Ashish Venkat

Ashish Venkat is an Associate Professor in the Department of Computer Science at the University of Virginia, part of the School of Engineering and Applied Science. He holds a Ph.D. from UC San Diego and has established himself as a leading researcher in computer architecture, compilers, and computer security.

Research Interests:

His research focuses on cross-disciplinary hardware and software techniques to build secure, high-performance computing systems. He investigates robust exploit mitigations that maintain energy efficiency and programmability, with a particular emphasis on speculative execution, memory safety, hardware security, and privacy-preserving computing. He also explores the application of machine learning to detect security threats and model execution behavior.

Publication Trends:

His recent publications (2020–2025) demonstrate a strong focus on hardware-based security, particularly microarchitectural vulnerabilities (e.g., micro-op cache attacks), memory safety via microcode capabilities, and secure accelerators for bioinformatics. There is a consistent trend of publishing in top-tier venues like ISCA, MICRO, IEEE S&P, and USENIX Security, often featuring novel hardware/software co-design solutions.

Scientific Awards:

• NSF CAREER Award (2023)
• NSF CRII Award (2018)
• IEEE Micro Top Pick (2019)
• IEEE Design & Test Top Pick (2020, 2021)
• HPCA Best Paper Runner-Up (2019)
• DATE Best Paper Nominee (2023)
• UVA Research Achievement Award (2023)
• ISCA Prolific Author of the Decade (2013–2022)

Advising and Grants:

He actively mentors graduate and undergraduate students, many of whom have pursued advanced degrees or joined leading tech companies. He has secured significant funding as PI or co-PI from NSF, DARPA, SRC, and Intel, including a $4.9M DARPA HERCULES grant and an NSF CAREER award. His projects focus on holistic security solutions, speculative optimization, and privacy-preserving machine learning frameworks.

Labs and Teams:

He leads a research group focused on secure and efficient computing systems, collaborating with researchers at institutions like UC San Diego, UC Riverside, and UC Irvine, as well as industry partners including Intel and IBM.