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• Randomized Algorithms
• Networking
• Social Networks
• +7 more

Aravind Srinivasan is a Professor of Computer Science at the University of Maryland, College Park, USA. He holds tenure and has held academic positions at the National University of Singapore and Bell Labs. He completed his B.Tech at IIT Madras and his Ph.D. at Cornell University, with postdoctoral research at the Institute for Advanced Study and DIMACS. His research focuses on randomized algorithms, networking, social networks, combinatorial optimization, and their applications to public health, machine learning, and energy systems. He has published over 115 papers in top journals like Nature and the Journal of the ACM, and his work has received significant recognition, including 1,551 citations for his 2004 Nature paper on disease modeling. Education: B.Tech, Indian Institute of Technology Madras Ph.D., Cornell University Awarded Fellowships from ACM, AAAS, IEEE, and EATCS, he also serves as Editor-in-Chief of the ACM Transactions on Algorithms. His students have secured roles in academia, industry, and government, reflecting his impactful mentorship. His contributions span theoretical computer science and applied domains, with notable work in probabilistic methods, network science, and interdisciplinary applications.

Wojciech SzpankowskiView profile

Professor

• Analysis of Algorithms
• Information Theory
• Analytic Combinatorics
• +6 more

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University. He holds concurrent positions as a Guest Professor at ETH Zurich and a Professor at Jagiellonian University, Krakow. His research focuses on analysis of algorithms, information theory, analytic combinatorics, and random structures. He has directed major initiatives like the NSF Science & Technology Center on Science of Information (CSoI), a $50M 10-year project. Szpankowski is a Fellow of the IEEE and has received prestigious awards including the Flajolet Prize (2020) and Humboldt Research Award (2010). He has held visiting roles at institutions worldwide, including Stanford University and the Newton Institute, Cambridge. His academic journey includes tenure as Full Professor at Purdue (since 1992), Assistant Professorships at McGill University (1984–1992) and Technical University of Gdańsk (1980–1984). His scholarly contributions span monographs like "Average Case Analysis of Algorithms on Sequences" (2001) and "Analytic Pattern Matching" (2015), with a forthcoming work on Analytic Information Theory (2022). He actively contributes to scientific boards, including HIIT Helsinki and NeuroMat Brazil. Szpankowski’s publications emphasize interdisciplinary research at the intersection of computer science, mathematics, and biology. His work on privacy-preserving data analysis, dynamic network inference, and protein superfamily evolution showcases his broad impact. Keynote talks at major conferences (e.g., SODA 2019, AofA 2016) reflect his leadership in theoretical computer science. His grants and recognitions underscore institutional trust: the NSF Science & Technology Center (2010) and Arden L. Bement Jr. Award (2015) highlight his pioneering role in redefining information science. Collaborations span academia and industry, with affiliations at Hewlett-Packard and INRIA.

Raimundo SaonaView profile

Research Fellow

• Game Theory
• Probability Theory
• Algorithmic Game Theory
• +3 more

Raimundo Saona is a Research Fellow in the Department of Mathematics at the London School of Economics and Political Science (LSE), where he joined in 2025. His work bridges theoretical mathematics with practical applications in computer science and decision theory. His research focuses on Game Theory, Probability Theory, Algorithmic Game Theory, Stochastic Analysis, Optimal Control, and Imprecise Probability Theory. He develops mathematical tools to provide new insights on situations involving uncertainty, working with both continuous and discrete models, proving theoretical existence theorems, coding efficient algorithms, and analyzing both dynamic online and static offline settings. His publication record shows a strong focus on stochastic games, Markov decision processes, and prophet inequalities. His recent work (2022-2024) has centered on ergodic unobservable MDPs, marginal values in stochastic games, and zero-sum random games on directed graphs, demonstrating his continued contribution to advancing theoretical frameworks in game theory and decision processes. His earlier work established foundational results in POMDPs and prophet inequalities.