
• More

Department

Applied Physics and Applied Mathematics

Project Title

Isotope separation by liquid centrifugation (filled)

Description

Liquid centrifugation to separate isotopes is a recently approach developed by the PI of this applicant. Unlike conventional centrifugation, liquid centrifugation uses chemicals with low or no environmental/chemical hazards and corrosion. It is also capable of separating multiple elements simultaneously, such as D, 7Li, and 37Li together in a LiCl aqueous solution. The team is continuing on this research direction and focus on two research frontiers: 1) Model development to simulate isotope separation in liquid centrifugation. 2) Identifying high-performance recipes for liquid centrifugation of D, 7Li, and 37Cl, including both salt solutions and neutral liquid chemicals. and 3) Prototype designing.

The master student will carry out activities such as optimizing simulation code and searching for materials with potentially high performance for separating isotopes. In optimizing simulation code, the master will apply matrix-based calculation and other numerical method to improve the speed of simulation. In materials searching, the master will be responsible for literature searching to find out key physical parameters of relevant materials and estimate if they are suitable for isotope separation.

Faculty Member

Yuan Yang

Qualifications

• Fluidic mechanics, Transport Phenomena, General Chemistry
• More

Department

Applied Physics and Applied Mathematics

Project Title

Anode-free lithium batteries with high energy density (filled)

Description

Anode-free lithium metal battery uses a lithium-containing cathode (e.g. NCA, NCM) and a Cu foil as the current collector for the anode. Such design avoids the use of air-sensitive lithium anode and has a high energy density. This project targets to understand parameters that control lithium deposition/striping and cell performance in such anode-free batteries, and develop new gel polymer electrolytes to enhance cycling performance. The objectives of this project include 1) Understanding parameters controlling lithium deposition properties and coulombic efficiency between Cu foil and electrolytes. 2) Improving the performance of anode-free batteries by interfacial modifications and electrolyte tuning. 3) Developing gel polymer electrolytes for anode-free lithium batteries.

The two master students will assist a postdoc working on this project and carry out research activities such as preparing electrolyte, cell testing, and data analysis. The two master students has started to work in my lab since Jan 1, 2024. They will learn basic skills of battery fabrication and analysis in Spring 2024, and carry out extensive research activities in the summer
 

Faculty Member

Yuan Yang

Qualifications

• Electrochemistry and energy storage
• More

Department

Applied Physics and Applied Mathematics

Project Title

AI agents for real-time x-ray data reduction protocols (filled)

Description

The project will develop a flexible, configurable set of data analysis “agents” for x-ray data reduction from materials science experiments, operating in the the NSLS-II software ecosystem, that do granular tasks but can be chained together to do more complex tasks. Such software components, or agents, will allow users to quickly build bespoke analysis pipelines that allow them to compare datasets from different sample and different runs, optimize analysis conditions, take scaled differences and so on, on a time-scale that they can make meaningful decisions about future measurements based on results from previous ones. We will explore the use of ML and AI in these tools as the software develops.

Faculty Member

Simon Billinge

Qualifications

• Python
• More

Department

Applied Physics and Applied Mathematics

Project Title

Automated Grain Boundary Detection in Bright-Field Transmission Electron Microscopy Images (filled)

Description

Most technologically useful materials are polycrystalline microstructures composed of a myriad of small monocrystalline grains separated by grain boundaries.  The aim of the project is to further advance the use of machine learning to automatically trace grain boundaries in bright-field transmission electron micrographs for subsequent statistical analysis of microstructural metrics, both static and dynamic.  The project has had a strong track record of undergraduates as coauthors on manuscripts.

Faculty Member

Katayun Barmak

Qualifications

• python
• More

Department

Applied Physics and Applied Mathematics

Project Title

Designing singular structures in bar-joint networks

Description

The investigation of singular structures occupies a pivotal position within the field of mechanism and robot kinematics. A characteristic feature of most robotic systems is their under-constrained nature, affording them a degree of deformability without breaking the systems. Typically, the deformation space of these systems is described as "smooth," indicating that the kinematic properties of the system transition seamlessly during deformation. However, certain configurations, identified as singularities, prompt abrupt and significant changes in the kinematic properties of a system. Recognizing and understanding these singular structures, along with developing systematic strategies for their design, holds substantial practical significance.

For the forthcoming summer project, our goal is to numerically detect singular structures in a specific robotic system, namely the bar-joint network, utilizing a numerical saddle point search scheme. A prior understanding of singularity in robotic systems is not necessary. We will provide an introduction to the fundamental concepts of singular structures before proceeding to investigate them numerically, with the objective of enhancing our design skills concerning these structures.

Faculty Member

Xuenan Li

Qualifications

• Participants are only required to have foundational knowledge in linear algebra, multi-variable calculus, and some programing skills (Matlab, Python or Julia are all welcome).
• More

Department

Applied Physics and Applied Mathematics

Project Title

3Demos Developer (filled)

Description

3Demos (http://3Demos.ctl.columbia.edu/) is a web application developed for the creating and sharing of visualizations for multivariable calculus concepts for the purpose of education. We seek to expand the library of demonstrations ("Story Mode" in the link above) to include more advanced mathematical concepts as well as more creative examples of fundamentals. The position will also include some code maintenance, documentation, and the like. Throughout, students will advance their understanding of mathematics, computer graphics, and web development.
Experience with Javascript/HTML/CSS preferred, but interested students with programming experience in other environments should apply.

Faculty Member

Drew C Youngren

Qualifications

• mathematics through linear algebra, Javascript or similar language
• More

Department

Biomedical Engineering

Project Title

Fully implantable cochlear implant microphone (filled)

Description

The student will work with myself,  a Columbia medical student researcher and a Columbia Mechanical Engineering doctoral student.  We will be further developing a fully implantable microphone, composed of a  piezoelectric polymer.  The microphone's basic design has been patented (collaboration between Columbia, Harvard and MIT).  It works by being implanted in the middle ear space, in contact with one of the small bones that connect the eardrum to the inner ear.  These small bones vibrate in synchrony with incoming sound pressure, and this vibration is detected by the implanted microphone.   The summer work by the student will entail developing and testing the microphone and its fixation device on the lab bench and developing and testing a radio-wave transmitter and receiver we will use for testing the microphone in live sheep in later years.  This is a hands-on mechanics and electronics project.  The student will attend weekly zoom group meetings with the Harvard and MIT groups, and weekly lab meetings with my Columbia group.  The student will submit written reports approximately weekly, and a complete report at the end of the summer.

Faculty Member

Elizabeth Olson

Qualifications

• Electronics, Physics, Calculus
• More

Department

Biomedical Engineering

Project Title

Neural information processing in perceptual decision making (filled)

Description

This project aims to study how sensory information is processed in the brain to lead the formation of a decision. In perceptual decision-making tasks, sensory information is accumulated over time in the central nervous system, eventually leading to a decision to choose one of the alternatives and generating motor commands to indicate the animal’s choice. The perceptual decision-making process is shaped by many factors, including brain state, the gain/loss of each possible decision, motivation, task engagement, reward size, and prior knowledge about upcoming sensory signals. The student will analyze spiking data that we recorded during perceptual decision making tasks. During the task, we systematically manipulated prior knowledge of upcoming sensory signal and reward size. The manipulation of these experimental variables altered the animals’ behavior. We aim to identify neural representation of these experimental variables from our neural recordings. The student is expected to have experience with analyzing spiking data and have some basic knowledge about psychophysics experiments.

Faculty Member

Qi Wang

Qualifications

• Analysis of neuronal spiking data and psychophysics behavioral data
• More

Department

Biomedical Engineering

Project Title

Bioengineered human tissue models to recapitulate breast cancer dormancy (filled)

Description

The recapitulation of metastatic disease with engineered tissues holds promise to advance our understanding of cancer progression and to improve the efficacy of therapies targeting secondary sites of metastasis which are currently poorly responsive to treatment. Bioengineered human tissue models of metastatic sites entail biologically meaningful milieus, where stromal and immune cells support the infiltration and maintenance of disseminated cancer cells within a 3D architecture. In ongoing research, our focus is on the bone marrow niche, engineered to include stromal components along with healthy HSPCs. This tissue model in integrated culture with metastatic breast cancer cells allows for the characterization of changes induced in a key target organ of metastasis during colonization, including changes to healthy hematopoiesis and remodeling of ECM composition and organization. This project is part of a broader effort to establish a fully isogenic breast cancer metastasis platform that can be used to monitor disease progression within controlled, biomimetic microenvironments. In particular, isolated studies of the colonization of the eBM niche will inform subsequent integrated studies to understand the transition of cancer cells into and out of the cell-cycle in a multi-tissue context.

Faculty Member

Gordana Vunjak-Novakovic

Qualifications

• Students must have knowledge of cell culture and basic Tissue Engineering concepts
• More

Department

Biomedical Engineering

Project Title

Title: Engineering a Strategy for the Growth and Rehabilitation of Living Allogenic Heart Valves to Treat Children with Congenital Valve Disease (filled)

Description

Pediatric patients with congenital heart valve disease undergo multiple reoperations throughout their lifetime to replace structurally degraded or outgrown valve replacements. The shortcomings observed in currently available cryopreserved valvular homografts (the clinical gold-standard) are motivating the introduction of living allogenic valve transplantation (LAVT). In LAVT, human allografts are harvested from a donor and immediately transplanted in the recipient, with lifelong immunosuppression preventing host immune response to the graft. This strategy has the ability to deliver a living valvular graft with the potential for growth and self-repair. Nevertheless, several key limitations thwart its widespread clinical implementation: limited donor availability, uncertainties surrounding ex vivo tissue viability, and graft immunogenicity. What remains lacking is a living valvular allograft that can be transplanted off-the-shelf with minimal risk of host rejection. Therefore, this project’s goal is to develop a strategy for the preservation of living valvular allografts. To accomplish this, the project’s specific aims are to: (i) identify the pathology associated with structural valve degeneration in currently-available cryopreserved allografts, and (ii) design a storage environment for preserving allograft viability and function ex vivo. We hypothesize that environmental control of the key factors inducing valve degradation, combined with biomimetic physical stimuli, will enable maintenance of valve viability, microarchitecture, and function.

Faculty Member

Gordana Vunjak-Novakovic

Qualifications

• Cell culture and basic tissue engineering concepts
• More

Department

Biomedical Engineering

Project Title

Investigating mechanisms underlying HNRNPH2-Related  (filled)

Description

The goal of this summer project is to rigorously characterize the generated mutant iPSC lines and their isogenic controls to model HNRNPH2 Related Neurodevelopmental Disorder. We hypothesize that the mutant lines will display defects in neuronal differentiation, and/or functional neural network communication. This summer project will be a critical component to discover novel biomarkers that may be used as targets in future therapeutic intervention for this monogenetic disorder.

Faculty Member

Gordana Vunjak-Novakovic

Qualifications

• Students must have knowledge of cell culture and basic Tissue Engineering concepts
• More

Department

Biomedical Engineering

Project Title

Cardiac Fibroblast BAG3 contribution to heart disease (filled)

Description

Mutations in Bcl2-associated athanogene 3 (BAG3) are associated with dilated cardiomyopathy (DCM), a highly prevalent heart disease characterized by an enlarged left ventricle (LV), systolic dysfunction, and fibrosis. Aside from monogenic disease, loss of BAG3 is observed in patients with non-genetic heart failure (HF), making it an attractive therapeutic target. BAG3 is a ubiquitously expressed co-chaperone protein with key binding domains critical to protein quality control. In cardiomyocytes (CMs),
BAG3 forms a complex with heat shock protein (HSP) 70 and HSPB8 to chaperone
sarcomere proteins denatured by mechanical stress. However, BAG3’s function in other cell types is unexplored. Mature engineered heart tissues (EHTs) formed with BAG3 KO CFs and WT CMs mimic the clinical phenotype of DCM of decreased contractility and increased matrix deposition, highlighting the importance of BAG3 in CFs to cardiac function. This project focuses on Cardiac Fibroblast BAG3 and its contribution to heart disease and fibrosis. The project will use a range of molecular biology techniques, such as RT-qPCR, Western Blot, luciferase assays, combined with tissue engineering approaches to develop EHTs.

Faculty Member

Gordana Vunjak-Novakovic

Qualifications

• Students must have knowledge of cell culture and basic Tissue Engineering concepts
• More

Department

Biomedical Engineering

Project Title

Lung Bioreactors to Model Gene Therapy of Cystic Fibrosis (filled)

Description

We developed lung bioreactor models that allow for multiplexed, nondestructive monitoring of tissue function and for gene delivery through clinically relevant means. A small “mucosal tissue bioreactor” houses thin slices of mucosa in air-liquid interface culture in a chamber designed for grab-and-go non-destructive monitoring.
Multiscale lung bioreactors can be used to model CF lung disease in ways that permit both high-throughput and at-scale testing of putative gene therapeutics. A focus on the CF biophysical environment, including viscous airway mucus, helps to address specific barriers. This model recapitulates the CF lung environment to enable high-throughput and clinical-scale testing of CF gene therapy.

Faculty Member

Gordana Vunjak-Novakovic

Qualifications

• Students must have knowledge of cell culture and basic Tissue Engineering concepts
• More

Department

Biomedical Engineering

Project Title

Temporal Dynamics of Immune Cells in Patients with Graft vs Host Disease (filled)

Description

This project aims to conduct a comprehensive analysis of Graft vs Host Disease (GVHD) patient data, focusing on identifying key trends in time-seris T cell lineage genomic data and understanding the impact of different treatments on patient outcomes. By leveraging statistical methods and data visualization techniques, the student will explore patterns in disease progression and treatment response.

Faculty Member

Elham Azizi

Qualifications

• Molecular Biology, Programming, Linear Algebra, Statistics
• More

Department

Biomedical Engineering

Project Title

Mechanobiology of vertebrate gut morphogenesis

Description

The broad goals of the lab are to understand how a single cell builds the precise, complex morphologies of our varied tissues and organs. We specifically focus on understanding how biochemical and biomechanical aspects of embryonic development are integrated, with a particular interest in endoderm-derived organs. The trainee will gain experience in embryology, microscopy, molecular biology, and soft tissue biomechanics.

Faculty Member

Nandan Nerurkar

Qualifications

• Cell biology, programming
• More

Department

Biomedical Engineering

Project Title

Mechanobiology of vertebrate gut morphogenesis

Description

Mechanical instability is a core mechanism of shaping tissues during embryonic develpoment. The focus of this project is to study the cell behaviors and governing signaling pathways that regulate tissue mechanics during buckling morphogenesis of the small intestine.

Faculty Member

Nandan Nerurkar

Qualifications

• biomechanics, cell biology
• More

Department

Biomedical Engineering

Project Title

Synovial Joint Research (filled)

Description

Students will work on projects in the Cellular Engineering Laboratory (CEL) related to knee joint and factors that lead to joint degeneration and osteoarthritis.  Students will work with cell and tissue culture, mechanical testing, biochemical and molecular assays.

Faculty Member

Clark Hung

Qualifications

• Cell and tissue culture
• More

Department

Biomedical Engineering

Project Title

GUI Development for Advancing Breast Cancer Detection through Ultrasound-Based Imaging

Description

We are offering an exciting summer internship opportunity focused on developing a user-friendly Graphical User Interface (GUI) using MATLAB for one of our ongoing works on finite element analysis of acoustic wave propagation in soft tissue, aiming to advance the detection and monitoring of breast cancer through ultrasound-based imaging. The intern will gain valuable insights into (1) ultrasound-based imaging applications in biomedical engineering, (2) acquire a basic understanding of finite element analysis for solving partial differential equations, and (3) develop proficiency in using MATLAB for analysis and GUI development. This role provides a unique chance to contribute to cutting-edge research and enhance your skills in a collaborative and dynamic environment. Basic familiarity with MATLAB is advantageous for this hands-on opportunity.

Faculty Member

Dr. Elisa E. Konofagou

Qualifications

• Linear Algebra and basic coding skill (MATLAB is preferred)
• More

Department

Biomedical Engineering

Project Title

Age and Gender Impact on Acoustic Attenuation of Mouse Skulls (filled)

Description

Transcranial focused ultrasound (tFUS) holds promise for treating various brain diseases, including gliomas, Alzheimer’s, and Parkinson’s. Numerous studies involving rodent models are underway to understand the mechanisms and enhance therapeutic interventions. A crucial concern in tFUS is the attenuation of ultrasound energy through the skull, impacting the delivery of acoustic pressure to the brain target. Many preclinical studies use a fixed value (18-20%) for mice skulls, neglecting age and gender influences on skull attenuation. This project aims to investigate the age and gender impact on acoustic attenuation in mouse skulls. Mice skulls of different ages and genders will be harvested, and their attenuation will be measured using an ultrasound transducer and a hydrophone. Changes in skull thickness and density related to age and gender will be investigated through micro-CT scans.
The student is expected to be present in the lab every weekday for 6 hours, with flexibility based on workload. Responsibilities include conducting the water bath experiment, analyzing acoustic measurements and micro-CT data, and documenting progress reports.

Faculty Member

Dr. Elisa Konofagou

Qualifications

• Ultrasound
• More

Department

Biomedical Engineering

Project Title

Ultrasound Elastography for Cardiac Diagnostics (filled)

Description

Have you ever wondered how doctors look at our hearts? Ultrasound is an important point-of-care imaging modality for cardiac diagnostics. In addition to existing clinical uses for qualitative assessments, this project focuses on creating reliable objective assessments of cardiac function through tracking the movement of the heart muscle throughout the cardiac cycle. This project uses raw ultrasound signal to calculate strain and electrical activation to diagnose high-risk problems such as ischemia, infarction, and arrhythmia. This project has the potential to impact the speed and accuracy of diagnostics and increase health awareness. We have clinical projects in the Emergency Department and Cardiology, where we gather patient data for validation of our methods’ accuracy and precision. Interns will be able to help with equipment testing and optimization, code development, automation, and clinical data collection and interpretation. This project is great for a wide range of students, especially Mechanical, Electrical, Computer, and Biomedical Engineering students interested in healthcare and health-related research. Please contact Hannah ([email protected]) or Melina ([email protected]) with any questions!

Faculty Member

Dr. Elisa Konofagou

Qualifications

• MATLAB, Python, Physics
• More

Department

Biomedical Engineering

Project Title

Development of vessel-mimicking phantom for ultrasound imaging

Description

Pulse Wave Imaging (PWI) is a technique that visualize the propagation of the pulse wave along arteries using high framerate ultrasound, deriving pulse wave velocity and blood flow profile to determine arterial stiffness and other parameters relating to arterial health. The goal of this project is to develop carotid artery phantoms with tissue matching acoustic properties to test PWI under a controlled environment. The study will explore complex phantom making techniques to mimic diseased arterial geometries and mechanical properties such as viscoelasticity. Current works involve CAD modeling molds and casting phantoms, designing experimental setups to study the pulse wave propagation in fluids and solids, and ultrasound data postprocessing. An example of the project is published at https://www.umbjournal.org/article/S0301-5629(23)00306-X/fulltext .
We seek students interested in the fabrication and experimental setup of the arterial phantoms as well as data processing and algorithm optimization aspect of PWI.

Faculty Member

Dr. Elisa Konofagou

Qualifications

• MATLAB, advance knowledge of mechanics or signal processing is desired
• More

Department

Biomedical Engineering

Project Title

Engineering bacteria for cancer therapy (filled)

Description

The design and engineering of bacterial strains to enhance localization to tumors, be safe for clinical application and for releasing payloads such as cytokines for cancer therapy

Faculty Member

Tal Danilo

Qualifications

• bacterial work, cell culture
• BS
• More

Department

Biomedical Engineering

Project Title

Engineering bacteria for cancer therapy

Description

The design and engineering of bacterial strains to enhance localization to tumors, be safe for clinical application and for releasing payloads such as cytokines for cancer therapy

Faculty Member

Tal Danilo

Qualifications

• bacteria culture, cloning, cell culture
• MS
• More

Department

Biomedical Engineering

Project Title

Engineering liposome-based hydrogels (filled)

Description

The student will develop and characterize a library of polymer-nanoparticle hydrogels with different nanoparticles using techniques as described by Dr. Correa’s research (https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202103677 ) beginning with liposomes, lipid nanoparticles, polystyrene nanoparticles, and gold nanoparticles. To make liposomes, a student will use both a microfluidic automatic nanoparticle system and rotary evaporator thin film rehydration, and hydrogels will be syringe-mixed and rheometrically characterized with our rheometer. Techniques include thin film evaporation, liposome extrusion, confocal microscopy, rheometric characterization, work with microfluidics, and basic lab techniques.

Faculty Member

Santiago Correa

Qualifications

• cell biology, biomaterials
• More

Department

Biomedical Engineering

Project Title

Molecular engineering of immunomodulatory hydrogels (filled)

Description

Students will work at the intersection of chemistry, materials engineering, and biology and will contribute to on-going efforts to develop self-assembling materials that can provide immunomodulatory cues to immune cells.

Students will learn to use bioconjugate techniques to introduce new functionality in polysaccharide or protein-based biopolymers, and will learn analytical techniques to characterize these materials at the molecular or macromolecular level. Students will also assist in developing assays to determine the impact of engineered materials on cultured cells.

Faculty Member

Santiago Correa

Qualifications

• cell biology, biomaterials
• More

Department

Biomedical Engineering

Project Title

Mobile app for disease tracking (filled)

Description

Student will work closely with a graduate student on an independent project.  The project will focus on developing mobile apps for blood tests. The project will focus on writing a mobile app for iOS and Android. Mobile development: front-end app development, back-end/server-side development, React/React native framework. Exposure to ML/DL. Students with coding experience in any language and a willingness to learn are also encouraged to reach out.

Faculty Member

Samuel Sia

Qualifications

• JavaScript, Python. Students with coding experience in any language and a willingness to learn are also encouraged to reach out.
• More

Department

Biomedical Engineering

Project Title

Drug release hydrogels to improve wound healing (filled)

Description

"Student will work closely with a graduate student on an independent project for a wearable patch to improve wound healing. Student will work with hydrogels and drug release, as well as characterization with actuation methods involving a small degree of
knowledge in electronics and software."

Faculty Member

Samuel Sia

Qualifications

• Small degree of knowledge in electronics and software preferred but not necessary.
• More

Department

Biomedical Engineering

Project Title

Wearable device for health monitoring (filled)

Description

Student will work closely with a graduate student on an independent project for wearable devices for health monitoring. Students will work with a subset of circuit design, electronics, and software, including ML/DL.

Faculty Member

Samuel Sia

Qualifications

• Some knowledge of circuit design and electronics preferred but not required.
• More

Department

Biomedical Engineering

Project Title

COVID PCR diagnostics (filled)

Description

Student will work closely with a graduate student on a project to build a PCR device for monitoring COVID. Students will work on CAD design, design and testing of instrumentation, design and testing with microfluidic chips, and/or performing PCR.

Faculty Member

Samuel Sia

Qualifications

• CAD design, microfluidic chips.
• More

Department

Biomedical Engineering

Project Title

3D printing of protein-based scaffolds (filled)

Description

This project involves the 3D printing of the plant-derived protein, zein, for use as a tissue engineering scaffold. Solvent and melt processing will be explored as well as the incorporation of cross-linkers to form a hydrolytically stable 3D construct. Characterization will include mechanical testing and degradation studies.

Faculty Member

Treena Arinzeh

Qualifications

• biomaterials processing and characterization, biocompatibility testing
• More

Department

Biomedical Engineering

Project Title

CT lung image harmonization across large cohorts of mulitple scanners and sites (filled)

Description

These projects will include application of AI to study emphysema and COPD by analyzing large cohorts of CT images from general (MESA) and diseased (SPIROMICS) populations. The students will carry out unsupervised machine / deep learning learning methods to spatial and visual / texture features of the lung parenchyma in HRCT scans, independent of all clinical information (demographics). In addition, we have recently begun to develop generative and Markov models to learn of airway tree structure, which our prior work demonstrated is fundamental to understanding COPD risk. The overall goal of this project is to define / discover quantitative lung airway tree subtypes (QTS) in the general population.

Faculty Member

Andrew Laine

Qualifications

• Basic programming skills in Matlab and/or Python.
• More

Department

Biomedical Engineering

Project Title

Neural information processing in perceptual decision making

Description

This project aims to study how sensory information is processed in the brain to lead the formation of a decision. In perceptual decision-making tasks, sensory information is accumulated over time in the central nervous system, eventually leading to a decision to choose one of the alternatives and generating motor commands to indicate the animal’s choice. The perceptual decision-making process is shaped by many factors, including brain state, the gain/loss of each possible decision, motivation, task engagement, reward size, and prior knowledge about upcoming sensory signals. The student will analyze spiking data that we recorded during perceptual decision making tasks. During the task, we systematically manipulated prior knowledge of upcoming sensory signal and reward size. The manipulation of these experimental variables altered the animals’ behavior. We aim to identify neural representation of these experimental variables from our neural recordings. The student is expected to have experience with analyzing spiking data and have some basic knowledge about psychophysics experiments.

Faculty Member

Qi Wang

Qualifications

• Experience with rodent behavioral training
• More

Department

Biomedical Engineering

Project Title

Optimized analysis strategies for in vivo and phantom MR Spectroscopy data acquired on multiple MR platforms (filled)

Description

We are seeking an intern to perform a variety of research duties in support of a MR spectroscopy (MRS) study involving the optimization of in vivo and phantom data analysis. This will be a paid research opportunity for an undergraduate student from SEAS. The goal of the study is to identify optimal and efficient analysis strategies for a range of brain MRS datasets. Mouse, rat, monkey and potentially human brain spectra, with and without a clinical condition, such as a cancerous brain mass, will be assessed.

Faculty Member

Christoph Juchem

Qualifications

• Prior knowledge on basic physics, chemistry and programming. Experience with MRI/MRS data is preferred.
• More

Department

Biomedical Engineering

Project Title

3D Printing models of glioblastomas with accurate mechanical properties (filled)

Description

The McIlvain VIBES lab uses MRI to measure the mechanical properties of the brain with a technique called magnetic resonance elastography (MRE). MRE has many applications, with a big goal of our work to measuring the stiffness of brain tumors. We are currently aiming to build tangible models of the MRE images that we collect. The goal of this project will be to determine best practices for converting MRI images into a format that can be printed and modify a 3D printer to allow for printing materials of specific geometries that have variable mechanical properties.

Faculty Member

Grace McIlvain

Qualifications

• 3D Printing,
• Matlab
• More

Department

Biomedical Engineering

Project Title

Generating a Matlab toolbox for Magnetic Resonance Elastography Data

Description

The McIlvain VIBES lab uses MRI to measure the mechanical properties of the brain with a technique called magnetic resonance elastography (MRE). MRE has many applications, including for studying aging, neurodegenerative disease, brain tumors, and traumatic brain injury. We have developed several tools that work for MRE including subject motion correction, and image acquisition acceleration, and we have implemented them in our MATALB based MRE simulation. We aim to integrate these separate tools to work well together in a single streamlined platform that can be distributed to others. Our goal is to build a flexible open access platform that can be added to in the future.

Faculty Member

Grace McIlvain

Qualifications

• Matlab
• Computer Programming
• More 

Department

Biomedical Engineering

Project Title

Idenifying changes in gene regulation that drive therapeutic resistance (filled)

Description

The McFaline-Figueroa laboratory aims to leverage information regarding how individual cancer cells respond to therapy to identify novel and potent points of intervention. To this end, the lab develops and applies genomic tools at single-cell resolution to account for the innate heterogeneity of tumors cells as well as catalog heterogenous responses to exposure within seemingly similar cells.We are seeking a summer research technician interested in helping to apply  single-cell genomics to answer diverse questions in biology. Experience in molecular biology, mammalian cell culture, PCR and general wet-lab techniques is required. The trainee will gain experience in the development of single-cell CRISPR screens and single-cell transcriptomics libraries.

Faculty Member

José L. McFaline-Figueroa

Qualifications

• Experience in molecular biology
• mammalian cell culture
• PCR and general wet-lab techniques is required.
• More

Department

Biomedical Engineering

Project Title

Comparative Analysis of Fatigue Wear Mechanisms in Immature and Mature Bovine Knee Cartilage (filled)

Description

In this research position, I will develop expertise in dissecting both immature and mature bovine knees. This will involve meticulously dissecting to reveal cartilage on the femur and tibia, as well as learning precise extraction techniques for cartilage from the tibial bone in immature bovines. These skills will be crucial for contributing to CV's project, which will compare fatigue wear mechanisms in mature and immature bovine tissue. The study will aim to determine if mature tissue deteriorates at the same rate as immature tissue and identify contributing factors. Ensuring consistency in sample setup will be essential, requiring uniform flat surfaces for each sample. While extracting samples from immature bovines will be relatively straightforward, dissecting mature specimens will pose challenges due to inconsistent cuts and variations in size. I will need to develop strategies to adapt protocols for consistent cuts and ensure ease of placement within sample holders for future studies.

Faculty Member

Gerard Ateshian

Qualifications

• CAD Modeling, bovine dissection, MATLAB
• More

Department

Biomedical Engineering

Project Title

Single Molecule Fingerprinting (filled)

Description

Our goal is to enable massively parallel identification and quantification of single oligopeptide molecules in very small samples. In its first proof-of-concept implementation, our new method will rely on conjugates of organic receptors with short oligonucleotides. These conjugates will undergo a self-assembly templated by target oligopeptides to provide specific fingerprints. Currently we work on the proof-of-concept of the fundamental steps of this process. Our first experiments aim to demonstrate using the single molecule imaging method DNA-PAINT that a peptide conjugated to a guide oligonucleotide can attract a specific oligonucleotide-organic receptor conjugate, with the organic receptor stabilizing duplex formation through interactions with the specific amino acid side chain. The student will be involved in these single molecule experiments and acquire familiarity with fluorescence microscopy and the data analysis pipeline.

Faculty Member

Henry Hess

Qualifications

• Nanobiotechnology
• More

Department

Biomedical Engineering

Project Title

Reverse-engineering neuro-inspired transformer models

Description

This research project focuses on building a new class of transformer models, inspired by the fundamental properties of the brain, with a particular emphasis on the neural basis of attention. By leveraging empirical findings from studies of attention mechanisms across species, we will extract and apply key computational principles that will inform the development of our models. Our first goal is to engineer a model that mirrors the brain's energy efficiency and implements biologically-constrained strategies for attention modulation. In addition, we plan to extensively train this model on a series of cognitive tasks. This approach will allow us to generate theoretical insights into how cortical attention mechanisms support diverse computational needs across different contexts and task demands.

Faculty Member

Nuttida Rungratsameetaweemana

Qualifications

• ML/AI, deep learning models (prior experience in transformer models is strongly preferred), advanced programming, foundational knowledge in linear algebra, calculus, and statistics.
• More

Department

Biomedical Engineering

Project Title

Building biological multi-agent reinforcement learning models

Description

This research project focuses on probing the neural codes and computational algorithms the brain uses to tackle dynamic reward-learning tasks. We are particularly interested in tasks where the optimal solution not only changes over time but is also influenced by the policies of other agents within the environment. To investigate this, we will create a simulated environment initially featuring a single agent, with a second agent introduced later. Each agent will be constructed as a biologically-plausible reinforcement learning model, each employing distinct, time-varying learning rules. By simulating a wide range of learning rule functions for each agent, we aim to elucidate biological reward-learning mechanisms at play both in individual scenarios and in more complex settings where an agent’s policy is affected by dynamic environmental changes, including the strategies adopted by other agents.

Faculty Member

Nuttida Rungratsameetaweemana

Qualifications

• ML/AI, deep learning models (prior experience in reinforcement learning (RL) models is strongly preferred), advanced programming, foundational knowledge in linear algebra, calculus, and statistics.
• More

Department

Biomedical Engineering

Project Title

Probing memory manifolds

Description

This research project focuses on investigating the neural codes essential for the efficient encoding and maintenance of short-term and long-term memory at the single-neuron and circuit levels in humans. Working alongside myself and our physicist collaborator, you will play an important role in analyzing human electrophysiological datasets. Our goal is to develop a robust, data-driven theory that incorporates concepts of neural geometry and manifolds to enhance our understanding of memory dynamics in healthy individuals and uncover how these processes may be altered in various disorders.

Faculty Member

Nuttida Rungratsameetaweemana

Qualifications

• Python and R, foundational knowledge in linear algebra, calculus, and statistics.
• More

Department

Chemical Engineering

Project Title

Synthesis and process development of encapsulated electrocatalyst materials (filled)

Description

This project is based on investigating the changes in properties and performance of encapsulated electrocatalysts under different processing conditions. Learnings from these encapsulated electrocatalysts will be translated to photocatalytic particles for hydrogen production from water splitting. Photocatalytic water splitting with suspension reactors is an attractive way to produce clean energy because they can be used to directly use sunlight to convert low energy reactants such as water into energy dense and storable chemical fuels such as hydrogen. This project will be primarily experimental in nature, focusing on modifying processing conditions of ultrathin overlayers and exploring the influence of surfactant molecules on electrocatalytic activity and selectivity. Research will also involve the use of advanced materials characterization tools, and analysis of the performance of the fabricated electrodes using a variety of electroanalytical tools.

Faculty Member

Daniel Esposito

Qualifications

• Chemical Engineering and electrochemistry
• More

Department

Chemical Engineering

Project Title

Ultrathin membrane materials for water electrolysis (filled)

Description

This project will investigate suitable precursors and processing conditions for the deposition of oxide-based membrane materials for use in water electrolyzers. Research will include determining the optimal phosphorous content to incorporate into doped oxide membranes fabricated by wet chemical processing. Experience will be gained in the areas of thin film deposition, electrochemical, chemical, and physical characterization of such films under guidance of the mentor and team. Additionally, the student will gain hands-on experience with electroanalytical methods and materials characterization tools like ellipsometry and Raman spectroscopy.

Faculty Member

Daniel Esposito

Qualifications

• Chemical Engineering and electrochemistry
• More

Department

Chemical Engineering

Project Title

Engineering human organogenesis in vitro

Description

The Simunovic lab is at the interface of stem cell biology and cellular engineering, seeking to understand the fundamental rules of how cells become specified toward their fates in early embryogenesis. We employ a combination of techniques, including tissue engineering, CRISPR gene editing screens, live-cell confocal microscopy, and organoid biology, with goals to reconstitute some of the processes in embryogenesis and organogenesis in vitro. We seek students who will contribute to our efforts engineering novel tools to dissecting the signaling mechanisms in human organogenesis.

Faculty Member

Mijo Simunovic

Qualifications

• Wet lab skills, cell biology
• More

Department

Chemical Engineering

Project Title

Engineering microbes for therapeutic development (filled)

Description

Students will engage in research focused on the study and manipulation of protein-carbohydrate interactions for therapeutic and biomedical applications, including vaccine development, as well as engineering microorganisms for the biosynthesis of carbohydrate-based drugs and related enzymes.

Faculty Member

Asher Williams

Qualifications

• Molecular biology, proteins
• More

Department

Chemical Engineering

Project Title

Tunable Product Selectivity on Nano-engineered Protonic-Ceramic Electrocatalysts for CO2 Utilization (filled)

Description

Under the direction of the Co-PI, the student will work closely with the graduate students to assist in setting up necessary instrumentation (e.g., reactors), establishing synthetic methods, and collecting and analyzing experimental data. Moreover, students will participate in weekly group meetings to present and discuss their research progress with the team.

Faculty Member

Juliana S. A. Carneiro

Qualifications

• Chemical Engineering, Basic Chemistry
• More

Department

Chemical Engineering

Project Title

Stabilizing Spouted Beds (filled)

Description

This project involves experiments, simulations and data analysis on the use of vibration to stabilize flow in spouted fluidized beds

Faculty Member

Chris Boyce

Qualifications

N/A

Department

Chemical Engineering

Project Title

Reconfigurable DNA-based nanomaterials (filled)

Description

The project involves the development of DNA-based nanomaterials that respond in the designed way to molecular stimuli and external fields. The work will include the assembly of nanomaterials from DNA and nanoparticles, their characterization using spectroscopic and structural methods, and the study of their structural reconfiguration. The developed systems will be used to explore the manipulation and switching of optical and mechanical properties of the newly developed nanomaterials. 

Faculty Member

Oleg Gang

Qualifications

• Material chemistry methods, experience in wet chemistry lab, material characterization
• More

Department

Chemical Engineering

Project Title

Sustainable biocomposite materials of proteins and polysaccharides (filled)

Description

Proteins have evolved to have an incredible range of functions – from superb catalysts to resilient materials. For these reasons, protein biopolymers have the potential to serve as sustainable, biodegradable materials that could potentially displace petroleum-derived plastics that currently dominate the marketplace. However, protein-based materials typically lack the performance materials properties found in traditional plastic materials. This research project seeks to develop biocompatible processes to improve the strength and ductility of protein-containing materials. To accomplish these goals, a panel of protein biopolymers based on natively evolved protein sequences will be engineered with varying assembly and crosslinking domains. The engineered proteins will be produced in a heterologous host and isolated prior to testing. Critically, these proteins will be used to create composite biomaterials that interact with and strengthen polysaccharide based materials.

Faculty Member

Allie Obermeyer

Qualifications

• The student should have basic knowledge of biology and chemistry and a willingness to learn. Ideally, the student would be familiar with molecular cloning techniques, protein production and purification techniques, and microbial growth.
• More

Department

Chemical Engineering

Project Title

Chemical autoxidation as a route to polymer recycling

Description

Experimental work to use partial oxidation to recycle polymers with improved properties

Faculty Member

Sanat Kumar

Qualifications

• organic chemistry
• More

Department

Chemical Engineering

Project Title

Engineering protein assembly and phase behavior (filled)

Description

We are interested in promoting protein interactions with a host of biological and synthetic polymers for applications in protein stabilization, purification, and delivery as well as in biocatalysis and sustainable materials. By facilitating these interactions we can combine the biological functionality of the protein with the physical properties of the (bio)polymeric materials. The projects will involve genetic engineering of model proteins to increase non-specific intermolecular interactions or to introduce specific binding interactions. These genetically engineered proteins will be produced in host organisms, such as E. coli or S. cerevisiae, and then purified for materials characterization. As an example, protein net charge and charge distribution will be genetically engineered on fluorescent or enzymatic proteins to promote phase separation with nucleic acids. The phase behavior of the engineered proteins will be evaluated through spectroscopy, fluorescence microscopy, and materials characterization in vitro and/or in living cells. The student will work closely with the PI, including regularly meetings with the PI to discuss research goals and progress.

Faculty Member

Allie Obermeyer

Qualifications

• Biology and Chemistry, and a willingness to learn. Ideally students will be familiar with molecular biology, protein expression and purification, and microbial growth.
• More

Department

Chemical Engineering

Project Title

Nano-carries of insulin for intraocular use (filled)

Description

This research aims to develop an insulin delivery platform with an extended-release function for intraocular use, focusing on mitigating diabetic retinopathy. The work will involve engineering hydrogel nanoparticles based on polymers, enhanced with DNA-aptamers for binding and encapsulating insulin molecules and probing the release stages. The student wll establish methods for producing hydrogel nanoparticles with desired sizes, from tens to hundreds of nanometers in diameter, and with insulin encapsulation and its regulated release.

Faculty Member

Oleg Gang

Qualifications

• polymers and soft materials
• More

Department

Civil Engineering

Project Title

Atmospheric Sensing for Urban Sustainability (filled)

Description

By 2050, a staggering 68% of the global population will reside in cities. To foster cities that will not only accommodate such vast numbers but also ensure a secure, healthful future for their inhabitants, we must focus on constructing urban areas that are sustainable, equitable, and resilient against extreme natural events. To contribute to this vision, the Environmental Flow Physics Laboratory has recently equipped itself with cutting-edge meteorological instrumentation, including meteorological stations, wind lidars, and drone-based sensing devices. This new collection of instruments will allow us to take in-situ atmospheric measurements, capturing quantities such as wind speed, temperature, relative humidity, airborne particulate matter, and incoming solar radiation. The mission behind this initiative, generously sponsored by the US Army, is to enhance our comprehension and ability to model heat, moisture, and pollution transfer between the urban surface and the atmosphere.We plan to deploy a network of weather stations (similar to the ones shown in the figure above) at selected sites in New York State and use measurements to develop new theories and numerical models for the aforementioned processes.To push this project to fruition, we're opening three Summer research positions. These roles will involve supporting the field deployment and/or developing algorithms to interpret the data. Participants in this project will gain invaluable experience in sensing technology, the physics of fluids, field deployments, and computational methods.

Faculty Member

Marco Giometto

Qualifications

• Python or Julia languages
• More

Department

Civil Engineering

Project Title

Develop pedestrian safety warning system on Apple Watch

Description

Ensuring pedestrian safety at intersections remains a persistent challenge. We aim to utilize camera-based object detection combined with user device localization to alert pedestrians to potential dangers. This application demands low-latency communication with user devices such as the Apple Watch or smartphones. Latency can occur at various stages, including from the camera to the server, during calculations on the server, and from the server to the user's device. We aim to minimize latency at each stage to ensure the effectiveness of the warning messages. The project needs students to develop warning generation algorithms and send/receive MQTT messages. And do uncertainty quantification for the algorithms used.

Faculty Member

Sharon Di

Qualifications

• Experience in developing using C++/java is prefered
• Experience in communication method like MQTT and RabbitMQ is preferred
• More

Department

Civil Engineering

Project Title

Understand Travel Mode Changes through LLMs and In-context Learning

Description

In recent years, Large Language Models (LLMs), such as GPT-3, GPT-4, and LLama 2, are algorithms trained on extensive datasets, exhibiting exceptional zero-shot learning capabilities across numerous unlabelled tasks. Building on this notion, in-context learning involves conditioning LLMs on specific linguistic instructions or task demonstrations, subsequently enabling them to tackle analogous tasks through sequence predictions. In the field of Travel Mode Analysis, a significant volume of unlabeled data exists. Of particular interest are the unlabelled tweets generated by commuters, which offer insights into evolving travel patterns, especially in the context of events like a pandemic. By harnessing the strengths of LLMs and in-context learning, there exists potential to extract valuable insights from unlabelled data.We aim to address the following research questions:What constitutes an effective prompt structure that reliably directs LLMs to produce specific outputs? For instance, how can an LLM discern whether an unlabelled tweet is related to a private vehicle or a service like taxi or Uber?
How can LLMs be employed to investigate shifts in travel behavior, encompassing aspects such as travel frequency, travel distance, and interpersonal similarity in travel patterns?
Is it feasible to use LLMs to generate travel mode datasets from recent or context-specific data, facilitating immediate adaptability?

Faculty Member

Sharon Di

Qualifications

• tune LLM models
• More

Department

Civil Engineering and Engineering Mechanics

Project Title

MP³ - Morningside Park Pond Project (filled)

Description

Climate change threatens our urban landscapes, bringing a surge in mean temperatures and catastrophic weather events. In these challenging times, public infrastructure, especially in underprivileged areas, provides vital respite and recreational spaces . The historic Morningside Park, serving the Columbia University community and neighboring residents in Morningside Heights and West Harlem, is at the heart of this mission. The park’s iconic pond and waterfall have deteriorated due to harmful algal bloom (HAB) outbreaks, which pose a threat to both human and environmental health. Additionally, the deterioration of the pond is exacerbated by pump failures due to frequent flooding of the system. The Frederick Law Olmsted-designed park constitutes a natural environment that requires careful stewardship; we propose to form an interdisciplinary team of Columbia faculty, staff, and students to tackle the challenge of restoring the pond and its signature waterfall in concert with local communities. This project will improve the park’s flora and fauna ecosystem (LDEO, EEE), repair the broken pumps (CEEM, MECE), and generate a preliminary upper park redesign to ensure a sustainable source of spring water for the pond. New York City Department of Parks and Recreation (NYC Parks) is excited to join this effort, which also gained support by University President Shafik. Further, using the Morningside Park pond as a testbed, we propose to devise a holistic and sustainable management plan for the control and reduction of HABs that currently seasonally befall over forty water bodies in New York’s greenspaces.

Faculty Member

Adrian Brügger

Qualifications

• CAD/CAM, engineering design (CEEM, MECE, EEE)
• More

Department

Computer Science

Project Title

Deep learning for Alzheimer's disease prediction (filled)

Description

Alzheimer's disease (AD) has a substantial heritable component, including substantial variation beyond the well-characterized APOE alleles. AD associated common variants are strongly enriched in microglia regulatory elements but it remains unclear how these variants disrupt microglial function to impact disease risk. We train deep learning models of pre and post-transcriptional gene regulation using multi-omics from AD-relevant cell-types. We use the resulting variant effect predictions (VEPs) in functionally informed fine-mapping and polygenic risk calculation. This project will explore using the VEPs in rare variant association testing on whole genome sequencing of 21k individuals from the AD sequencing project.

Faculty Member

David Knowles

Qualifications

N/A

Department

Computer Science

Project Title

Unraveling the genetic basis of neurological disorders with machine learning (filled)

Description

RNA splicing, the cellular process by which "junk" intronic regions are removed from initially transcribed RNA, is tightly regulated in healthy human development but frequently dysregulated in disease. This complex process involves hundreds of proteins and non-coding RNAs, so that predicting the effects of mutations on outcome is beyond current biophysical models. We instead take a data-driven approach: leveraging large-scale RNA-seq and massively parallel reporter assay data to train deep neural networks to predict splicing directly from gene sequence. This project will extend and apply such models to better understand the genetic basis of ALS, Alzheimer’s and/or autism using large-scale whole-genome and RNA sequencing data we have access to through collaborators at the New York Genome Center.

Faculty Member

David Knowles

Qualifications

• Machine learning, computational biology
• More

Department

Computer Science

Project Title

Spatiotemporal dynamics of aging

Description

Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues impacting such complex alterations remain unclear. We built a cellular and spatial atlas of the colon, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the colonic tract and across the main crypt axis, and the temporal dynamics impacting  multicellular programs in the aging large intestine. Our work presents a multi-modal framework for the investigation of cell and tissue organization that can aid in the understanding of cellular roles in tissue-level pathology. During the project, we will further develop statistical and computational methods for integration tissue level histology into spatial transcriptomics and multi-omics data.

Faculty Member

Sanja Vickovic

Qualifications

• Computational biology, statistics, python
• More

Department

Computer Science

Project Title

LLMs Meet Cybersecurity

Description

In the rapidly evolving landscape of cybersecurity, LLMs have emerged as transformative tools. These advanced AI systems, capable of understanding and generating human-like text, are revolutionizing how we approach complex problems, including those in cybersecurity. We are excited to offer two groundbreaking projects on the intersection of LLMs and cybersecurity.Project 1: Securing the Future of LLMs
This project delves into the security of LLMs themselves. As these models become more integral to various applications, ensuring their robustness and resistance to adversarial attacks is paramount. Students will investigate potential vulnerabilities in LLMs and develop strategies to fortify them against emerging threats.Project 2: Harnessing LLMs for Cybersecurity Solutions
The second project explores the application of LLMs in solving complex cybersecurity challenges. Students will leverage the advanced capabilities of LLMs to identify, analyze, and mitigate cybersecurity threats, contributing to the development of smarter, more efficient security systems.Students will work under the guidance of Professor Yang and his PhD students and Postdocs. Past summer projects have led to e.g. publications in top-tier conferences and the discovery of over 100 zero-day vulnerabilities in deployed smart contracts.Join us in shaping the future of cybersecurity with the power of LLMs.

Faculty Member

Junfeng Yang

Qualifications

• Candidates should have outstanding technical skills in running and finetuning LLMs, cybersecurity experience, strong motivation, and the capacity and creativity for independent research. This is not just a summer project but an opportunity to contribute significantly to AI and cybersecurity.
• More

Department

Computer Science

Project Title

Explainable Computer Vision

Description

We are looking for a highly motivated student to work on explainable computer vision and natural language processing methods. While AI methods achieve excellent performance today on many tasks, it is an open question to understand what they learn and develop methods that are explainable by construction. The student will work on new methods towards this goal, developing research that may be eventually published. Minimum qualifications are experience in PyTorch and one course in computer vision.

Faculty Member

Carl Vondrick

Qualifications

• PyTorch
• More

Department

Computer Science

Project Title

Robot Interaction with Diffusion Models

Description

The robots of today struggle to perform a wide variety of tasks, often limited to just automating the tasks they have already seen during their training. In this project, we will explore generative models, in particular diffusion models, in order to learn robust policies that are able to solve a wide variety of tasks. Minimum qualifications are experience in PyTorch and willingness to integrate hardware and machine learning together.

Faculty Member

Carl Vondrick

Qualifications

• PyTorch
• More

Department

Computer Science

Project Title

Uncertainty Quantification in Frontier AI Models (filled)

Description

Multimodal AI has the potential to revolutionize healthcare. Combining vision and language capabilities, these systems can analyze visual data (e.g., medical imaging) and linguistic information (e.g., patient records), to offer a comprehensive and nuanced assessment of patient health. However, while modern large vision and language models (LVLMs) like GPT-4 and LlaVa have impressive accuracy on a wide range of benchmarks, they are prone to errors that can be difficult to predict and interpret. To enable reliable LVLM deployment, we must produce robust techniques for identifying when a model is uncertain and enable a practitioner to calibrate their confidence to the model's prediction when making critical decisions. The aim of the summer project is to develop an algorithm to quantify the uncertainty of large multimodal medical AI systems. Nikita will work with instruction-tuned VLMs, including LLaVa-Med, which are trained to give their answers in a conversational fashion. She will draw on existing literature on uncertainty quantification for LLMs and vision models to develop a method for scoring the uncertainty of individual predictions made by these models. Although we focus on the safe usage of multimodal models in healthcare, such a method can also be applied to other risk-sensitive domains, for example autonomous vehicles.

Faculty Member

Richard Zemel

Qualifications

• Machine learning, deep learning, current methods of uncertainty quantification in AI
• More

Department

Computer Science

Project Title

Community History through AR

Description

This project, in partnership with Columbia’s Center for Smart Streetscapes, is to imagine how augmented reality (including phone-based AR) can help neighborhoods and communities record and relive historical moments to feel connected and proud of a shared community identity. As buildings get demolished and redeveloped, newer residents crave more information about what used to be around them (e.g., “I had no idea that here at this gas station used to be Sunnyside Gardens, the second-largest boxing arena behind Madison Square Garden, with decades of boxing history!“). The history could be curated, like a walking tour (Dublin, Ireland has a great one, and so does Boston’s Freedom Trail). It could also be user-generated, similar to Snap Map on Snapchat, where people record moments that persist at that location for others to discover and experience later.There is a large opportunity for innovation here: What counts as a moment? How should creating a moment or a Story (similar to Instagram and Snapchat stories, but in situated AR) work? How can people become aware of these important stories as they walk around neighborhoods? Does it have to be via smartphones or is there a way of experiencing them using public display kiosks?

Faculty Member

Brian A. Smith

Qualifications

• User interface design
• More

Department

Computer Science

Project Title

Virtual and Augmented Reality for High-Level Control of Remote Robots (filled)

Description

This project addresses the design, implementation, and evaluation of virtual reality (VR) and augmented reality (AR) user interfaces for high-level control of a suite of robots at a remote factory assembly line. These user interfaces are intended to allow future workers to control from home industrial robots on an assembly line without having to understand the low-level details of how the robots operate. To accomplish this, the user will wear a VR or AR headset and interact with a virtual representation of the factory environment, specifying the key steps in the assembly process by directly editing object positions and orientations to describe the desired goal states. This project will explore a range of different user interface designs, comparing combinations of different interaction and visualization techniques for performing tasks.

Faculty Member

Steven Feiner

Qualifications

• Unity, 3D UI design, VR
• More

Department

Computer Science

Project Title

Research in AR and VR (filled)

Description

We are exploring the development of augmented reality (AR) and virtual reality (VR) user interfaces that enable users to perform skilled tasks effectively, working alone or together, indoors or outdoors, in 3D spaces that are purely virtual or integrated with the user’s physical environment. This project will involve designing and implementing prototypes to determine the best ways to accomplish tasks in one of a range of different domains. Much of our research is joint with faculty in other schools and departments.

Faculty Member

Steven Feiner

Qualifications

• Unity, 3D UIs, VR and AR
• More

Department

Computer Science

Project Title

Light for Communication, Sensing, and Security

Description

We are looking for highly motivated students who are interested in developing light-based systems for high-speed communication, fine-grained object tracking, and AI security. Specifically on communication and sensing, we are developing a communication technology that uses a steerable laser beam to track highly-mobile targets while sending data at Gbps levels. The student will work on the integration of the system with an existing virtual reality headset to realize wireless VR. On AI security, we are investigating the protection of live speech video integrity by modulating ambient light to encode verification information. Students will work with PhD students to explore light communication design, feature extraction, as well as experimentation and video collection.

Faculty Member

Xia Zhou

Qualifications

• computer networks, wireless communication and system design, machine learning
• More

Department

Computer Science

Project Title

Weather-Wireless Data Analyst and Integration Coordinator

Description

This project aims to manage and analyze datasets from both communication and weather databases to understand the impact of weather phenomena on new generation wireless networks. Students will be involved in integrating code for communication link analysis across a range of frequencies and applying basic anomaly detection and pattern recognition techniques to identify how various weather conditions affect network performance. The project will provide hands-on experience with real-world datasets and offer insights into the challenges and solutions for weather-resilient communication systems.

Faculty Member

Gil Zussman

Qualifications

• Proficiency in database management and data analysis tools.
• Experience in coding for data analysis in Python, API requests, Github.
• Basic knowledge of applying machine learning algorithms.
• Interest in researching weather phenomena and their impact on communication networks.
• More

Department

Computer Science

Project Title

Developing a personalized GenAI Tutor for classes (filled)

Description

A student will develop a personalized Tutor interface for courses. The idea is for instructors to upload course material (lecture notes, slides, PDFs of textbooks etc.) and the tutor automatically develops a chatbot tailored towards that class.

Faculty Member

Vishal Misra

Qualifications

• Python, LLM APIs
• More

Department

Computer Science

Project Title

Physiological Sensing with Fabric Sensors

Description

We are looking for a highly motivated student interested in developing textile/fabric sensors for collecting user's physiological signals such as electromyography and electrocardiogram signals. Fabrics as sensors are lightweight, flexible, and easy to wear. It can be seamlessly integrated into various wearable systems such as virtual reality headsets or rehabilitation devices. The student intern will work with PhD student and be involved in (1) sensor fabrication and design, and (2) analysis of collected neurosignals using signal processing techniques and/or machine learning models.

Faculty Member

Xia Zhou

Qualifications

• embedded systems, neuroscience, machine learning, signal processing
• More

Department

Computer Science

Project Title

Differentiable Fluid Dynamics for Inverse Design (filled)

Description

This project aims to develop a new numerical method for simulating fluid dynamics based on our lab's previous codebase. The research problem we will investigate is to make the simulation differentiable with respect to the structure (i.e., solid objects) in the fluid. Our algorithm will not only predict fluid dynamics but also compute how the fluid dynamics change as the structure's geometry changes. This algorithm, if succeeded, can be directly integrated into the current machine-learning framework to optimize the design for fluid-structure interaction automatically. For example, it can be used to find better car designs to reduce air drag or to search for a more efficient control policy for a drone. The summer research assistant will work with the PI and be responsible for exploring various algorithmic implementations and testing them under different setups. The research assistant is expected to have background knowledge of numerical computing, read relevant academic literature, and be fluent in C++ programming language and CUDA-based GPU programming framework. The research assistant will work with other graduate students in the lab and meet with the PI on a weekly basis.

Faculty Member

Changxi Zheng

Qualifications

• numerical computing
• fluid dynamics
• large scale GPU programming
• C++ programming language
• More

Department

Computer Science

Project Title

Developing a foundational genome-scale language model (filled)

Description

The project focuses on developing a DNA language model, leveraging innovations in deep learning and architecture design that have revolutionized natural language processing. This approach has already seen success in biological applications, notably with AlphaFold's protein structure prediction. Genomes, encoded in DNA, represent the most information-rich biological data, holding key insights into the relationship between code and characterization and offering avenues for the manipulation and engineering of biological machines, with broad applications in health and sustainability. The project aims to advance this field by developing and testing novel model architectures, benchmarks, and datasets. Numerous projects are available, depending on preference. Students will either delve into transformer models and alternative state space models for model training, create unique and informative benchmarks tailored to genomic data, or devise sampling strategies to efficiently learn from the vast amount of existing DNA sequences, which collectively span approximately 100 exabytes. Through these efforts, the project seeks to deepen our understanding of DNA and unlock its potential for solving complex biological challenges. Stard and end dates flexible.

Faculty Member

Mohammed AlQuraishi

Qualifications

• Python programming, data science, machine learning, basic biology.
• Experience with PyTorch, large language model, biological data analysis a plus.
• More

Department

Computer Science

Project Title

Predicting domain–peptide interactions using machine learning (filled)

Description

Numerous essential biological functions depend on protein–protein interactions mediated by interactions between modular peptide-binding domains (PBDs) and peptidic sites within partner proteins. Due to the immense diversity of PBDs in the human proteome the characterization of PBD–peptide interactions remains incomplete. The project aims to develop a machine learning model for predicting PBD–peptide interaction affinities at proteome-scale. The student will be involved in multiple aspects of model development, from benchmarking existing models and testing novel architectures to curating novel datasets and applying models at scale.

Faculty Member

Mohammed AlQuraishi

Qualifications

• Machine learning and molecular biology
• More

Department

Earth and Environmental Engineering

Project Title

Carbon dioxide and conversion to useful products (filled)

Description

We have developed a variety of CO2 sorbent/catalyst combinations called dual function materials (DFM) materials that are capable of capturing CO2 from the air and converting it to useful products (PTC patent application). We have filed a provisional patent on a novel method for desorbing captured CO2 at low temperatures thereby conserving energy.  My current MS student will advance this technology towards commercialization as he works closely with two one of my PhD students.

Faculty Member

Robert J. Farrauto

Qualifications

• Heterogeneous catalysis and CO2 capture
• More

Department

Earth and Environmental Engineering

Project Title

Social-economic analysis of electrification in New York City

Description

This project focuses on the electrification of energy sectors, crucial for achieving net-zero emissions by 2050, and its challenges, particularly for marginalized communities. These communities face increased risks due to outdated grid infrastructure and volatile electricity prices, exacerbated by the integration of renewables and extreme weather events.Centered on New York City (NYC), the project aims to combine grid capacity data with demographic and building/land-use data to tackle electrification challenges. Utilizing data from Consolidated Edison, NYC's electricity provider, and extensive demographic and building datasets, the project will deliver: 1) Forecasts of electric demand increases across NYC, integrating demographic data, building characteristics, electricity consumption patterns, and heating demand models; 2) Analysis of potential network upgrade costs, identifying areas likely to be underserved by the current grid capacity; 3) Recommendations for alternative strategies like demand response or energy storage to reduce costs related to increased electrification demands.

Faculty Member

Bolun Xu

Qualifications

• Basic data processing, visualization, and analysis; linear regression, LASSO, PCA, clustering
• Experience with Python or R is a must.
• More

Department

Earth and Environmental Engineering

Project Title

Towards sustainable in-situ critical mineral recovery in deep hard rocks

Description

Conventional mining technologies face increasing technical challenges and expenses in processing deposits of declining grade, challenging mineralogy, or accessing ores that occur at increasingly greater depths. Economic quantities of high-value metals/critical minerals (Cu, Ni, PGM, REE, Li, Co, Au, Ag, Te, Mo, Re, W) are found within hard rock primary ores, which are igneous and metamorphic hydrothermal deposits generally found 500 to 5000 meters below surface (Haschke et al., 2016). Step changes in in situ recovery (ISR) technologies are needed to access these critical minerals deposits. The first-order major challenge facing hard rock ISR is the low porosity and low permeability of most igneous and metamorphic rocks. The proposed program seeks to test physical and chemical methods to increase in situ accessible reactive surface area and leaching of critical minerals within hard rock ores.We are seeking undergraduate student researcher(s) to assist with the following aspects of this project:Creation of rock core aggregates
Testing of rock core fragmentation methods
Design of proof of concept column leaching experiments
Reactive transport modeling

Faculty Member

Shaina Kelly

Qualifications

  

• Surface chemistry, fluid-mineral interactions, fluid mechanics
• Lab and/or modeling experience preferred
• More

Department

Earth and Environmental Engineering

Project Title

Data analysis in electricity markets

Description

The student will conduct data analysis in electricity markets related to price analysis and prediction, and energy storage adoption. The student will get familiar with our in-house machine learning models and optimization models used to simulate electricity market clearing and storage participation. The student will also collect, clean, and analyze real market participation data from California and Texas. The student must be familiar with Python or Julia, with prior experiences with machine learning and optimization, and background knowledge about electricity markets.

Faculty Member

Bolun Xu

Qualifications

• Python/Julia, Machine learning, Optimization, Electricity markets
• More

Department

Earth and Environmental Engineering

Project Title

Development of Li selective membrane technologies (filled)

Description

The development of separation techniques with enhanced selectivity can unlock opportunities for new applications in energy, water, and the environment, such as producing lithium from geothermal brines, purifying rare earth elements for permanent magnets in wind turbines, harvesting uranyl ions from seawater, and removing/recovering nitrogen and phosphorus from wastewaters. This research seeks to demonstrate dual-driving force operation in charge-selective polymeric membranes (termed ion-selective membranes, ISMs) to achieve radically improved and tunable ion-differentiating capabilities. Conventional separation techniques ubiquitously use a single driving force, e.g., temperature-swing adsorption and vacuum filtration (i.e., negative pressure). We hypothesize that the simultaneous utilization of two different driving forces and the thoughtful control of the magnitude and direction of the driving forces can affect the underlying mechanisms governing the separation selectivity. Specifically, we propose to apply a coordinated second driving force of hydraulic pressurization in ISM processes, in addition to the primary driving force of electrostatic potential, to attain unprecedented ion selectivities.

Faculty Member

Ngai Yin Yip

Qualifications

• Membrane processes, water chemistry, transport theory, thermodynamics
• More

Department

Earth and Environmental Engineering

Project Title

Investigation of Point-of-Use Systems from a Major Online Retail Platform (filled)

Description

Point-of-use (POU) drinking water (DW) treatment systems are rapidly gaining popularity and supplanting centralized treatment facilities as the final safety barrier before consumption. Because POU DW systems are currently not regulated, retailers often make unsubstantiated claims about the enhancements to water quality. There are presently no rigorous scientific studies on the effectiveness of POU DW systems sold through Amazon.com and other online retailers. As such, false advertising and other socially irresponsible marketing practices are left unchecked, potentially compromising public health and safety. This proposed project aims to evaluate the water purification performance of four popular POU systems retailed on Amazon.com using technically robust standard methods and compare the results with claims made by the manufacturers.

Faculty Member

Ngai Yin Yip

Qualifications

• water chemistry, water treatment processes
• More

Department

Earth and Environmental Engineering

Project Title

Targeted Selective Separation of Lithium Ions from Alkali Metal Cations in Brine Mixtures Using Switchable Solvents (filled)

Description

Lithium is a critical mineral for the clean energy transition. To meet the impending surge in demand, direct lithium extraction (DLE) technologies that are economical and sustainable are urgently needed to enable increased production from conventional sources, access “technologically-locked” lithium assets in geothermal brines and produced water, and realize recycling and reuse of existing lithium stock. We propose a novel DLE technology based on switchable solvents. The innovation, termed switchable solvent selective extraction (S3E), utilizes temperature-responsive switchable hydrophilicity solvents to target the mining of Li+ from mixed-electrolyte brines. The overarching aim of this project is to advance fundamental understanding of the principal phenomena governing competitive ion partitioning in biphasic systems of switchable hydrophilicity solvents and lithium brines.

Faculty Member

Ngai Yin Yip

Qualifications

• water chemistry, transport theory, thermodynamics
• More

Department

Earth and Environmental Engineering

Project Title

Battery Research

Description

Learn to build, test, and break batteries. Help graduate students execute their projects.

Faculty Member

Dan Steingart

Qualifications

• Chemistry
• More

Department

Electrical Engineering

Project Title

Automated Classification of Optical Images of Uterine Cancer (filled)

Description

Tissue optical properties provide a fast readout of tissue pathology and measurements of tissue fiber structure, morphology, and directionality. Summer researchers will aid in  developing a database of optical coherence tomography and near infrared spectroscopy characterization of uterine fibroids with registered histology. Image and signal processing algorithms will be used to measure tissue architecture and optical properties. Students will also aid in developing custom automated  algorithms to classify areas within optical images as cancer or normal.

Faculty Member

Christine Hendon

Qualifications

• MATLAB
• More

Department

Electrical Engineering

Project Title

Projects in the Bioelectronic Systems Lab

Description

Our group engineers electronic systems to create new tools for biology and biomedicine. In nearly all cases, this includes the design of CMOS integrated circuits. Our main focus is on brain-computer interfaces, wearable imaging devices, and biosensing. Undergraduate students can become involved with all aspects of the design of the hardware and software for these systems.  

Faculty Member

Kenneth Shepard

Qualifications

• Electrical engineering or computer engineering students
• More

Department

Electrical Engineering

Project Title

Advanced Microscopy Techniques to Analyze Device Degradation in Gallium Nitride Transistors

Description

Wide-bandgap gallium nitride (GaN) heterostructure devices that leverage the two-dimensional electron gas (2DEG) channel support the development of uncooled/unshielded microelectronic components and systems for robust operation in corrosive, radiation-rich, and ultra-hot/cold environments. The reliability of GaN 2DEG devices and circuits under hostile environmental conditions is rarely limited by the semiconductor material itself, and instead limited by the lack of suitable contact metallization and passivation schemes. Failure mechanisms associated with the contacts and passivation layers that cause premature device failure include gate sinking, voiding, electromigration, and passivation cracking. This project will involve the preparation and imaging of GaN high electron mobility transistor (HEMT) samples with novel metallization and passivation schemes that have undergone accelerated aging. Advanced microscopy techniques including high-angle annular darkfield scanning TEM (HAADF-STEM) and fast Fourier transform (FFT) TEM will be utilized to pinpoint device damage and investigate aging effects on the morphology of the alloyed contact stack, crystallinity of the heterostructure, and cracking in the passivation layers. The results will be correlated with findings from electrical measurements to determine the suitable of the device layers for extreme environment applications. This project will require working closely with other group members to perform experiments in the lab.

Faculty Member

Savannah Eisner

Qualifications

• Semiconductor Devices and material science
• More

Department

Electrical Engineering

Project Title

Decoding auditory attention for real-time BCI control (filled)

Description

This project seeks to advance Brain-Computer Interface (BCI) technology by focusing on the development of a highly efficient auditory-based BCI system. This project aims to address and overcome the limitations of current auditory BCI systems, primarily their slow stimulus presentation rates and modest information transfer capabilities. By integrating auditory attention decoding techniques, we aim to significantly enhance the system's ability to identify target sounds amidst background noise accurately. This approach not only improves the practicality and accessibility of BCIs for individuals with disabilities but also paves the way for advanced communication interfaces between humans and machines. Our goal is to create a more natural and efficient method of interaction that leverages the brain's innate capabilities, thereby opening new avenues for research and application in neuroscience, engineering, and beyond.

Faculty Member

Nima Mesgarani

Qualifications

• Signal Processing, EEG
• More

Department

Electrical Engineering

Project Title

Control of microLED array devices

Description

Our group has been developing microLED arrays for use in superresolution microscopy applications.  One of the challenges, however, has been the control of a large number of access lines for the devices. In this project the student will develop a controller using a high pin count FPGA and synchronize the illumination with the camera capture of multiple images.  These images will then be reconstructed into computed images with a higher resolution than possible using a widefield image.  Following validation the system will be used by our collaborators to map the brain activity in freely moving mice as part of a larger program centered at Colorado State University examining the learning associated with the olfactory response of animals.

Faculty Member

Ioannis Kymissis

Qualifications

• Optics, electronics, a little programming
• More

Department

Electrical Engineering

Project Title

Photonics for Accelerated AI/ML Communication Collectives

Description

This position focuses on optimizing communication-intensive operations in AI/ML workloads. Employing novel optical comb sources, we aim to enhance connectivity through multi-wavelength selective switching and multicasting. The goal is to accelerate data exchange, contributing to the efficiency and scalability of AI/ML systems.

Faculty Member

Alex Meng

Qualifications

• Optics, Photonics, and Electrical Engineering
• More

Department

Electrical Engineering

Project Title

Photonic Computing Systems

Description

Research opportunity to perform experimental work on integrated photonic circuits for applications in computing systems.

Faculty Member

Keren Bergman

Qualifications

• computing systems, software, photonics
• More

Department

Electrical Engineering

Project Title

A Data Center Simulator Enabling Large Design Space Exploration of Distributed Accelerators

Description

The end-to-end performance acceleration of data center applications requires a large number of distributed accelerators as these applications exhibit wide diversity and operational complexity. Unfortunately, today’s systems community lacks the infrastructure needed to explore the large design space of distributed accelerators. To close this gap, in this project, we aim to build an accelerator-centric data center simulator. Our simulator will take dependency graphs of distributed applications and high-level descriptions of accelerator algorithms as inputs and investigate the design trade-offs of distributed accelerators. We will validate the effectiveness of our simulator with a key case study of where to place any given accelerator for data center applications, on a die, on a chiplet, on a CXL-attached device, or on a network interface card. We will release our research artifacts with flexible open-source licenses to better facilitate academic and industry research on accelerators for data center applications.

Faculty Member

Tanvir Ahmed Khan

Qualifications

• C/C++, Linux
• More

Department

Electrical Engineering

Project Title

Advancing Photonic Chip Technology: Design, Testing and Optimization

Description

This project centers on the testing and optimization of photonic chips within the university lab setting. Leveraging state-of-the-art equipment and methodologies, our research aims to evaluate the performance, reliability, and efficiency of photonic chips for various applications. Through rigorous testing protocols, we seek to identify potential improvements and innovative solutions that can enhance the capabilities of these photonic devices. The project not only contributes to the advancement of photonic chip technology but also provides valuable insights for future applications in communication, sensing, and computation.

Faculty Member

Alex Meng

Qualifications

• Optics, Photonics, and Electrical Engineering
• More

Department

Electrical Engineering

Project Title

Manifold Denoising with Learned Optimization (filled)

Description

Students will implement models for fast denoising of high-dimensional data using learned Riemannian optimization, and apply these models to signal reconstruction (compressed sensing) and signal generation (diffusion models). Project will be a mix of mathematical modeling, implementation (python), and experimentation with data from imaging, computer vision and materials. This project builds on recent results on fast signal detection using unrolled optimization, which yields neural networks with significantly improved performance-accuracy tradeoffs compared to both classical approaches and generic machine learning architectures.

Faculty Member

John Wright

Qualifications

• Coding (python/pytorch), optimization
• More

Department

Electrical Engineering

Project Title

Joint Sensing and Communication with mmWave and Terahertz Wireless for 6G Networks

Description

6G cellular networks will utilize high-frequency (mmWave and/or terahertz) data transmissions to jointly sense the environment and communicate with users. This project is a collaborative effort with Nokia Bell Labs to evaluate the feasibility of 6G wireless sensing. We will be using custom wireless measurement equipment including 28 GHz and 0.14 THz channel sounders to examine responses at a variety of locations within the Columbia campus and wider NYC metro area. Student responsibilities will include developing experiments, collecting measurements using this equipment, and analyzing collected datasets to better understand the sensing capabilities at these frequencies.

Faculty Member

Gil Zussman

Qualifications

• Interest in emerging technologies for next-generation wireless systems.
• Experience with simple hardware systems (Raspberry Pi, Arduino and similar).
• Basic experience with mathematical data analysis software (MATLAB, Numpy/Julia).
• Must be able to access the Columbia Morningside Heights campus.
• Willing to work outdoors in the summer.
• More

Department

Electrical Engineering

Project Title

Experimentation with Adaptive Full-Duplex (FD) Wireless Communications

Description

Full-duplex (FD) wireless technology allows for simultaneous transmission and reception on the same frequency channel, a more spectrum-efficient communication paradigm than the current half-duplex architecture used in all modern wireless systems. The FlexICoN interdisciplinary project directly addresses important cross-layer challenges stemming from novel small-form-factor FD transceiver implementations. In this project, students will use existing software-defined FD radio nodes to build link- and network-level infrastructure and explore the effects of FD integration on communication systems, including both traditional and novel use cases. This project will provide hands-on experience with both hardware and software challenges and offer the opportunity to explore next-generation communications paradigms.

Faculty Member

Gil Zussman

Qualifications

• Previous programming experience (C++, Python) is required.
• Preliminary background in Digital Signal Processing is required.
• Experience with hardware systems (SDR, FPGA, Arduino) is preferred.
• Background or interest in computer communications and wireless networks is preferred.
• Must be able to access the Columbia Morningside Heights campus.
• More

Department

Electrical Engineering

Project Title

Weather Effects on High Frequency Communication Links

Description

Future wireless networks will use high-frequency mmWave links for transmitting and receiving information with high throughput. A key difference between mmWave links and conventional sub-6GHz links is that mmWave links are severely affected by weather conditions. Students working on this project will use a state-of-the-art mmWave radar to assess the impact of wind speed, temperature, humidity, and other factors on the high-frequency link. The end goal of the project is to develop a classifier that can infer weather conditions based on the signal received from the mmWave radar. Students are expected to learn how the mmWave radar works, design experiments to obtain labeled data, perform measurements, and develop the classifier.

Faculty Member

Gil Zussman

Qualifications

• Some background in Digital Signal Processing such as FFT is required.
• Some experience with Python is required.
• Some experience with classification predictive modeling is preferred.
• Some understanding of wireless networks, or interest to learn is preferred.
• Must be able to access the Columbia Morningside Heights campus
• More

Department

Electrical Engineering

Project Title

Development of photonic structures (filled)

Description

Develop novel photolithography techniques for achieving ultra  high resolution for photonic applications. The researcher will be trained in clean room equipment of CNI. No previous experience is needed.

Faculty Member

Michal Lipson

Qualifications

• photonics
• More

Department

Industrial Engineering and Operations Research

Project Title

Equivariant neural networks for material design (filled)

Description

Feedforward neural networks are able to approximate very complex functions of the input variables. However, the amount of data required to train these networks, in particular, deep neural networks effectively grows rapidly with the size of the network. One way to reduce this data requirement is to encode the symmetries of the function directly into the network structure. In this project, we will explore how to effectively encode symmetries of various kinds.

Faculty Member

Garud Iyengar

Qualifications

• Calculus
• Python
• More

Department

Industrial Engineering and Operations Research

Project Title

Statistical learning in a changing environment: adaptation to non-stationarity (filled)

Description

This project aims to develop methods for statistical learning in an evolving environment with unknown dynamics. The student and the mentor will (1) develop flexible inferential tools for adapting to temporal distribution shifts; (2) collect and clean real-world data to test the proposed methods; (3) conduct a mathematical analysis to establish theoretical guarantees; (4) write a paper to summarize the results. The student needs to (1) have past experience demonstrating independent research capabilities; (2) have taken advanced courses and conducted research on statistics, machine learning, optimization and real analysis; (3) be proficient in Python and LaTeX.

Faculty Member

Kaizheng Wang

Qualifications

• Statistics, machine learning, optimization, real analysis, Python, LaTeX
• More

Department

Industrial Engineering and Operations Research

Project Title

Two-Sided Fairness in Heterogeneous Online Matchings (filled)

Description

Traditionally, online two-sided matching markets have been employed to find profitable ways of allocating resources. Consider, for instance, online advertising problems, whose goal is to match a sequence of customers arriving in an online fashion to advertisers from a given ground set. In these models, preferences of customers do not matter – actually, they are not even really part of the input. It is then not surprising that recent years have seen the surge of studies on one-sided objective functions, such as the Nash Social Welfare, or Submodular Welfare.In this project, the student will first get acquainted with the literature on the area, and then develop fairness concepts and algorithms for online two-sided matching markets that take into account the utility of both sides of the market, as well as their differences in goals. We will in particular focus on markets arising in social media. In such markets, the heterogeneity of the two sides requires that their utilities are defined and handled differently, thus creating a challenge and an opportunity for research.

Faculty Member

Yuri Faenza

Qualifications

• algorithms; basic probability; coding in python
• More

Department

Industrial Engineering and Operations Research

Project Title

Sharpness aware minimization (filled)

Description

This project concerns a variant of the gradient method called sharpness aware minimization proposed proposed in 2021. Its purpose is to achieve better generalization when training neural networks. The purpose of this internship is two fold. First, it is to implement the method and apply it to new examples not yet considered in the literature. The efficiency will be compared with the gradient method. Second, we seek to understand in the deterministic setting why it should reduce sharpness, and potentially at what rate.

Faculty Member

Cédric Josz

Qualifications

• Real analysis
• Calculus
• More

Department

Mechanical Engineering

Project Title

A Teleoperation Framework for Dexterous Robot Hands (filled)

Description

Develop a framework for teleoperating novel dexterous robotic hands in a simulated environment, with the goal of performing design optimization and Behavioral Cloning on the resulting models.

Faculty Member

Matei Ciocarlie

Qualifications

• Robotics, Transforms and spatial reasoning, Ubuntu Linux, Python
• More

Department

Mechanical Engineering

Project Title

Understanding and Controlling Tissue Mechanics During Embryo Morphogenesis

Description

Working with the PI and a graduate student in the lab, the student will work to understand and control how tissue mechanical properties and cell-generated mechanical forces shape tissues in the developing fruit fly embryo. The student will learn quantitative image analysis, confocal microscopy, and/or Drosophila genetics.

Faculty Member

Karen Kasza

Qualifications

• Fundamentals of mechanical engineering and lab safety
• Experience with coding in Python or MATLAB
• More

Department

Mechanical Engineering

Project Title

Development and validation of 6-DoF Cable-Driven Neck Brace

Description

The researcher will design and test new neck braces for patients with neck impairments. The robotic neck brace will apply forces/moments to the head-neck to assist and stabilize the head-neck motion.

Faculty Member

Sunil Agrawal

Qualifications

• robotics, dynamic, control, electronics, programing
• More

Department

Mechanical Engineering

Project Title

Coordinated Pelvis-Chest Positioning With a Robotic Stand Trainer Robot

Description

The goal is to characterize how humans coordinate their pelvis and chest when making everyday reaching movements. This research will provide information how a robotic stand trainer can be programed and assist individuals with spinal cord injury who have impaired upper body movements,

Faculty Member

Sunil Agrawal

Qualifications

• robotics, dynamics, control, electronics, programing
• More

Department

Mechanical Engineering

Project Title

Training Human Gait using mobile TPAD (mTPAD)

Description

Children with cerebral palsy have impaired walking. We are studying the use of robotic device to assist and improve walking in children.

Faculty Member

Sunil Agrawal

Qualifications

• robotics, dynamics, control, electronics, programing
• More

Department

Mechanical Engineering

Project Title

The Human Wear Project and the Nitro Project (filled)

Description

Primary responsibilities will revolve around mechanics and design, with a focus on utilizing SolidWorks for design, 3D printing, and machining tasks such as milling and lathe work. May also be involved in data collection for various projects within the lab. The role will include ensuring that the components and prototypes you create are rigid and meet the required specifications for the research projects. This may involve collaborating with other team members, conducting experiments, and troubleshooting mechanical issues as they arise.

Faculty Member

Gerard Ateshian

Qualifications

• 3D printing, machining (milling and lathering), solidworks modeling, laser scanning, laser cutting
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