University of Maryland Honors Graduate Assistants from the Department of Computer Science

The University of Maryland announced the recipients of the Graduate School’s Outstanding Graduate Assistant Award for the 2023-24 academic year, recognizing seven teaching assistants and eight research assistants from the Department of Computer Science. The honor is awarded annually to graduate students who demonstrate exceptional contributions as administrative, research or teaching assistants.

The recipients of this year's award were Bang An, Yang Bai, Nolan Coble, Armin Gerami, Sreyan Ghosh, Sharath Girish, Priyatham Kattakinda, Nakyung Lee, Geng Liu, Alan Luo, Daniel Nichols, Nitya Raju, Suho Shin, Siddharth Singh and Laura Zheng.  

The Graduate School’s Outstanding Graduate Assistant Award was established to honor the vital role that graduate assistants play in supporting the university's academic environment. The awardees are selected based on their exemplary service and commitment to enhancing student learning and supporting faculty research.

Professor Ramani Duraiswami, the associate chair for graduate education in the Department of Computer Science, echoed this emphasis on graduate student excellence, underscoring the role of graduate assistants in advancing the computer science program. He attributed the department's Top 10 rankings for its undergraduate and graduate programs among public institutions in the United States to the efforts and achievements of these students. 

“The graduate office primarily focuses on recruiting top-tier students, maintaining their retention and steering them toward outstanding achievements,” Duraiswami said. “In essence, we serve as the human resource for the graduate program. It's important to acknowledge exceptional students, as they play a critical role in research and as teaching assistants in our undergraduate programs.”

Through this recognition, UMD underscores the importance of graduate assistants in maintaining a high quality of education and research.

“Initially, graduate students receive training from their advisors, but as they progress, they take the lead in research,” Duraiswami said. “As teaching assistants, they support office hours, grading and recitation sections, which is key in achieving student learning outcomes.”

UMD is home to over 4,000 graduate students, including approximately 460 within the department, who serve the campus community in various capacities. The selection of the 15 individuals places them in the top 2% of their peers, marking a significant achievement in their academic and professional journey.

“Graduate assistants are the cornerstone of our department's success in education and research,” said Computer Science Department Chair Matthias Zwicker, who holds the Elizabeth Iribe Chair for Innovation and the Phillip H. and Catherine C. Horvitz Professorship. “Their dedication and expertise play a crucial role in maintaining the high standards of our programs, and they are instrumental in fostering a dynamic and collaborative learning environment. We are immensely proud of their contributions and are committed to supporting their growth and development as future leaders in the field.”

The research focuses of the awardees are:

Bang An (Research Assistant): An is a Ph.D. student advised by Assistant Professor Furong Huang. Her research interests include various aspects of artificial intelligence and its applications. In her study "Transferring Fairness under Distribution Shifts via Fair Consistency Regularization," she investigated maintaining fairness in machine learning models under distribution shifts, a practical issue for fair model development. 

Yang Bai (Research Assistant): Bai is a Ph.D. student advised by Assistant Professor Nirupam Roy. Her research focuses on developing acoustic-based precise and low-power motion tracking, localization and navigation solutions for human and robotic systems. This includes innovations in spatial signal processing, sensory intelligence, embedded learning and power-aware computing. 

Nolan Coble (Teaching Assistant): Coble is a Ph.D. student advised by Electrical and Computer Engineering Professor Alexander Barg. His research interests are in quantum complexity theory and quantum error correction. He is especially interested in how the two fields intersect and inform one another.

Armin Gerami (Research Assistant): Gerami is a Ph.D. student advised by Professor Ramani Duraiswami with a research focus on high-performance computing. His work primarily involves exploring the foundational aspects of widely used deep learning systems. His approach is underpinned by his expertise in linear algebra and non-convex optimization. While acknowledging the success stories of neural networks popularized in the media, Gerami’s research is motivated to understand the core principles behind why these systems work and to identify their limitations.

Sreyan Ghosh (Research Assistant): Ghosh is a Ph.D. student advised by Distinguished University Professor Dinesh Manocha, who holds the Paul Chrisman Iribe Endowed E-Nnovate Professorship in Computer Science. Initially joining UMD as a master’s student in fall 2022, Ghosh transitioned to the Ph.D. program in spring 2024. His work at UMD's Gamma Lab focuses on various problems in speech, language and audio processing. Additionally, Ghosh frequently engages in applied research topics such as Room Impulse Response (RIR) estimation, audio generation, compositional reasoning and audio captioning.

Sharath Girish (Research Assistant): Girish is a Ph.D. student advised by Assistant Professor Abhinav Shrivastava. His research mainly focuses on accelerating and compressing deep networks. He is also interested in learning efficient and compact neural representations for data. Through deep network compression, he aims to democratize the use of large AI models on standard consumer devices.

Priyatham Kattakinda (Research Assistant): Kattakinda is an electrical and computer engineering Ph.D. student advised by Associate Professor Soheil Feizi. His research focuses on the distributional robustness of machine learning models and multimodal learning. Kattakinda aspires to develop tools to assist AI/ML practitioners in building reliable and safe ML models. He emphasizes the significance of this endeavor, noting the increasing impact of automated decisions made by these models on everyday life.

Nakyung Lee (Teaching Assistant): Lee is a Ph.D. student advised by Professor and UMIACS Director Mihai Pop. She is currently developing computational methods for detecting pathogens in metagenomic samples. Lee aims to create novel and efficient methods to improve the accuracy and speed of pathogen detection within the field.

Geng Liu (Teaching Assistant): Liu is a Ph.D. student and an active member of the Mind Lab and is advised by Professor Ashok Agrawala. His primary research interest is exploring how technology—specifically robotics, security and IoT devices—can enhance health and quality of life in everyday scenarios. With a focus on practical applications, his expertise is centered around operating systems, low-level programming and computer networking.

Alan Luo (Teaching Assistant): Luo is a Ph.D. student studying “usable security,” the intersection of human-computer interaction and security and privacy. Advised by Associate Professor Michelle Mazurek, his current research focuses on the security and privacy challenges faced by underserved and at-risk populations. Luo investigates how users evaluate and build trust in artificial intelligence (AI)-powered systems like large language models (LLMs). In the future, he hopes that his work can be used to make recommendations for policymakers and designers that help build inclusive, equitable AI and applications.

Daniel Nichols (Research Assistant): Nichols is a Ph.D. student working with the Parallel Software and Systems Group and advised by Associate Professor Abhinav Bhatele. His research interests lie at the intersection of high-performance computing and machine learning, where he focuses on applying machine learning to computer systems problems that arise in supercomputing. His work enables more efficient use of supercomputers through intelligent job scheduling and resource placement, large language model-guided performance optimizations and machine learning (ML)-driven performance modeling. Much of his research is done in collaboration with some of the largest supercomputing sites in the U.S., such as the Lawrence Livermore National Laboratory.

Nitya Raju (Teaching Assistant): Raju is a Ph.D. student advised by Distinguished University Professor Aravind Srinivasan. She studies computer science theory, focusing on problems related to probability, graph theory and algorithms.

Suho Shin (Teaching Assistant): Shin is a Ph.D. student advised by Professor Mohammad Hajiaghayi, who holds the Jack and Rita G. Minker Professorship in Computer Science. He is deeply interested in the interdisciplinary field of computer science and economics known as EconCS. His research encompasses various topics within this domain, including algorithmic game theory, mechanism design and their intersections with online algorithms, computational complexity and combinatorial optimization. Recently, Shin has been concentrating on algorithmic mechanism design, mainly focusing on the delegated choice problem, examined from combinatorial and learning perspectives.

Siddharth Singh (Research Assistant): Singh is a Ph.D. student advised by Associate Professor Abhinav Bhatele. His research primarily focuses on the practical aspects of distributed training and inference for large neural networks. 

Laura Zheng (Research Assistant): Zheng is a Ph.D. student advised by Distinguished University Professor Ming Lin, who holds the Dr. Barry L. Mersky Professorship and a Capital One Professorship. Zheng’s research interests lie in traffic simulation and autonomous driving. She wants to make autonomous driving technology safer, interpretable and centered around humans.

—Story by Samuel Malede Zewdu, CS Communications