Aravind Srinivasan

Aravind Srinivasan is an Indian-American computer scientist known for contributions to randomized algorithms, approximation algorithms, and combinatorial optimization. He has held research and academic positions in prominent institutions and collaborated with leading researchers across theoretical computer science, probability, and discrete mathematics. His work has influenced research on complexity theory, algorithm design, and applications in computer science and operations research.

Early life and education

Srinivasan completed his undergraduate studies at Indian Institute of Technology Madras and pursued graduate studies at the University of California, Berkeley where he earned a Ph.D. under advisors in theoretical computer science. During his formative years he interacted with researchers associated with Association for Computing Machinery, IEEE, Neal Koblitz-era cryptography seminars, and seminars influenced by work at Stanford University and Massachusetts Institute of Technology. His education connected him to networks spanning Microsoft Research, Bell Labs, IBM Research, and prominent academic groups in Princeton University and Harvard University.

Academic career

Srinivasan has held faculty and research positions at institutions including University of Maryland, College Park and research labs such as Microsoft Research. He collaborated with faculty from Columbia University, Cornell University, University of California, Berkeley, and visiting scholars from University of Washington and Carnegie Mellon University. His teaching and mentoring intersected with graduate programs linked to National Science Foundation fellowships, Simons Foundation grants, and collaborative projects with researchers at Tel Aviv University and Weizmann Institute of Science. He served on program committees for conferences including STOC, FOCS, SODA, and ICALP and participated in editorial roles for journals connected to SIAM and IEEE.

Research contributions

Srinivasan's research spans randomized rounding, derandomization, discrepancy theory, and sublinear algorithms. His work on probabilistic methods and concentration inequalities built on foundations from Paul Erdős, Alfréd Rényi, and influenced subsequent research at Institute for Advanced Study and Mathematical Sciences Research Institute. He contributed to approximation schemes that relate to results from Vazirani, Gonzalez, and Karp, and to network design problems studied by groups at Bell Labs, AT&T Labs, and Google Research. Srinivasan developed techniques applied in graph algorithms studied alongside work from Jon Kleinberg, Éva Tardos, and Ravi Kumar, and in streaming algorithms tied to research at Yahoo Research and Facebook AI Research. His collaborations connected to scholars like Sanjeev Arora, David Karger, Noga Alon, Ravi Kannan, and Avi Wigderson, integrating combinatorial optimization with probabilistic combinatorics and theoretical computer science.

Awards and honors

Srinivasan's contributions have been recognized by awards and fellowships from organizations such as the National Science Foundation, the Association for Computing Machinery, and professional societies aligned with SIAM and IEEE. He received invited talks at major venues including STOC, FOCS, and ICML, and held visiting positions associated with Institute for Advanced Study and Microsoft Research New England. His mentorship and service were acknowledged in awards given by departments at University of Maryland, College Park and collaboration grants linked to Simons Foundation and Gordon and Betty Moore Foundation.

Selected publications

- "Randomized Rounding and Probabilistic Methods" — contributions related to work by Raghavan and Thompson and widely cited in venues like Journal of the ACM and SIAM Journal on Computing. - Papers on discrepancy minimization building on methods from Beck and Spencer, appearing in proceedings of STOC and FOCS. - Works on approximation algorithms for network design problems related to research by Jain and Goemans, published in SIAM Journal on Computing and Mathematics of Operations Research. - Articles on streaming and sublinear algorithms connecting to research from Muthukrishnan and Cormode, presented at SODA and PODS. - Collaborative papers with researchers such as Sanjoy Dasgupta, Noga Alon, and David Karger addressing combinatorial optimization and probabilistic analysis, appearing in major theoretical computer science conferences.

Category:Living people Category:Computer scientists Category:Theoretical computer scientists