2024 Faculty

Faculty for summer 2024 are not confirmed yet, but the names listed below are most likely participating.
Professor: Laura Lewis [ChemE] | Research Page
Mentor: TBD
REU Student: TBD
Project: Life Cycle Assessment/Techoeconomic Analysis of permanent magnets

Professor: Amy Mueller [CEE] | Research Page
Mentor: TBD
REU Student: TBD
Project: Studying VOC pollution in Boston though sensors, field work, and data analytics

Professor: Muhammad Noor E Alam [MIE] | Research Page
Mentor: TBD
REU Student: TBD
Project: Healthcare Analytics

Professor: Sarah Ostadabbas [ECE] | Research Page
Mentor: TBD
REU Student: TBD
Project: Computer Vision and its Applications in Infant Health Monitoring

2023 Faculty

Professor: David Kaeli [ECE] | Research Page
Mentor: Kaustubh Shivdikar
REU Student: Ayman Alabbasi & Shamil Imakaev
Project: Leveraging Parallel Processing for Advanced Graph Computations

Professor: Michael Kane [CEE] | Research Page
Mentor: Katherine Bassett & Emma Casavant | SungKu Kang & Jose Velazquez Avila
REU Student: Zoe Chappell & Justin Muñoz & Sarah Callecod
Project 1: Cleaning, Curation, and Communication of Home Metadata
Project 2: Demographic and Building Information for Residential Load Flexibility Data Set

Professor: Laura Lewis [ChE] | Research Page
Mentor: Criss Zhang
REU Student: Isabella Eliassen
Project: Creating a Magnetic Field Gradient for Engineering of Magneto-Functional Materials

Professor: Amy Mueller [CEE] | Research Page
Mentor: Ben Eck & Ruifeng Song (Link)
REU Student: Olivier K. Francois & Jodi Zangari
Project 1: Mechanical Design of Sequential Injection Analysis System for Nitrate Sensing
Project 2: Cross-Calibration and Visualization of Data for a Portable Air Quality Monitor

Professor: Sarah Ostadabbas [ECE] | Research Page
Mentor: Elaheh Hatami
REU Student: Muskan Kumar
Project: Analyzing Infant Behavior Using Machine Learning

2022 Faculty

Professor: David Kaeli [ECE] | Research Page
Mentor: Kaustubh Shivdikar
REU Student: Lina Adkins
Project: Accelerating Operations on Graph Neural Networks

Professor: Laura Lewis [ChE] | Research Page
Mentor: Criss (Xiaoyu) Zhang
REU Student: Frances Power
Project: Using Fourier Transforms to Disambiguate Microstructures of Ferromagnetic FeSiB Ribbons

Professor: Carol Livermore [MIE] | Research Page
Mentor: Ashkan Ghanavati, Chastity Kelly
REU Student: Thomas Fitzgerald
Project: Relating Carbon Nanotube Network Structure to Mechanical and Viscoelastic Performance

Professor: Aatmesh Shrivastava [ECE] | Research Page
Mentor: Mostafa Abedi
REU Student: Matthew Sharon
Project: Analog Computing Simulation Tool for Machine Learning Inference in Edge Biomedical Devices

Professor: Rifat Sipahi [MIE] | Research Page
Mentor: Xu Wang
REU Student: Xeon and Seartch Delva
Project 1: Causal Coupling Inference in Mass Shootings Data
Project 2: Information Theory to Pinpoint Causal Links from Complex Data

Professor: Qing Zhao [ChE] | Research Page
Mentor: Pax Makhura
REU Student: Tyler Arnold
Project: Computational Design of Single Atom Catalysts for Electrochemical CO2 Reduction

Possible Projects:

Project: Exploring GPU Acceleration on Emerging Machine Learning Algorithms and Applications
Professor: David Kaeli
Project Description: Machine learning algorithms are enabling new discoveries in health, security and the environment. These algorithms are very powerful, but they rely on analysis of growing mountains of data. The processing required to train a machine learning model can become a major barrier to scientific discovery. In this project we leverage the power of graphic processing units to advance the state-of-the-art in a variety of applications. Students will have access to high performance computing clusters and will learn how to program on these platforms.
Learning Objectives: Students will 1) learn how to run machine-learning algorithms on state-of-the-art graphics processing units; 2) learn and use Python.
Role of REU Student: Students will work with large data sets and will run Python-based programs to identify patterns in the data. Problem areas include health, security and the environment.
Expected Learning Outcomes: The student gains these skills: (1) deal with high-speed computers; (2) learn and use the popular Python programming language; (3) use large data sets for data mining and analytics.

Project: Analog Computing Simulation Tool for Machine Learning Inference in Edge Biomedical Devices
Professor: Aatmesh Shrivastava
Project Description: Wearable and implantable biomedical devices continue to disrupt healthcare through the development of continuous sensing and monitoring, timely medical intervention, efficient and tailored drug delivery, and reduced medical cost. However, a significant technological gap exists when it comes to adopting ML (machine Learning) based solutions for wearable and implantable biomedical devices. Power consumption and hardware requirement make it challenging to integrate ML solutions in existing mobile health technologies. This project aims to develop ML based hardware solutions for mobile health technologies.
Learning Objectives: (1) develop basic understanding of basic ML algorithms; (2) develop software coding skills with the development of Matlab codes; (3) develop skills in technical writing and presentations.
Role of REU Student: (1) Model and evaluate analog computing circuits to be used for ML network; (2) Model and evaluate nonidealities in analog computing and its impact on network performance.
Expected Learning Outcomes: (1) learn about the use of Machine learning to conduct simulation analysis; (2) apply Machine Learning in mobile healthcare applications; (3) gain basic coding skills in MATLAB.

Project: Visualizing Infrastructure Dynamics
Professor: Michael Kane
Project Description: Today, we sit on the cusp of the information age and the age of automation, where computations break free of cyberspace–ubiquitously affecting the physical world. This future of closed-loop cyber-physical systems (CPS), moving beyond sensing and analytics, will overcome the barriers to sustainable and resilient infrastructure. This project will conduct a literature review of everyday applications and cutting-edge innovations of automation and controls in civil infrastructure to better understand how breakthroughs in automation and machine learning can be generalized to lead to a smarter, more resilient and adaptive built environment.
Learning Objectives: (1) Comprehend and extract information relevant to a query from scientific literature, and (2) Synthesize findings from literature and professional knowledge into a persuasive story.
Role of REU Student: The student will: (1) conduct a literature review, mentoring by graduate students trained to do so. (2) interview professionals to synthesize connects between science and industry.
Expected Learning Outcomes: The students will gain knowledge about: (1) cyber-physical systems; (2) conducting scientific literature review; (3) impact of Machine Learning on building better infrastructure.

Project: Li-ion conductivity in nanocomposite solid electrolytes
Professor: Joshua Gallaway
Project Description: Lithium-ion batteries have revolutionized portable power, allowing production of handheld tools, cell phones, and laptop computers. However, the flammability of the liquid carbonate electrolytes used in Li-ion batteries make them a safety risk because a break in the cell casing can ignite the electrolyte and cause a runaway reaction. This research project focuses on the production of electrolytes that transport Li ions with high conductivity to enable all solid state batteries eliminate electrolytes from the design. Experiments will be performed in an inert atmosphere glovebox.
Learning Objectives: (1) Understand transport concepts in solid state; (2) Understand the effect of solid state composite on conductivity; (3) Understand the used of electrochemical impedance spectroscopy (EIS).
Role of REU Student: student will: (1) Construct electrochemical cells using solid electrolyte nanocomposites. Use electrochemical impedance spectroscopy (EIS) to measure transport in solid state systems.
Expected Learning Outcomes: (1) gain knowledge about nanocomposites; (2) conduct controlled experiments; (3) design safer lithium-ion batteries for consumer portable devices.

Project: Investigating and Controlling Phase Transitions in Functional Magnetic Materials
Professor: Laura Lewis
Project Description: Magnetism and magnetic systems permit the interconversion of mechanical, electrical and thermal energy that is foundational to a wide variety of technologies, including hybrid vehicles, wind power and robotics. The functionality of a magnetic system is determined by the condition of the magnetic components within that system. Some highly responsive magnetic phases feature unusual coupling between the crystal lattice and the electronic configuration, allowing phase formation in these systems to be controlled by application of strain, magnetic & electric fields in addition to standard temperature application.
Learning Objectives: (1) understand of fundamental of phase transitions; (2) gain a basic understanding of magnetic materials; (3) operate sensitive equipment; (4) learn how to analyze and interpret calorimetric data.
Role of REU Student: (1) investigate the effect of an in-situ magnetic field on thermodynamic phase transitions; (2) gain knowledge of thermodynamics of phase transitions; (3) learn to perform data analyses.
Expected Learning Outcomes: (1) learn magnets science and materials; (2) learn data collection and analysis; (3) analyze and interpret scientific results.

Project: Relating Carbon Nanotube Network Structure to Mechanical and Viscoelastic Performance
Professor: Carol Livermore
Project Description: Vibration damping by structural materials is a critical requirement for creating a comfortable consumer experience and for protecting everything from soldiers to bridges. In particular, we need stiff, strong, lightweight materials that also dissipate energy effectively through viscoelastic deformation, but current materials are largely an either/or compromise between load bearing and vibration damping. This research project involves mechanical characterizations over a range of loading frequencies, amplitudes, and temperatures plus structural characterization coupled with deep learning to reveal the structure-property relationships.
Learning Objectives: (1) understand how mechanical testing elucidates energy dissipation; (2) predict the effects of network structure on energy dissipation; (3) predict the effects of network structure on storage modulus.
Role of REU Student: (1) evaluate how energy dissipation varies with loading amplitude; (2) evaluate the dependence of storage and loss moduli on network structure through dynamic mechanical analysis testing.
Expected Learning Outcomes: The student will (1) gain knowledge of carbon nanotubes; (2) know how to conduct mechanical testing; (3) evaluate mechanical properties of carbo nanotube network structures.

Project: Endothelial Mechano-Biology Research
Professor: Eno Ebong
Project Description: We seek to define endothelial cell and glycocalyx mechanisms of blood vessel regulation by solid-fluid mechanics. The project tests the hypothesis that fluid (blood) and solid (blood vessel wall) forces cooperate to regulate endothelial cell behavior via the glycocalyx, a sugar complex that is anchored to and coats endothelial cells and can convert fluid and solid force stimuli into cellular responses. Endothelial cells lie at the vascular fluid-tissue interface and play an essential role in maintaining blood vessel health. An incomplete knowledge of endothelial cell mechanobiology mechanisms results in limited vascular disease prevention and treatment efficacy. This project will address this critical knowledge gap.
Learning Objectives: (1) Characterize glycocalyx architecture; (2) Link glycocalyx structure to cell molecular mechanisms; (3) Clarify how molecular stimuli evoke an endothelial cell response that impacts production of nitric oxide, a vasodilator.
Role of REU Student: (1) design and conduct rigorous research in the areas of bioengineering and endothelial mechanobiology: (2) collect and access relevant data; (3) create written and oral presentations of study results.
Expected Learning Outcomes: (1) apply interdisciplinary fields to research; (2) design of experiments and data interpretation; (3) responsible and ethical conduct of research; (4) technical communication.

Project: Enhancing Photosynthesis for the Production of Useful Chemicals in Plants
Professor: Carolyn Lee-Parsons
Project Description: Depletion of fossil fuel reserves and climate change attributed to greenhouse gases are imminent challenges facing our planet. To address both of these global challenges, research focuses on engineering plants for the production of useful chemicals previously derived from petroleum. This project focuses on altering plant development to yield more leaves by which light capture, carbon fixation, and conversion to sugars to useful molecules can occur. This project involves characterizing the effect of several transcription factors that control plant development and ultimately their ability to capture energy through photosynthesis and conversion of these carbon skeletons to useful chemicals.
Learning Objectives: (1) explore how photosynthetic capabilities and biomolecule production can be enhanced; 2) design experiments to measure the efficiency of photosynthesis; 3) perform basic molecular biology methods.)
Role of REU Student: (1) evaluate the effect of silencing specific factors on the extent of chloroplast or leaf development; 2) evaluate the effect of overexpressing these specific factors on the expression of genes.
Expected Learning Outcomes: The student will know (1) biomolecular engineering; (2) design of experiments; (3) computational biology.

Project: Information Theory to Pinpoint Causal Links from Complex Data
Professor: Rifat Sipahi
Project Description: Capability to pinpoint causal relationships between interacting dynamical systems provides many opportunities in understanding the world surrounding us, from how animals interact with each other in group settings to explaining how public health policies influence human behavior. Performing such inferences are however difficult given only measured data pertaining to the dynamics. Information Theory provides powerful statistical tools to achieve this inference effort in an effective manner. In this project, the researcher will learn about Information Theory, develop some of the Information Theory metrics in a software package and perform analysis on real world data.
Learning Objectives: (1) learn statistical tools to investigate the data to draw critical conclusions; 2) learn the foundations of information theory; (3) learn coding skills in Matlab or Python.
Role of REU Student: (1) use data and/or collect new data in an area related to health; 2) analyze the data; (3) present the results to a small audience including what-if analysis.
Expected Learning Outcomes: (1) apply information theory statistical tools; (2) perform data collection and analysis; (3) learn how to use Python and/or MATLAB.

Project: Designing sensor systems that support smart operations of environmental infrastructure
Professor: Amy Mueller
Project Description: The Environmental Sensors Lab is actively engaged in research thrusts related to stormwater, wastewater, aquaculture, and coastal environmental protection. Water chemistry is important in all of these scenarios, to ensure that infrastructure and anthropogenic activities do not adversely impact surface water quality. A major research challenge is designing sensors that operate correctly in these very different and sometimes chemically challenging environments. This lab is working on (1) design of novel printable nutrient sensors, and (2) developing advanced data analytics approaches that can integrate expert knowledge (for instance, about water chemistry) with sensor measurements to report usable information to city managers and citizens.
Learning Objectives: (1) learn about experimental design as applied to studying the environment; (2) gain experience in lab work; (3) understand how to integrate and compare datasets with different sampling rates and levels of uncertainty.
Role of REU Student: (1) participate in lab and field testing of sensors and sensor arrays in stormwater sewers; (2) collect physical water samples, (3) perform lab analysis of samples; (4) compare results of lab analyses with sensor readings.
Expected Learning Outcomes: The students will acquire these skills: (1) use smart sensors; (2) conduct sampling collection and analysis; (3) use advanced data analytics methods.

Blog Posts – by Date

Blog Posts – by Category

Learn More about our Programs