Aravind Srinivasan

Aravind Srinivasan is a Distinguished University Professor of computer science at the University of Maryland, College Park, with simultaneous appointments in the Department of Computer Science, UMIACS (University of Maryland Institute for Advanced Computer Studies), and AMSC. His research focuses on algorithms particularly randomized and probabilistic methods and their applications in fields ranging from data science, health, and algorithmic fairness to networks, cloud computing, and sustainable systems.

Aravind Srinivasan
Born	1968 (age 56–57) India
Nationality	American
Alma mater	Indian Institute of Technology Madras Cornell University
Known for	Randomized algorithms, algorithmic fairness, networks, computational epidemiology
Awards	AMS Fellow (2019) ACM Fellow (2014) IEEE Fellow (2010) Dijkstra Prize Danny Lewin Award
Scientific career
Fields	Computer science, algorithms, probabilistic methods, data science
Institutions	University of Maryland, College Park
Doctoral advisor	David Shmoys
Website	https://www.cs.umd.edu/~srin/

Education and academic career

Srinivasan earned his B.Tech. from Indian Institute of Technology Madras (1989) and his M.S. and Ph.D. in Computer Science from Cornell University (1993). He conducted postdoctoral research at the Institute for Advanced Study (Princeton) and DIMACS at Rutgers, worked at the National University of Singapore and in industrial research at Bell Labs, and joined the University of Maryland faculty in the early 2000s.^[1]

Research

His research spans randomized and probabilistic algorithms,^[2] combinatorial and continuous optimization, and applications in computational epidemiology,^[3] genomics, Internet economy, social networks, energy systems, and fairness in AI.^[4]

He has held editorial leadership roles, serving as Editor‑in‑Chief of ACM Transactions on Algorithms (2014–2020),^[5] Editor of Theory of Computing (2006–2019),^[6] Editor of Journal of Discrete Algorithms (Elsevier) (2004–2012) and as an Editor for the Journal of the IISc^[7]

Honors and awards

• Fellows of ACM (2014),^[8] IEEE (2010),^[9] AAAS (2012),^[10] AMS (2019),^[11] EATCS (2017)^[12] and SIAM (2020)^[13]
• Distinguished University Professor, University of Maryland (2020)^[14]
• Distinguished Alumnus Award from IIT Madras (2016)^[1]
• Recipient of the Dijkstra Prize and Danny Lewin Award^[15]
• Distinguished Career Award in Computer Science from the Washington Academy of Sciences (2021)^[14][16]

Professional service

He served as Vice Chair of the IEEE Technical Committee on Mathematical Foundations of Computing (2015–2017), has held visiting positions varioys institutions, including Amazon Scholar since 2019.^[17]

Selected Bibliography

Articles

• Eubank, Stephen; Guclu, Hasan; Anil Kumar, V. S.; Marathe, Madhav V.; Srinivasan, Aravind; Toroczkai, Zoltán; Wang, Nan (13 May 2004). "Modelling disease outbreaks in realistic urban social networks". Nature. 429 (6988): 180–184. Bibcode:2004Natur.429..180E. doi:10.1038/nature02541. PMID 15141212.
• Han, Bo; Hui, Pan; Kumar, V.S. Anil; Marathe, Madhav V.; Shao, Jianhua; Srinivasan, Aravind (May 2012). "Mobile Data Offloading through Opportunistic Communications and Social Participation". IEEE Transactions on Mobile Computing. 11 (5): 821–834. Bibcode:2012ITMC...11..821H. doi:10.1109/TMC.2011.101.
• Schmidt, Jeanette P.; Siegel, Alan; Srinivasan, Aravind (May 1995). "Chernoff–Hoeffding Bounds for Applications with Limited Independence". SIAM Journal on Discrete Mathematics. 8 (2): 223–250. doi:10.1137/S089548019223872X.
• Srinivasan, A.; Ham, T.; Malik, S.; Brayton, R.K. (1990). "Algorithms for discrete function manipulation". 1990 IEEE International Conference on Computer-Aided Design. Digest of Technical Papers. pp. 92–95. doi:10.1109/ICCAD.1990.129849. ISBN 0-8186-2055-2.
• Naor, M.; Schulman, L.J.; Srinivasan, A. (1995). "Splitters and near-optimal derandomization". Proceedings of IEEE 36th Annual Foundations of Computer Science. pp. 182–191. doi:10.1109/SFCS.1995.492475. ISBN 0-8186-7183-1.

Patents

• Systems and methods for mapping a term to a vector representation in a semantic space.^[18]
• Allocation of workloads in dynamic worker fleet.^[19]
• Fast and scalable approximation methods for finding minimum cost flows with shared recovery strategies, and system using same.^[20]