Three CSE grad students recognized by NSF Graduate Research Fellowship Program
The NSF Graduate Research Fellowship Program has recognized three CSE PhD students for their promising research in a variety of disciplines. Neal Mangaokar was awarded a fellowship, and Noah Curran and Serafina Kamp were each 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.
Neal’s research interests revolve around the security, privacy, and fairness implications of machine-learning. Most recently, he’s worked on better understanding deepfake images and videos, and improving the state-of-the-art for deepfake detection in adversarial settings. He also works on improving the adversarial robustness of machine-learning models, both in the image and text domains. Neal is advised by Prof. Atul Prakash.
Noah Curran is a member of the Real-Time Computing Lab. His research efforts are focused on hardening the security and reliability of semi-autonomous vehicular systems. This area involves the detection of incorrect sensor readings (e.g., via physical damage or adversarial falsification) or human control input (e.g., inattentive driving or malicious intention) in order to ensure the safest decision is made when the vehicle is controlled. His recent work has been on using smartphone sensors as a source of redundancy for estimating vehicle sensor values and detecting anomalies among reported vehicle sensor readings. He has also recently started on a project that aims to determine which sources of information to trust for vehicle decision-making. Noah is advised by Prof. Kang G. Shin.
Serafina will be starting a PhD next year at the University of Michigan in CSE. Her research will focus on machine learning, with particular interest in questions about fairness in machine learning. She aims to explore how we can measure ML fairness and ensure that algorithms in general are “fair.” Serafina is advised by Prof. Benjamin Fish.