Summer Undergraduate Research Program

Mentor: Petko Bogdanov

PFAS molecules have been valuable in many industrial and consumer products and processes from firefighting foams to non-stick coatings and chip fabrication. Their toxicity and resistance to natural degradation have posed environmental and health challenges as they have been found to be ubiquitous in soil, water, air and organisms (including humans). 

The goal of this project is to design and train novel machine learning models for molecule representations and employ them to:

1. Design effective materials for remediation (sorption and filtration)
2. Search/design alternatives to PFAS molecules

The project will enable a dual-pronged strategy powered by machine learning (ML). First, the development of predictive models to accelerate the remediation of existing contamination, and second, the application of generative AI to design novel, safe and functional materials to replace PFAS, preventing future environmental burdens. 

This computational direction offers the potential to dramatically reduce the time and cost associated with experimental research, enabling the rapid evaluation of remediation strategies and the exploration of a vast chemical space for sustainable alternatives.

Student Skills/Requirements: Programming and math background, experience with AI/ML, python, pytorch

Location: UAB 416

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Petko Bogdanov

Self-supervised machine learning methods are often used to pretrain neural networks by reconstructing their inputs, such as individual pixels in images or tokens in text. However, recent work on Joint Embedding Predictive Architectures (JEPAs) suggests that predicting meaningful latent representations, rather than reconstructing raw inputs, avoids modeling irrelevant detail and can lead to more semantically useful representations. 

While JEPA-style approaches have been successfully applied to natural images and video, their adaptation to graph-structured data instrumental to modeling molecules has not been sufficiently investigated. As part of this project, students will work on building and evaluating novel JEPA graph neural networks in the context of chemical and biological datasets.

Student Skills/Requirements: 

• Programming and math background
• Experience with AI/ML
• Python
• Pytorch

Location: UAB 416

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Amreeta Chatterjee

As Computer Science (CS) departments develop artificial intelligence (AI) usage guidelines, faculty reasonably make different decisions based on their course goals but lack data on how students experience navigating these varied expectations across a semester. 

This project will study the student side of that experience by collecting anonymized course policy categories and deploying a survey that measures perceived clarity, cognitive load, confidence and sense of belonging across courses. We will also investigate whether students with different cognitive problem-solving styles experience this navigation differently. 

Findings will also produce recommendations that help the department develop coherent AI guidance benefiting both students and faculty. Students working on this project will gain hands-on experience in IRB preparation, survey design and statistical analysis and submit work to ACM conferences.

Student Skills/Requirements: 

• Completed ICSI 201
• Experience navigating AI policies as a student (firsthand perspective valued)
• Strong written communication
• Willingness to learn statistical analysis and research methods

Location: UAB 402

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Michael Phipps

Work on one of three projects: 

• Extend a compiled object-oriented programming language (Tran)
• Develop the Tran IDE/debugger
• Extend a micro-kernel operating system

You need not be an expert on these topics, but you must be familiar with at least some of them and willing to learn. Teamwork, working with a large code base, documentation and source control are skills that you’ll practice over the course of the summer.

Student Skills/Requirements: 

• Excellent programming skills (Java/C#/C)
• Either or both: Operating Systems, Programming Language Implementation

Location: UAB 401

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 4: Research for credit

Mentor: Mariya Zheleva

This project, leveraging my lab’s previous work, will focus on the implementation and release of a spectrum machine learning benchmark and comprise:

• Metadata-encapsulated datasets
• Release-ready implementations of spectrum analytics methods
• Benchmark metrics
• Performance indicators for the released methodologies against the released datasets

Students will:

• Help implement and release of a metadata standard and corresponding toolbox, which we’ve been researching
• Repackage existing datasets or re-run existing experiments with our proposed metadata schema
• Employ existing implementations or reimplement research prototypes for broad release
• Establish and maintain the benchmark website
• Participate in lab meetings to present their methodologies and findings and train their presentation skills
• Contribute to paper development to train their technical writing

Student Skills/Requirements: 

• Required: 
  • Proficient in Python programming
  • Three years in computer science, electrical and computer engineering, or a related program
• Optional: 
  • Coursework and prior experience in systems engineering, machine learning, open-source projects

Location: UAB 417

Students from the following tracks are encouraged to apply:

• Track 1: SURP-funded
• Track 2: Faculty-grant Funded SURP
• Track 3: Volunteer
• Track 4: Research for Credit

Mentor: Mustafa Aksoy

This project aims to characterize the electrical properties (specifically, the complex permittivity) of lunar regolith simulants representing both lunar mare and highlands regions, in support of NASA’s Artemis program and future lunar missions. 

The study will measure the real and imaginary parts of permittivity across a wide range of microwave frequencies and under varying temperatures using an advanced microwave network analyzer and a specialized temperature-controlled chamber in UAlbany's Microwave Remote Sensing Laboratory, supervised by the mentor. 

These measurements are essential for understanding how electromagnetic waves interact with lunar regolith, which directly impacts the performance and interpretation of microwave remote sensing instruments such as radar and radiometers used for subsurface mapping, ice detection and geological analysis. 

The resulting comprehensive database of permittivity values — unprecedented in scope — will improve data interpretation from remote sensing missions, aid in designing and calibrating antennas and radar systems and support site selection, resource identification and infrastructure planning for sustained lunar presence. 

Beyond its scientific contributions to lunar exploration and potential extension to other planetary bodies, the project offers valuable hands-on training in electromagnetic theory, materials science and space technologies for the undergraduate researcher.

Student Skills/Requirements: 

• Matlab
• Advanced Physics and Math Skills
• High GPA (ideally above 3.5) undergraduate student

Location: CNSE Downtown

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 2: Faculty grant funded SURP
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Nathan Dahlin

Solar energy deployment has accelerated significantly in New York in recent years. To reduce costs and improve grid integration, accurate hour-ahead and day-ahead forecasts are essential. However, the intermittent nature of solar output makes reliable forecasting challenging. 

In this project, students will explore various time-series forecasting approaches, ranging from classical autoregressive models to modern solutions like Facebook’s Prophet and IBM’s Granite time-series models. 

Students will assess the strengths and limitations of each method via evaluations involving real-world datasets and develop an ensemble approach that combines multiple techniques for enhanced solar energy forecasting accuracy. The findings will be relevant to NYISO and other grid operators and energy stakeholders.

Student Skills/Requirements:

• Familiarity with python AI/ML/data science packages such as Scikit-Learn, XGBoost, SciPy is preferred
• Statistics, AI/ML background encouraged

Location: CNSE Downtown 301H

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Aveek Dutta

This project requires knowledge of PCB layout tools and network analyzers for testing. Students are expected to work with graduate students to carry out this task.

Student Skills/Requirements:

• Python
• AWS
• Distributed Systems

Location: ETEC

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Aveek Dutta

Implement a custom Blockchain protocol and Smart Contracts in AWS, while closely working with other graduate students. The student will build on an existing codebase to include new features for the system.

Student Skills/Requirements:

• Python
• AWS
• Distributed Systems

Location: ETEC

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Dola Saha

The project requires capturing baseband signals using multiple software-defined radios at various frequencies, which will be used for UAV detection. The transmitter will transmit 5G signals, which will get reflected by the rotating propellers of UAV to show micro-doppler effects. The signals captured by all the UAVs should be automatically stored with proper timestamp and metadata.

Student Skills/Requirements:

• Familiarity with MATLAB, Linux Shell, Basic Programming (Scripting/Python/C)

Location: CNSE Downtown EN304

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Gary J. Saulnier

Create a graphical user interface for an electrical impedance tomography (EIT) medical device using the C/C++ programming language. EIT produces images of the interior of the body by injecting small currents through electrodes on the body’s surface, measuring the resulting voltages, and solving the inverse problem to find the admittivity distribution within the body. The interface will work with existing software to perform instrument control and display reconstructed images in real time.

Student Skills/Requirements:

• Advanced C/C++ programming
• Ability to find solutions to programming challenges

Location: CNSE Downtown ENB001

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Saurabh Sihag

This project focuses on developing novel graph neural network models to assess neurodegeneration in brain imaging datasets. The students will be expected to curate brain imaging data from raw MRI scans; implement graph neural networks for supervised and unsupervised learning tasks; and perform thorough statistical analysis to validate biomarkers in disease cohorts.

Student Skills/Requirements: 

• Python
• Machine learning
• Statistics
• Second or third year Electrical & Computer Engineering, Mathematics and Computer Science students preferred

Location: CNSE Downtown EN204

Students from the following tracks are encouraged to apply:

• Track 2: Faculty grant funded SURP

Mentor: Daphney-Stavroula Zois

Supervised machine learning models typically assume that training labels are accurate. However, in many real-world applications, labels are noisy due to human error, ambiguity or automated annotation pipelines. 

This project will focus on understanding and mitigating the impact of label noise on model training and generalization. The student will investigate different types of label noise (such as symmetric noise, class-dependent noise, instance-dependent noise) and implement robust training strategies such as noise-tolerant loss functions, sample reweighting and data cleaning approaches. 

Through controlled experiments on benchmark datasets, the student will analyze how label noise affects learning dynamics and model reliability. The project aims to provide both foundational research experience and practical insights into building trustworthy AI systems and potentially lead to novel improvements for training trustworthy machine learning systems in imperfect data environments.

Student Skills/Requirements:

• Basic knowledge of machine learning
• Programming experience in Python
• Familiarity with linear algebra and probability
• Experience with libraries such as PyTorch/TensorFlow and scikit-learn
• Interest in research and willingness to read and discuss academic papers

Location: CNSE Downtown

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Md. Aynul Bari

The primary objective of this project is to develop, validate and deploy a high-resolution, spatiotemporal artificial intelligence (AI) framework to predict ambient concentrations of black carbon (BC) and brown carbon (BrC) in data sparse underserved communities in New York State. 

By leveraging multiple datasets — including BC and BrC data from 15 cost-effective carbon monitors already deployed in New York State, reference aethalometer data and satellite-derived and meteorological data — we will perform model validation, uncertainty quantification and explainable AI (XAI) analysis to determine key pollution drivers, which provides “actionable intelligence” central to the project's vision, empowering communities and regulators with the evidence needed to advocate for and implement targeted, effective environmental policies.

Student Skills/Requirements:

• Python
• AI/ML models
• Third year student in Electrical & Computer Engineering

Location: ETEC B039

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Md. Aynul Bari

In collaboration with the Department of Cybersecurity, the project will build a layered security and privacy framework that integrates technical safeguards with transparency and privacy to build trust among community stakeholders. Leveraging the indoor and outdoor low-cost air quality sensor data collected through the EPA-funded project, we will apply encryption and validation to ensure secure data transmission. 

Different privacy-preserving measures such as Differential Privacy (DP) and Role-Based Access Control (RBAC) will be applied to protect sensitive information and maintain compliance with data governance standards. The layered approach will guarantee confidentiality, transparency and resilience throughout the data lifecycle.

Student Skills/Requirements:

• Python
• AI/ML models
• Third year student in Electrical & Computer Engineering

Location: ETEC B039

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Md. Aynul Bari

In the United States, community air quality observation networks are limited in characterizing the diverse group of air pollutants including climate-driven pollutant black carbon (BC) and brown carbon (BrC) that affect exposure across regional to neighborhood scales. 

Under the initiatives of EPA enhanced air quality monitoring for communities, we will measure indoor and outdoor concentrations of BC/BrC and other pollutants in at least 30-40 homes/apartments in New York City. 

The study will increase public awareness and understanding of community-specific air quality problems, identify local sources and indoor predictors, and guide how to reduce indoor air pollution.

Student Skills/Requirements:

• Python/R
• Field campaigns

Location: ETEC B039

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Rixiang Huang

New York City’s drinking water is primarily sourced from the Catskill Mountains. During transport from mountain top, land, streams and finally to water reservoirs, water interacts with plants and soils, affecting its composition. 

This project will study the effects of plant species (decomposition of their litter) and soil erosion (from agriculture land and riverbank) on water composition, combining field sampling and laboratory experiments. Students can work on either the plant litter or sediment part of the project.

Student Skills/Requirements:

• Environmental science and/or engineering
• Biology
• Chemistry
• Geosciences

Location: ETEC 040

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 4: Research for credit

Mentor: Lu Li

Join a summer research team building a Smart, Sustainable and Healthy City through data. You’ll analyze real air-quality and energy datasets to uncover when and where pollution spikes and energy use surges, and why. 

You’ll learn practical data analysis skills, visualization and how to translate results into solutions for healthier communities. By summer’s end, you’ll create a city sustainability dashboard or research poster that you can showcase.

Student Skills/Requirements: 

• No specific requirements

Location: ETEC 121

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Yanna Liang

Emerging contaminants have become and caused significant concerns to the general public and regulatory agencies. To remove them from the environment, Liang's lab has been working on a systematic approach that integrates bioremediation, phytoremediation and green material design. By combining different processes, we have been able to find transformative solutions to minimize the impact of these pollutants on the public and the ecosystem.

Student Skills/Requirements: 

• Third year and above chemistry, biology and environmental engineering students

Location: ETEC 24

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: John D. Paccione

Photoreactors are used to run a variety of reactions, including treating water for regulated compounds such as 1,4 dioxane. These reactors are designed to ensure the concentration of the targeted compound is reduced to a level that is below the maximum contaminant level. 

During process startup, there is a lag between process initiation and steady state. It is important for process operators to understand the number of reactor turnovers that will be required before the concentration of the targeted agent is at or below the maximum contaminant level. 

This work will use traditional reactor engineering analysis that will be applied to an important environmental engineering problem.

Student Skills/Requirements:

• MATLAB
• Excel
• Differential Equations
• General Chemistry

Location: Corning Tower 1376

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: John D. Paccione

Natatoriums, food preparation areas and other occupied spaces may become microbiologically contaminated in a variety of ways that present a potential risk of infection to occupants and those served by these facilities. Traditional disinfection methods focus on the use of chemical disinfectants, such as sodium hypochlorite, quaternary ammonium compounds and peracetic acid products. However, these disinfection procedures are both time-consuming and labor-intensive. 

One response to this problem is to implement the use of germicidal UV (GUV) in these kinds of spaces, which allows both surfaces and air to be disinfected simultaneously. The results of this kind of disinfection will reduce labor and be potentially more effective as the disinfection process requires the illumination of GUV light. However, one of the hurdles that must be overcome is the lack of understanding of how these processes are run and what parameters are important. 

One misperception in the industry that needs to be addressed is the potential false sense of security that comes with applying a technology without understanding what variables and parameters are important. The analysis of the disinfection process for surfaces requires a simple calculation of intensity multiplied by time. However, the air disinfection process requires further analysis as the air changes that occur due to ventilation affect the “contact time” of the air with the GUV. 

To address this, it is possible to model the disinfection process by making some first order approximation assumptions to demonstrate how light intensity, air flow patterns and flow rate affect the quality of air in an occupied space. The focus of this work is to model air flow in an occupied space and to use first-principles disinfection models to demonstrate the important parameters that affect the efficiency of an air and surface disinfection process.

Student Skills/Requirements:

• MATLAB
• Excel
• Differential Equations
• General Chemistry

Location: Corning Tower 1376

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Weilan Zhang

This project aims to support U.S. EPA-associated stakeholders in understanding the factors influencing PFAS accumulation and plant uptake resulting from the land application of biosolids in agricultural settings. The study will explore how different plant species, biosolids treatment and soil types affect PFAS uptake by crops. 

By examining these variables, the project will provide valuable insights into the movement of PFAS within agricultural ecosystems, contributing to broader efforts in environmental sustainability and food security and informing sustainable land management practices.

Student Skills/Requirements:

• STEM majors with wet lab experiences

Location: ETEC B020

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Bu Zhao

This project will explore how used household electronics — such as phones, laptops and TVs — move through U.S. homes over time and how much valuable metal they contain. 

Using material flow analysis (MFA), students will track the “life cycle” of these products from purchase to disposal and estimate the size of urban “mines” of copper, gold and other critical materials stored in households. Machine learning (ML) models will then be used to map where and when electronic waste (WEEE) is generated across the country, revealing hotspots of untapped secondary resources. 

Together, these tools will help identify opportunities to recover valuable materials, reduce environmental impacts and support a more circular, sustainable electronics system in the United States.

Student Skills/Requirements:

• Basic knowledge of coding in Python or R
• Open to junior or senior-level engineering students

Location: ETEC 133

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer

Mentor: Robert Brainard

The goal of this project is to develop organometallic compounds that can be used as high resolution photoresists in the microelectronics industry to fabricate future integrated circuits. 

Students will synthesize and/or characterize compounds containing main-group metals. These compounds are designed to undergo chemical reactions when irradiated with 13.5 nm extreme ultraviolet light resulting in a change in solubility.

Student Skills/Requirements:

• No experience necessary but strong background in chemistry required
• Rising sophomores are encouraged to apply

Location: CESTM 344 or 135

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 2: Faculty grant funded SURP
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Robert Brainard

The goal of this project is to develop a methodology for controlling the timing of self-assembly of bilayer stacks upon which cells are growing. The ultimate goal is to determine how shape changes influence the biology of cells. 

Students will synthesize polymers and formulate polymers into photoresists, which will be coated onto silicon wafers into multiple stacks of hydrogel films. Students will study the kinetics of self-assembly of these multi-layer stacks under conditions suitable for cell growth and may participate in growing cells onto these stacks.

Student Skills/Requirements:

• No experience necessary but strong background in chemistry and/or biology required
• Rising sophomores are encouraged to apply

Location: CESTM 344 or 135

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 2: Faculty grant funded SURP
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Nathaniel Cady

Nanotechnology is currently being used for many different applications, including the development of faster, more efficient computer chips, but did you know that nanotechnology has also been used for biology? 

In our research group, we explore the interface between nanotechnology and biology, which enables us to develop and test new types of biological sensors that we use to diagnose diseases and study biological events at then nano and micrometer size scale. 

In this project, you’ll participate in our ongoing research efforts in biosensor development and microfluidic devices, with a focus on disease diagnosis and studying the interactions between biological systems and nano/micro patterned surfaces.

Student Skills/Requirements:

• Biology and chemistry lab courses or research experience
• Computer-aided design
• Chemistry

Location: NFE 4904

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 4: Research for credit

Mentor: Nathaniel Cady

Artificial intelligence and machine learning are taking the world by storm. However, these forms of computing are highly energy intensive. My research group develops unique memory devices called memristors (also known as resistive random access memory or RRAM) that can be used for highly efficient in-memory computing and neuromorphic computing. 

These devices could significantly reduce the power requirements for AI and machine learning algorithms and training. Student interns working on this project will gain experience with the fabrication and electrical testing of memristors and/or application development for these devices in microchip-based format.

Student Skills/Requirements:

• Electrical engineering
• Materials science
• Python-based programming

Location: NFE 4904

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 4: Research for credit

Mentor: Arun Richard Chandrasekaran

Our lab concentrates on using DNA as a building block to design and create nanostructures such as DNA polyhedra, 3D lattices and dynamic DNA devices. 

Available projects focus on functionalizing DNA nanostructures with drug attachment and release chemistries for using in drug delivery, enhancing the biostability of DNA nanostructures, encoding binary information in DNA structures and structural analysis of biomolecular nanostructures.

Student Skills/Requirements:

• Basic chemistry and biology courses

Location: LSRB 2089

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 4: Research for credit

Mentor: Gregory Denbeaux

This project aims to understand the range of electrons and acids in chemically amplified photoresists, especially toward how the number/volume of reactions in the resist correlate with roughness of the resist — an important criteria for photoresist performance in semiconductor manufacturing. 

Experiments will include resist formulation, spin coating, electron exposures, development and thickness measurements with ellipsometry and roughness measurements with atomic force microscopy (AFM).

Student Skills/Requirements: 

• Basic physics or engineering background

Location: CESTM L246

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 2: Faculty grant funded SURP
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Gregory Denbeaux

We are working with a university that has developed a novel molecular layer deposition material that works as a photoresist for EUV lithography. 

Traditional photoresists are spin-coated with no net orientation. A resist that could have vertical reaction processes would have a tremendous advantage as the reactions would be vertical and not have as many issues of lateral diffusion, which limits resolution. 

This project will study these materials and try to understand the reaction mechanisms.

Student Skills/Requirements:

• Basic physics or engineering background

Location: CESTM L246

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 2: Faculty grant funded SURP
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Harry Efstathiadis

This study will examine lithium distribution and its evolution in both anode and cathode materials of lithium-ion cells subjected to high C-rate cycling, providing insights into lithium loss, trapping and plating mechanisms. 

Cells will be cycled at 1C to 3C rates, and post-mortem analysis will be performed using Li nuclear reaction Analysis (Li-NRA) and additional materials characterization techniques.

Student Skills/Requirements:

• Appropriate for juniors and seniors

Location: Physics 320

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 4: Research for credit

Mentor: Walid Redjem

This project introduces undergraduate students to the design of quantum photonic integrated circuits fabricated in a CMOS silicon photonics foundry. 

The student will use Python-based design tools to create optical circuits such as waveguides, directional couplers and interferometers that are used to generate and measure quantum states of light. They will learn how photonic chips are translated from layout files to fabricated devices and how these circuits are tested using lasers and photodetectors in the laboratory. 

The project will also include simulation and automated data acquisition to characterize device performance. Ultimately, the student will have contributed to a real research chip submission and gained experience highly relevant to graduate school and the semiconductor/quantum technology industry.

Student Skills/Requirements:

• Python

Location: NFE 1906

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Susan Sharfstein

3D tissue models, which provide physiologically relevant representations of human biology, rely heavily on hydrogels as culture substrates. However, these models are limited by the inability to interrogate their internal states. Recently, there has been growing interest in using hydrogels to create photonic (such as light-based) sensors to address these limitations. 

Working within a larger initiative focused on hydrogel photonic biosensors, this project aims to create hydrogels with tunable cellular interactions through the inclusion of cell attachment molecules and incorporation of micro- and nanostructured surface modifications. 

Participants will become familiar with adherent cell culture, hydrogel fabrication and characterization, and multiple cell adhesion assay formats.

Student Skills/Requirements:

• Background in biology, chemistry, chemical or biomedical engineering, with at least two years completed

Location: NFE 4906

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Susan Sharfstein

Chinese hamster ovary (CHO) cells are widely used in the pharmaceutical industry for the production of large, complex biomolecules. Different CHO cell lines exhibit differential oxygen requirements based on the host cell line and product molecule in a way that remains largely understudied.

This project aims to use oxygen-sensitive nanoparticles to resolve internal oxygen concentrations in range of different CHO cell lines using imaging flow cytometry. Participants will synthesize and optimize nanoparticles for cellular uptake and oxygen sensitivity assessment and become familiar with CHO cell culture, fluorescence microscopy and flow cytometry.

Student Skills/Requirements:

• Background in biology, chemistry, chemical or biomedical engineering, with at least two years completed

Location: NFE 4906

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Scott Tenenbaum

Structurally interacting RNA (sxRNA) is an RNA switch technology that requires the stem loop binding protein (SLBP), an RNA binding protein, to bind to the histone stem loop (HSL), an RNA stem loop motif. When sxRNA is incorporated into an mRNA sequence, it acts as a switch that controls where the mRNA is translated. The HSL-SLBP translation mechanism is analogous to Poly-(A) translation, but has consistently less protein production. 

By creating an sxRNA GFP-SLBP fusion protein positive feedback, it is hypothesized that the expression levels of the sxRNA will increase. The SURP student will work on designing the GFP-SLBP fusion protein by investigating the best protein linker sequence and protein order. Fusion protein expression levels with be compared side by side with current sxRNA positive control and poly-(A) expression levels using NIH 3T3 mouse embryonic fibroblasts as a model cell line.

Student Skills/Requirements:

• Third year nano or bio student
• Cell culture

Location: NFE 4905

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded
• Track 3: Volunteer
• Track 4: Research for credit

Mentor: Christophe Vallee

Atomic scale processes like Atomic Layer Deposition (ALD) are essential for large-scale implementation of superconducting quantum devices as a perfect control of material properties and chemical composition at the nanometer scale are mandatory. Plasma assistance in the ALD process may allow lower temperature processing and better film quality thanks to highly reactive neutral radical species from the plasma. 

In this work, we propose to study additional plasma components and their impact the film properties like the positive ions. We will also try to selectively deposit the superconducting film on NbN areas versus SiO2 areas using a small molecule inhibitor treatment before the ALD process.

Student Skills/Requirements:

• Backgrounds in physics and chemistry/materials

Location: CESTM L136

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Christophe Vallee

Plasma-enhanced atomic layer deposition (PE-ALD) is an essential method for the large-scale implementation of ultra-thin, uniform and highly conformal layers required in the fabrication of advanced nanoscale devices.

At the same time, stability of material properties and chemical composition are important. Conductive transition metal nitrides (such as HfNx, TiNx, TaNx) are widely used for nanoelectronics but have a tendency to oxidize and increase resistance. 

We will compare the conductivity and stability of TiNx and HfNx films depending on process parameters. For example, we will study the substrate biasing influence on film crystallinity and chemical composition.

Student Skills/Requirements:

• Backgrounds in physics and chemistry/materials

Location: CESTM L136

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Carl Ventrice

This project involves the temperature-dependent characterization of MEMS-based RF switches. It is a collaborative project with Menlo Microsystems. The goal is to determine the dynamic performance and reliability of the MEMS-based RF switches at the cryogenic temperatures that are needed for quantum computing applications.

Student Skills/Requirements:

• A background in physics and/or engineering is preferred

Location: CESTM R111

Students from the following tracks are encouraged to apply:

• Track 2: Faculty grant funded SURP

Mentor: Yubing Xie

Vascularization is a critical feature of 3D tissues, supporting cell survival and function by delivering oxygen and nutrients to deeper regions of the tissue construct. In native tissues, capillaries are typically spaced within ~100 µm, ensuring adequate mass transport. 

However, most tissue-engineered 3D models lack functional vasculature, leading to poor nutrient diffusion, cell death and formation of necrotic cores. Beyond transport, blood vessels also actively communicate with surrounding cells through paracrine signaling, playing an essential role in regulating tissue development and function. 

This project will investigate how scaffold presence and guided topographical and architectural cues influence 3D vascular network formation. The student will gain hands-on experience in fabricating 3D bio-scaffolds, mammalian cell culture, immunocytochemistry, microscopy and biological assays, providing comprehensive training in biomaterials-based tissue engineering and vascular biology.

Student Skills/Requirements:

• Biology, biochemistry, biomedical engineering, chemical engineering, nanoscale science, nanoscale engineering or equivalent majors

Location: ANC 4902

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded

Mentor: Yubing Xie

Immune cells are central to nearly every disease and injury in the body, where they coordinate damage response, repair and long-term tissue remodeling through continuous communication with both tissue parenchyma and the surrounding stromal environment. 

Despite this critical role, most current 3D tissue models lack a dedicated immune cell component, limiting their ability to capture dynamic immune signaling, feedback and regulation that occur in living tissues. 

This project will focus on engineering a 3D immune compartment — using biomaterial scaffolds with defined mechanical and architectural properties to investigate how scaffold composition, stiffness and microarchitecture influence immune cell viability, phenotype and activation states — and establishing a modular platform with future potential for integration with other tissue compartments. 

The student will gain hands-on experience in biomaterial fabrication, mammalian cell culture, immunostaining, microscopy and functional immune assays, providing foundational training in biomaterials-based tissue engineering and immunoengineering.

Student Skills/Requirements:

• Biology, biochemistry, biomedical engineering, chemical engineering, nanoscale science, nanoscale engineering or equivalent majors

Location: ANC 4902

Students from the following tracks are encouraged to apply:

• Track 1: SURP funded