ECE graduate students recognized by 2022 NSF Graduate Research Fellowship program

The NSF Graduate Research Fellowship Program has recognized three ECE PhD students for their promising research in a variety of disciplines. Austin Lin and Rachel Newton were each awarded a fellowship, and Evelyn Ware was recognized with an honorable mention.

This prestigious program recognizes and supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines who are pursuing research-based masters and doctoral degrees at accredited United States institutions. It is accompanied by three years of significant financial support.

Austin Lin

Austin Lin’s research interests lie at the intersection of power electronics and the power grid, specifically how we can use power electronics to create a sustainable, more powerful power grid by making maximum use of new and used batteries.

“I plan on co-designing the power electronic and auxiliary systems of grid scale battery energy storage systems to optimally implement battery storage onto the grid,” said Lin. “This will improve grid reliability, especially as the grid shifts to more renewable sources. I see this being specifically useful for implementing second life batteries (those recycled from old electric vehicles) to the grid.”

Lin added that battery storage will increase the overall efficiency of generators and the reliability of renewables. This will reduce the carbon emission of electrical generation, while also decreasing battery waste. Lin is advised by Professors Al Avestruz and Johanna Mathieu.

Lin received his bachelor’s degree from Northeastern University, with a dual degree in Electrical Engineering and Physics. He gained experience with internships at Jacobs Engineering and Lutron Electronics. After four years on the track team at Northeastern, during which time he was named a CoSIDA Academic All-American, Lin was eligible for an additional year on Michigan’s NCAA team. He is realizing his dream of being a scientist, confirmed at the age of eight when he changed his contact in his mother’s phone to “Dr. Austin Lin PhD.”

Rachel Newton

Rachel Newton’s research lies in machine learning for control systems. She is focusing on the problem of nonconvex optimization in relation to data science applications, and specifically to cooperative wind farm control.

“A direct impact of this work is the potential improvement in energy production efficiency for large-scale wind farms,” said Newton. “Better low-dimensional approximations can lead to better control law design and overall better run wind farms. This has a broad impact in terms of sustainable energy stability and long-term mitigation of climate change.”

Other applications include improving the fuel efficiency and power flow in aeroservoelastic systems. She is advised by Professors Laura Balzano and Peter Seiler.

Newton received her bachelor’s degree with honors at Arizona State University, where she was a co-leader of the electrical sub-team for the interdisciplinary student team, Sun Devil Motorsports-Formula SAE. She also conducted research in the area of wireless communication and signal processing, where the goal was a wearable respiratory sensor. She rounded out her education with internships at Viasat. The Boeing Company, and Sandia National Laboratories. Newton was inducted into HKN and Tau Beta Pi, and selected as the 2020 Electrical, Energy, and Computer Engineering Outstanding Graduate. In addition to her studies, she was a student ambassador, and participated in several STEM outreach activities, including Letters to a Pre-Scientist.

Evelyn Ware

Evelyn Ware’s research explores how we can use machine learning algorithms to calibrate analog-to-digital converters. ADCs make it possible to take in analog information and then process it digitally, which is required in such disparate applications as medical imaging, 5G networks, GPS, radar and communication systems, and sensor interfaces. Calibration is often necessary to improve performance.

“Artificial intelligence and deep neural networks provide the next frontier for ADC architecture and calibration,” said Ware. “Using DNNs for calibration of ADCs has been explored in simulation but there is a striking lack of prior literature surrounding practical implementations of DNN-ADC calibration schemes on chip, which is essential for their use in real-world systems.”

Ware presented her work at the SRC TECHCON conference in 2021. She is advised by Prof. Michael Flynn.

Ware received her bachelor’s degree from Purdue University with a dual degree in Electrical Engineering and Mathematics. She participated in internships at Pentair (now nVent), Delphi Technologies, and Sandia National Laboratories. She is passionate about inspiring younger students to pursue a career in electrical engineering. She has already begun that work as an award-winning teaching assistant at Purdue, where she also tutored students in 10 different courses.