معرفی
Amol Deshpande is a Professor and Associate Chair for Graduate Education in the Department of Computer Science at the University of Maryland, College Park, with a joint appointment in UMIACS. He holds a B.Tech. from IIT Bombay (1998), M.S. and Ph.D. from UC Berkeley (2001, 2004). His research focuses on databases, big data, machine learning, graph analytics, and privacy-aware systems. He has authored over 80 publications, including influential works in VLDB, SIGMOD, and CIDR. Awards include the NSF CAREER Award (2006) and CIDR 2025 Test of Time Award. He teaches advanced database courses and advises students in data science and systems.
Research Interests: Graph data management, probabilistic databases, privacy-first systems, query optimization, sensor networks, and scalable analytics. Current projects include declarative graph processing, privacy-aware data systems, and collaborative data science platforms like DataHub.
Recent Contributions: His work bridges database systems with modern challenges in scalable analytics, including graph processing, uncertainty management, and ethical data practices. Notable systems include TelegraphCQ (2003), PrDB (probabilistic databases), and NScale (graph analytics framework).
Awards & Service: Served as PC Chair for SIGMOD 2019, editorial roles for VLDB Journal and ACM TODS. NSF PI for multiple grants, including BIGDATA initiatives. Active in organizing conferences and workshops on data systems and uncertainty management.
