Postdocs

Mitchell Gordon is a postdoc working with professors Jeff Heer and Yejin Choi. He designs interactive systems and evaluation approaches that bridge principles of human-computer interaction with the realities of machine learning. He recently completed his Ph.D. in computer science at Stanford, advised by Michael Bernstein and James Landay. He will join MIT EECS/CSAIL as an assistant professor starting fall 2024.

Markus Grotz currently works with Dieter Fox in the UW Robotics and State Estimation Lab and Tamim Asfour in the High Performance Humanoid Technologies Lab (H2T). His research focuses on visual perception for robotic manipulation tasks.

Vinayak Gupta works with Prof. Tim Althoff in the Behavioral Data Science Lab. His research interests broadly lie in the intersection of data mining and machine learning. Specifically, he focuses on neural generative models, including graph-based and temporal point processes, and applies them to domains such as healthcare, behavioral streams, and recommendation systems.

Tianxing He works with Yulia Tsvetkov on natural language generation. He works towards a better understanding of how the current large language models work. Related, he is interested in monitoring and detecting different behaviors of language models under different scenarios, and approaches to fix undesirable behaviors.

Rohan Kadekodi works with professor Baris Kasikci in the Allen School on the design of tiered memory and storage systems for data centers with heterogenous hardware. Kadekodi's previous worked focused on building file and distributed systems aimed at accelerating legacy and new applications on byte addressable storage.

Taylor Kessler Faulkner works with professor Siddhartha Srinivasa in the Personal Robotics Lab. Her research interests include human-robot interaction and AI, and her recent work is on developing interactive algorithms for robots learning from non-expert human teachers. Taylor received her Ph.D. in Computer Science from The University of Texas at Austin.

Andrew McNutt is a postdoc working with Allen School professors Jeff Heer and Leilani Battle. His research interests include information visualization (including its theories and practices) as well as the design of programming interfaces (such as editors and DSLs). In Fall 2024 he will be joining the Kahlert School of Computing / Scientific Computing and Imaging Institute at the University of Utah as an assistant professor.

Fatemehsadat Mireshghallah works with Allen School professors Yejin Choi and Yulia Tsvetkov. Her research interests are Trustworthy Machine Learning and Natural Language Processing. She is a recipient of the National Center for Women & IT (NCWIT) Collegiate award in 2020, a finalist of the Qualcomm Innovation Fellowship in 2021 and a recipient of the 2022 Rising star in Adversarial ML award. She received her Ph.D. from the CSE department of UC San Diego in 2023.

Keisuke Motone works with research professor Jeff Nivala in the Molecular Information Systems Lab (MISL). His research focuses on developing chemical and computational approaches to decoding biological information stored within protein and peptide sequences with nanopore sensor technology. Motone completed his Ph.D. in Applied Life Sciences at Kyoto University in Japan.

Jonggyu Park is a postdoctoral scholar working with Allen School professors Tom Anderson and Simon Peter. Park's research interests include operating systems, storage, and data center infrastructure. In the past, Park has concentrated on developing high-performance computer systems with a particular focus on fairness, tailored specifically for consolidated environments. Presently, Park is dedicated to the design of new, energy-efficient, and carbon-conscious data center infrastructure.

I am excited about projects where engineering solutions meet medical needs, specifically those that enable individuals with disabilities interact with the world around them in a more inclusive environment. In the past, I have worked on developing affordable and customizable orthotic devices for individuals with spinal cord injuries and attempted to simplify control methods for complex prosthetic hands. As a postdoc at UW, I hope to harness the advancements in metamaterials and smart textiles to create custom solutions for assistance and rehabilitation needs of individuals with disabilities.

I am interested in applications of Deep Learning and Machine Learning in Genetics. We are trying to learn gene regulatory models from large-scale sequencing data to characterize the genetic sequences that determine molecular phenotypes. We focus on model interpretation to gain a better understanding of the underlying biological mechanisms that control changes in gene expression, and to identify the main trans-acting factors that are involved in these regulatory processes, such as transcription factors and RNA-binding proteins.

Min Jae Song is a postdoctoral scholar at UW, working with Allen School professors Rachel Lin and Jamie Morgenstern. Min Jae's research interests lie at the intersection of theoretical computer science and machine learning, with a recent focus on establishing algorithmic fairness using tools from theoretical computer science. He obtained his Ph.D. in computer science from New York University, under the supervision of Oded Regev and Joan Bruna, where his research focused on the computational complexity of statistical inference.

Max Willsey earned his Ph.D. at the Allen School and as a postdoc works mostly in programming languages (PLSE group) with Zachary Tatlock but also collaborates with friends in molecular systems (MISL), and machine learning systems (SAMPL). He is currently working on egg, a toolkit for program optimization and synthesis powered by e-graphs and equality saturation.

Xinyi Zhou is a postdoctoral scholar working with Allen School professors Tim Althoff and Amy Zhang. Her research interests are broadly in the intersection of data mining, machine learning, and social computing. Her research has and will continue striving to bridge the gap between social theories and (multimodal) ML/AI techniques to comprehend and improve the online information ecosystem that can breed misleading, untrusted, biased, threatening, insecure, and distracting messages.