Atri Rudra

Atri Rudra is the Katherine Johnson Chair in Artificial Intelligence and Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. He received his B.Tech from IIT Kharagpur in 2000 and his PhD from the University of Washington in 2007.

Education:

• PhD, Computer Science and Engineering, University of Washington, 2007
• MS, Computer Science and Engineering, University of Washington, 2005
• B Tech, Computer Science and Engineering, Indian Institute of Technology, India, 2000

Rudra's research spans Theoretical Computer Science with specific interests in structured linear algebra (with applications in machine learning), database algorithms, theory of error-correcting codes, and algorithms and society. He is a member of both the Algorithms and Theory group and the Computing for Social Good group at UB. His work bridges fundamental theoretical questions with practical societal implications, particularly in how computing affects society.

His publications reflect a strong focus on coding theory, as evidenced by his ongoing work on the book Essential Coding Theory with Venkatesan Guruswami and Madhu Sudan. His recent work shows an increasing interest in the intersection of machine learning and society, with courses like ML and Society reflecting this evolving research direction. His publications span theoretical foundations of coding theory to practical applications in database algorithms and societal implications of computing.

Awards and Recognition:

• SUNY Chancellor's Award for Excellence in Teaching (2022)
• NSF CAREER Award (2009)
• Best Paper awards at PODS (2012) and ESA (2010)
• William Chan Memorial Dissertation Award (2007)
• IBM Faculty Award (2013)

Rudra has advised numerous PhD students who have gone on to careers in academia and industry. His current research is supported by NSF grants IIS-1956149 and CCF-2247014. He has been actively involved in service to the community as an editor for IEEE Transactions on Information Theory and Theory of Computing Systems, and as program committee member for major conferences including PODS, SODA, and ITCS.

He leads the EaGL Theory Computation Workshop series and co-organizes workshops on Multiple Approaches for Teaching Responsible Computing and Algorithmic Opportunities in the Modern LLM Revolution, reflecting his commitment to both theoretical foundations and societal impact of computing.