New Tenure-Track Faculty to Join Department of Computer Science

The University of Maryland’s Department of Computer Science is set to welcome a cohort of eight new tenure-track faculty. The faculty members, with research expertise spanning a wide range of cutting-edge areas, are expected to enhance the department’s academic and research capabilities. 

"We are excited to welcome our newest faculty members," said Department Chair Matthias Zwicker, who holds the Elizabeth Iribe Chair for Innovation and the Phillip H. and Catherine C. Horvitz Professorship. "Their perspectives will drive forward impactful research and raise our level of excellence for years to come. We are thrilled to support them in their work as they join our department!"

Ruohan Gao

Ruohan Gao’s research focuses on computer vision and machine learning, with an emphasis on multisensory learning involving sight, sound and touch. The overarching goal of Gao's work is to empower machines to emulate and enhance human capabilities in perceiving and engaging with the multisensory world. He received a Ph.D. in Computer Science from the University of Texas at Austin. Following this, Gao completed a postdoctoral fellowship at Stanford Vision and Learning Lab.

Gao has been recognized with several honors, including the Best Paper Award Runner-Up at the British Machine Vision Conference (BMVC) in 2021 and the Stanford Artificial Intelligence Lab (SAIL) Postdoctoral Fellowship (2021-2023).

He will join the department as an assistant professor in January 2025. He will lead UMD's Multisensory Machine Intelligence Group.

Mohit Iyyer

Mohit Iyyer’s research interests lie broadly in natural language processing and machine learning. Iyyer is focused on improving the instruction-following abilities of large language models for long-form generation, designing methods to evaluate long-form and multilingual text, building collaborative human-LLM systems to assist human authors in creative writing tasks and increasing the robustness of LLM-generated text detectors to attacks. Iyyer earned a Ph.D. in Computer Science from the University of Maryland College Park in 2017, where he was a member of the Computational Linguistics and Information Processing (CLIP) Lab. He was also a postdoctoral researcher at the Allen Institute for Artificial Intelligence and later a faculty member at the University of Massachusetts, Amherst.

He is the recipient of best paper awards at NAACL (2016, 2018), an outstanding paper award at EACL 2023 and a best demo award at NeurIPS 2015. He also received the 2022 Samsung AI Researcher of the Year award.

Iyyer will join the department as an associate professor in January 2025.

Gabe Kaptchuk

Gabe Kaptchuk’s research specializes in cryptography and privacy, and he was a part of the research faculty at Boston University. Kaptchuk earned a Ph.D. in Computer Science from Johns Hopkins University. 

Kaptchuk has received several honors and awards, including the NSF Convergence Accelerator Track G award for "Secure Censor-resistant Overlay Resilient Networks" (2022–2023), the DARPA Young Faculty Award for "Guarding Against User Misperceptions of Differential Privacy" (2021–2024) and the NSF & CRA Computing Innovation Fellowship (2020–2023).

He will join the department as an assistant professor in August 2024. He will also be affiliated with the Maryland Cybersecurity Center (MC2) and the University of Maryland Institute for Advanced Computer Studies (UMIACS).

Binghui Peng

Binghui Peng’s research interests include theoretical computer science and machine learning. He received a Ph.D. in Computer Science from Columbia University and his bachelor's degree from Tsinghua University in the prestigious Yao Class. Peng researches the theory of computation and develops algorithms and complexity theory for machine learning, artificial intelligence and game theory.

His work addresses long-standing questions in learning theory and game theory and has been published in top theory conferences such as STOC, FOCS and SODA, where he received the Best Student Paper Award, as well as in leading ML conferences like NeurIPS, ICLR and ACL.

Peng will join the department as an assistant professor in the summer of 2025.

Han Shao

Han Shao’s research centers on machine learning theory, with a specific focus on modeling human strategic and adversarial behaviors within the learning process. Shao aims to understand how these behaviors affect machine learning systems and develop methods to enhance accuracy and robustness. Additionally, Shao is interested in gaining a theoretical understanding of empirical observations concerning adversarial robustness. Shao earned a Ph.D. in Computer Science from the Toyota Technological Institute in Chicago.

Her papers have been published at machine learning venues including NeurIPS, ICML and COLT. She was awarded EECS Rising Star by Georgia Tech and Rising Star in Machine Learning by the University of Maryland in 2023. 

Shao will join the department as an assistant professor in the summer of 2025.

Runzhou Tao

Runzhou Tao specializes in the intersection of programming languages, operating systems and quantum computing, with a focus on building trustworthy and efficient quantum computing system software. Tao earned a Ph.D. in Computer Science from Columbia University and completed a bachelor's degree from Tsinghua University, where he conducted research on theoretical computer science.

His contributions have been recognized with best paper awards from prestigious conferences, including FOCS. He won the Jay Lepreau Best Paper Award at the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2021).

Tao will join the department as an assistant professor in August 2024.

Fumeng Yang

Fumeng Yang’s research explores data-driven computational models, focusing on how individuals and groups perceive, use and develop these models. Yang’s work spans probabilistic forecasts and AI techniques such as foundation models. She is a researcher in Human-Computer Interaction (HCI) and Visualization. She earned a Ph.D. in Computer Science from Brown University, an M.Sc. from Tufts University and a B.Eng. from Shandong University.

Her research has been recognized with a best paper award at IEEE VIS and three honorable mention awards at ACM CHI, IEEE VIS and ACM IUI.

Yang will join the department as an assistant professor in August 2024.

Yaodong Yu

Yaodong Yu’s research interests are in the theoretical foundations and applications of trustworthy machine learning. Yu’s work includes interpretable white-box deep neural networks, differentially private foundation models, optimization and uncertainty quantification for collaborative learning and robustness under distribution shifts. Yu earned a Ph.D. in Computer Science from UC Berkeley. His academic background also includes a B.S. from Nanjing University and an M.S. from the University of Virginia.

He is the recipient of the CPAL-2024 Rising Star Award and first place in the NeurIPS-2018 Adversarial Vision Challenge.

Yu will join the department as an assistant professor in the summer of 2025. 

—Story by Samuel Malede Zewdu, CS Communications