Mohsen Baidey
Applied AI for underwater acoustics and environmental systems
Grand Challenge: Engineer tools of scientific discovery
This project has two branches. On the software side, it will apply signal processing and applied machine learning techniques for underwater acoustics and environmental systems using real-world data. This involves numerical modeling of underwater sound transmission and sea-going
opportunities for data collection and experimentation. On the hardware side, it will design multichannel data acquisition systems, which involves electronic instrumentation such as microcontroller programming and embedded systems for underwater sensors.
Suggested coursework: Good analytical and mathematical skills, applied physics skills, and Python/Matlab programming skills.
Austin Brockmeier
Simultaneous localization and mapping with brain waves
Grand Challenge: Reverse-Engineer the Brain
Mammalian brains consist of circuits of neurons spatially organized and interconnected. The activity of populations of these neurons produce brain waves that can be recorded as signals with implanted, flexible micro-electrodes. The goal of this project is to see if the occurrence of distinct patterns in the brain waves (local field potentials), which are specific to the circuits involved, can be used to deduce where in the brain the electrode is.
Suggested coursework: signals & systems, Python/Matlab programming, linear algebra, statistics and/or probability.
Hui Fang
Scientific Literature Mining
Grand Challenge: Engineer tools of scientific discovery
The project focuses on developing machine learning and artificial intelligence tools that can help domain experts to access and mine scientific literature, which can enable new discovery in important science domains such as chemical engineering.
Suggested coursework: good programing skills in C++/Java/Python, data structure, algorithm.
Steven Hegedus
Analysis of solar sensor and power data from agrivoltaic installation
Grand Challenge: Make solar energy economical
Agrivoltaics combines photovoltaics (PV) from solar modules with crops growing underneath. The solar modules’ shadow on the crops below creates a varying pattern of sun and shadow. This project will collect and analyze data from our agrivoltaic system. Data will include solar irradiance (sunlight) at many sensor locations above and below the rotating solar array as well as electrical power from the solar array. Students will organize the data, create meaningful graphs and analysis, and may also create a repository for the database or a web portal for viewing the archived data.
Suggested coursework: basic electronic device and circuits, managing data sets and making graphs in Excel or other apps.
David Hong
Matrix & tensor methods for scientific data
Grand Challenge: Engineer tools of scientific discovery
Modern scientific data is often big data and data science methods are needed to discover underlying low-dimensional phenomenon in the data. Matrix & tensor methods provide a promising approach. In this project, you will help develop these unsupervised machine learning methods to discover patterns in big data and contribute to an open-source package.
Suggested coursework: programming experience (Julia, Python, or Matlab), linear algebra, statistics and/or probability.
Nathan Lazarus
Liquid Metal Haptics for Disability Rehabilitation
Grand Challenge: Make solar energy economical
Virtual reality systems are an important emerging technology for assisting with rehabilitation after stroke and other neurological disorders, but providing haptic feedback comfortably over extended periods remains challenging with current technology. Our group has developed more comfortable stretchable haptic interfaces based on liquid metal vibrotactile actuators. In this project, I propose to use these stretchable haptics and stretchable sensors to make a wearable glove capable of detecting and providing feedback when the user diverges from a desired pattern of motion.
Suggested coursework: Experience in additive manufacturing, solidworks or sensors is a plus.
Mario Mencalgi
Metamaterial Analog Computing
Grand Challenge: Brain-inspired computing
Wave-based computing is gaining interest as a promising approach for resource-intensive, specialized processing tasks, with metamaterials (MTMs) and metasurfaces (MTSs) offering potential as fully analog computing platforms. Current MTMbased methods, however, face fabrication challenges and lack reconfigurability. This project aims to address these limitations
by developing a reconfigurable, cavity-based computing platform that simplifies fabrication and operates with minimal control signals, enabling efficient matrix-vector multiplications with waves. The project includes the design, fabrication, and testing of this innovative platform.
Suggested coursework: electromagnetics, wave-based analog computation, programming experience (Julia, Python, or Matlab).
Satwik Patnaik
Secure Hardware Design Using Generative Artificial Intelligence
Grand Challenge: Secure Cyberspace
Leveraging the power of generative AI, this research focuses on developing techniques to design and fortify hardware systems against hardware-based threats. This project is an exciting opportunity for students to engage in pioneering work at the intersection of AI and cybersecurity, contributing to developing next-generation secure hardware.
Suggested coursework: good programing skills in Python and C, digital logic and circuits.
Vishal Saxena
Device Characterization for Artificial Intelligence Chip Design
Grand Challenge: Reverse-engineer the brain
The student(s) will work on characterizing pre-fabricated devices on a chip for Artificial Intelligence hardware development. The students will learn to use a semiconductor probe station, source measurement unit (SMU), and oscilloscope. They will build experimental setups and perform electrical characterization of analog memory devices. Data collection and visualization will be automated using Python-based scripting.
Suggested coursework: Python or Matlab programming, circuits basics, Ohms law, Kirchoff’s laws.
Nektarios Tsoutsos
New Cybersecurity Tools and Datasets for Cybersecurity Education
Grand Challenge: Secure Cyberspace
Securing the cyberspace has evolved as one of the most complex engineering challenges we are facing today. To prepare the next generation of cybersecurity professionals, students in this project will be exposed to a variety of modern cybersecurity tools, develop security solutions, and analyze security vulnerabilities. Students will also develop algorithms, frameworks and datasets related to open cybersecurity challenges.
Suggested coursework: Python/C programming (assembly optional), computer networks, web programming (e.g., Javascript), operating systems (e.g., Linux), cybersecurity.