Dong Deng: Professor Deng works on data management and database systems, with a focus on vector databases, text management, and data curation.
Yipeng Huang: Professor Huang works on quantum and other unconventional computer architectures. Developing specialized hardware tools that help realize useful quantum computing.
Sudarsun Kannan: Prof. Sudarsun Kannan works on Operating Systems (OS) and their implications on Computer Architecture, Distributed Systems, and High-performance Computing systems. His research group is currently building systems for efficiently managing compute, memory and storage heterogeneity.
Uli Kremer: Professor Kremer works on compilers, embedded systems, CPS, automatic parallelization, parallel programming, power and energy management and optimization, underwater robotics, approximation management.
Amélie Marian: Professor Marian woks on Data Management and Algorithms, Accountability and Transparency of Algorithms, Explainable Rankings, Personal Information Management, Data Integration, and Data Corroboration.
Richard P Martin: Professor Martin works on Low Latency Networking, Machine Learning for Classification of Packet Flows, Using Machine Learning for Signal Detection.
Santosh Nagarakatte: Professor Nagarakatte works on building efficient, robust, and secure software systems. Primary research interests are in hardware-software interfaces spanning programming languages, compilers, runtimes and computer architecture. He enjoys working on the entire computing stack ranging from hardware modifications, operating system enhancements, compiler optimizations and programming language design to build robust systems.
Thu D. Nguyen: Professor Nguyen is generally interested in the design, implementation, and evaluation of efficient and sustainable computing systems. His research group is currently exploring the application of Deep Reinforcement Learning to efficiently manage “green” data centers. He is also exploring how to improve CS education and advance DEI in STEM.
Minesh Patel: Professor Patel takes an analytical approach to designing robust computer architectures and systems. His work combines new hardware-software mechanisms, test methodologies, and analysis techniques to address robustness challenges with a focus on the intersection of different system components.
He Zhu: Professor Zhu’s research focuses on neurosymbolic programming, which lies at the intersection of automated programming and deep learning and spans over programming languages, formal methods, and machine learning. He investigates program synthesis and formal program reasoning techniques to make machine learning systems more reliable and trustworthy.