Trio of ECE Graduate Students Honored by University of Maryland

Pasan Dissanayake, Mingju Liu, and Sydney Overton have each received a University of Maryland 2025 Outstanding Graduate Assistant Award. This award recognizes the outstanding contributions that graduate assistants provide to students, faculty, departments, administrative units, and the university as a whole. The Graduate School awards approximately 80 Outstanding Graduate Assistant Awards annually, which represents roughly the top 2% of campus GA's in a given year.

Mingju Liu

Second-year Ph.D. student Mingju Liu focuses his research on hardware-software co-design of deep learning algorithms to address challenges in Electronic Design Automation (EDA). Addressing these challenges involves developing reinforcement learning based algorithms used to finetune the existing ASIC technology mapping flow used in both open-source and commercial synthesis tools and the dataless differentiable algorithms that can be expanded to solve classic combinatorial optimization problems and addressing the limitations, such as scalability issues of state-of-the-art open-source and commercial optimizers. He is supervised by Professor Cunxi Yu.

Liu joined the ECE Department in the fall of 2023 after receiving his MS degree from Rutgers University and a B.S. from the University of Electronic Science and Technology of China. He was selected for the Association for Computing Machinery/IEEE Design Automation Conference (ACM/IEEE DAC) 2023 Young Fellows Program in 2023, and received the International Conference on Computer-Aided Design (ICCAD) Student Scholar Travel Grant in 2024.

“I am deeply honored to be selected as a recipient of the Outstanding Graduate Assistant Award. This recognition is incredibly meaningful to me, as it reflects the graduate school’s acknowledgment of my contributions to the community”, says Liu. “As an early-stage graduate student in the ECE department, I feel privileged to represent the department through this award. I am grateful for the department’s support and for fostering a motivating environment that encourages me to “fearlessly forward.”

"Watching Mingju grow as a PhD student has been truly inspiring”, says Professor Cunxi Yu, “The work he published last year has already made an impact in industry, and I have no doubt Mingju will continue to make significant contributions in their field.”

In the future, Liu explains that he plans to “continue contributing to the EDA field by introducing innovative methodologies to address challenges posed by current approaches, especially in the industry field. I aim to provide the community with high-performance and intelligent solutions that advance the state of the art.

Sydney Overton

Sydney Overton, a third-year Ph.D. student in electrical engineering, was selected for the University of Maryland Graduate School’s Outstanding Graduate Assistant Award.

“It’s always nice to be recognized for my hard work,” Overton said. “I’ve been fortunate to have mentors who provide counsel and advocate for me at the department, college, and university levels. This award is incredibly rewarding and speaks to my dedication to both teaching and research as a graduate student. Every graduate student I know is committed to their teaching, research, and coursework, so to be recognized among so many incredible graduate assistants is humbling. I’m very grateful.”

Overton conducts research in the MEMS Sensors and Actuators lab (MSAL) under Herbert Rabin Distinguished Chair in Engineering and Fischell Institute Fellow Reza Ghodssi.

At MSAL, Overton is designing an ingestible capsule that deploys sensors to measure gut serotonin concentrations. Her work centers on the gut-brain axis (GBA) and the development of tools to better understand specific pathways and biomarker dynamics. By taking a systems integration approach, she aims to create a new study platform to further research on the GBA—an effort that aligns with her longstanding interest in engineering solutions to medical challenges.

“I’m particularly interested in this research because of the physiological implications of the GBA, which plays a role in gut and neurological diseases,” Overton said. “The goal of my research is to help unravel some of the specific pathways within the GBA to improve our understanding of these diseases and potential treatments.”

Overton pursued a Ph.D. in electrical engineering to gain research experience and the credentials needed to lead a lab of her own. She earned her bachelor's degree in electrical engineering from Virginia Tech in 2019.

“When I was looking at Ph.D. programs, I knew I wanted to bridge the gap between electrical engineering and medical research,” Overton said. “The university’s A. James Clark School of Engineering offered me the research opportunities I was looking for. It also didn’t hurt that the Clark School is one of the top engineering graduate programs, and I knew my experiences here would open doors for my future.”

“Sydney exemplifies the best attributes that we, the faculty, always wish for our doctoral students,” said Ghodssi. “She is super intelligent and creative, a passionate researcher and educator, a community leader, and an effective mentor to the students she works with. It is indeed a privilege to work with her.”

Overton plans to publish her next research paper and officially advance to Ph.D. candidacy after proposing her thesis research this spring. She also aims to publish additional papers before graduating, while leaving a legacy of mentorship for undergraduate researchers at MSAL and through her teaching. After earning her doctorate, she hopes to secure a postdoctoral research position and eventually a faculty role.

Pasan Dissanayake

Ph.D. student, Pasan Dissanayake, concentrates his research on explainability in machine learning and AI. He is currently in his third year with the ECE department. Prior to joining UMD, he earned his BSc in Electronic and Telecommunication Engineering from the University of Moratuwa, Sri Lanka.

Developing efficient, interpretable, and trustworthy machine learning models is the main focus of Dissanyake’s research. Using mathematical techniques from optimization, statistics, and information theory, he aims to reconstruct simpler and more efficient models from complex ones while integrating explainability. Pasan Dissanayake is advised by Professor Sanghamitra Dutta who leads the Foundations of Reliable Machine Learning (FORMAL) research group at UMD ECE.

“The increasing complexity of machine learning has driven remarkable advancements in various domains including high-stakes applications. However, this unprecedented surge poses challenges for scalability and trust, creating an urgent need for efficient and interpretable models that maintain performance under resource constraints. Pasan's research addresses this challenge by developing rigorous strategies for model reconstruction from larger complex models to improve trust and transparency in AI systems. Since explainability techniques uncover how trained machine learning models make decisions, Pasan’s strategies lead to more efficient model compression by leveraging the underlying pathways in the decision-making process,” says Dutta as she celebrates this proud advisor moment.

Dissanayake’s research has been accepted and published in a variety of academic conferences, including the Conference on Neural Information Processing Systems (NeurIPS), the Conference on Artificial Intelligence and Statistics (AISTATS), the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), as well as, the IEEE Transactions on Information Theory, a peer-reviewed scientific journal well-known for its mathematical rigor. In addition, he is a passionate advocate for contributing to the academic community through mentoring, teaching, plus volunteering with high school students. He has been a student lead at the 2024 TRAILS AI Summer Academy, developing an experiential learning project for high school students titled, “Peeking Inside the Opaque Box for Explainability: From Neural Networks to LLMs.”

“Receiving the Outstanding Graduate Assistant award is a great honor, recognizing my contributions to both research and education,” says Dissanayake.

Continuing to advance the field of efficient and trustworthy AI, likely in the field of academia, is the goal of Dissanayake: “I aim to develop novel machine learning methodologies that enhance model interpretability, privacy, and efficiency while addressing real-world challenges”.

Published March 27, 2025