Bio

BTech. Computer Science and Engineering, IIT Chennai, 1989Ph.D. Computer Science, University at Albany-SUNY, 1994

"Our research focus is to understand general principles that govern the design, analysis, control, and optimization of complex networks. We take a team science approach and develop innovative solutions that have societal impact. "

 

Research interests include:

Network Science, Machine Learning, Artificial Intelligence, High-Performance Computing, Multi-Agent Systems and Epidemic Science. 

 

 

Madhav Marathe is an endowed Distinguished Professor in Biocomplexity, Director of the Network Systems Science and Advanced Computing (NSSAC) Division, Biocomplexity Institute and Initiative, and a tenured Professor of Computer Science at the University of Virginia. Dr. Marathe is a passionate advocate and practitioner of transdisciplinary team science. During his 25-year professional career, he has established and led a number of large transdisciplinary projects and groups. His areas of expertise are network science, artificial intelligence, high-performance computing, computational epidemiology, biological and socially coupled systems, and data analytics.

From January 2005 to September 2018, he was a Professor of Computer Science at Virginia Tech. Concurrently, at Virginia Tech, he was the Deputy Director (2005-2014) and then the Director (2014-2018) of the Network Dynamics and Simulation Science Laboratory at the Biocomplexity Institute of Virginia Tech. He obtained his Bachelor of Technology degree in 1989 in Computer Science and Engineering from the Indian Institute of Technology, Madras, and his Ph.D. in 1994 in Computer Science from the University at Albany -SUNY, under the supervision of Professors Harry B. Hunt III and Richard E. Stearns. Before coming to Virginia Tech in 2005, he worked in the Basic and Applied Simulation Science group (CCS-5) in the Computer and Computational Sciences Division at Los Alamos National Laboratory where he was team leader in a theory-based, advanced simulation program to represent, design, and analyze extremely large socio-technical and critical infrastructure systems. He held adjunct appointments at Chalmers University and the Indian Institute of Public Health.

Dr. Marathe has published more than 500 articles in peer reviewed journals, conferences, workshops and technical reports. Mentoring and training next generation scientists has been his life-long passion. He has mentored more than a dozen staff scientists, and (co)-advised more than 30+ doctoral students, 20+ MS students and 15 postdoctoral fellows.

Dr. Marathe and his division focus on developing the scientific foundations and the associated engineering principles to study large-scale biological, information, social, and technical (BIST) systems. His current interests span five broad themes: (i) methods to construct various BIST networks using partial and noisy data as well as procedural information; (ii) understanding the general form and structure of dynamical processes over BIST networks (e.g., key network/pathway properties and typical pathways that impact dynamics); (iii) algorithmic theory of optimization and control as it pertains to the dynamical processes, including methods to detect, enhance, arrest, and mitigate dynamics; (iv) general conceptual and algorithmic foundations to understand the co-evolution of networks and dynamics; and (v) high-performance services-based computing solutions that can be delivered seamlessly to end users and policy makers.

 

Awards

  • Distinguished Researcher Award, University of Virginia for excellence in research through significant discoveries and scholarship 2023
  • Honorary Doctoral Degree conferred by Chalmers University 2023
  • Distinguished Alumni Award, Indian Institute of Technology, Madras for exemplary accomplishments in academia and his immense contributions to COVID-19 response efforts 2022
  • Best paper award, SIGKDD 2021, Applied Data Science Category for the paper titled “Supporting COVID-19 policy response with large-scale mobility-based modeling” 2021
  • Finalist, PI of the team selected as a finalist at the recently concluded (June 2021) Trinity Challenge for better protecting the world against health emergencies, using data-driven research and analytics 2021
  • Endowed Distinguished Professor of Biocomplexity, University of Virginia 2019
  • Fellow, Society for Industrial and Applied Mathematics (SIAM) for contributions to high performance computing algorithms and software systems for network science and public health epidemiology 2018
  • Dean’s Award for Excellence in Research, College of Engineering, Virginia Tech 2018
  • National Energy Research Scientific Computing Center NERSC Award (joint with A Bhatele, J Yeom, N Jain, C Kuhlman, Y Livnat, K Bisset, L Kale) for innovative use of HPC that led to scalable mapping of epidemic simulations on NERSC machines 2017
  • Constellation Group’s Supernova Award presented to NDSSL in the category of “Data to Decisions” for work by the group on developing high performance computing solutions to support national disaster management 2016
  • Fellow, American Association for the Advancement of Science (AAAS) for contributions to high performance computing and network science 2015
  • Fellow, Association for Computing Machinery (ACM) for contribution to high performance computing algorithms and software environments for simulating and analyzing socio-technical systems 2014
  • Fellow, Institute of Electrical and Electronics Engineers (IEEE) for contributions to socio-technical network science 2013
  • Inaugural George Michael Distinguished Scholar, Lawrence Livermore National Laboratory 2011-12
  • Award for Research Excellence, Virginia Bioinformatics Institute, Virginia Tech 2010
  • Distinguished Alumni Award, University at Albany 2004
  • Technical Achievement Award, Los Alamos National Laboratory 2004
  • Distinguished Copyright Award for TRANSIMS Software System 2000

Research Interests

  • Network science
  • Machine learning and artificial intelligence
  • Discrete graphical dynamical systems and multi-agent systems
  • Computational epidemiology, computational economics, computational immunology, and computational sustainability
  • Modeling and simulation
  • High-performance computing
  • Developing innovative computing technologies for reliable, secure, and sustainable socially coupled systems
  • Data analytics
  • Theoretical computer science, including complexity theory and algorithmics

In the News

Selected Publications

  • Modelling disease outbreaks in realistic urban social networks. Nature, 2004, 429(6988):180-184. ABS Eubank S, Guclu H, Vullikanti A, Marathe M, Srinivasan A, Toroczkai Z, Wang N
  • Supporting COVID-19 policy response with large-scale mobility-based modeling. Proceedings of the ACM Annual conference on Knowledge Discovery and Data Mining (KDD2021). Best paper award ABS Chang S, Wilson M, Lewis B, Mehrab Z, Dudakiya K, Pierson E, Koh P, Gerardin J, Redbird B, Grusky D, Marathe M, Leskovec J
  • Formal language constrained path problems, SIAM Journal of Computing, 2000, 30(3):809-837. ABS Barrett C, Jacob R, Marathe M
  • Structural and algorithmic aspects of massive social networks. Proceedings of the 15th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2004, 718-727. ABS Eubank S, Kumar A, Marathe M, Srinivasan A, Wang N
  • PAC learnability of node functions in networked dynamical systems. Proceedings of the 36th International Conference on Machine Learning, 2019, 82-91. ABS Adiga A, Kuhlman C, Marathe M, Ravi SS, Vullikanti A
  • DEFSI: Deep learning based epidemic forecasting with synthetic information. Proceedings of the 30th Innovative Applications of Artificial Intelligence (IAAI), 2019. ABS Wang L, Chen J, Marathe M
  • Approximation algorithms for scheduling on multiple machines. Proceedings of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2005, 254-263. Complete version: Journal of the ACM, 2009, 56(5): 28:1-28:31. ABS Vullikanti A, Marathe M, Parthasarathy S, Srinivasan A
  • Computational epidemiology. Communications of the ACM (CACM), 2013, 56(7): 88-96. ABS Marathe M, Vullikanti A
  • A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation. Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 2016, 30(6): 1148-1174. ABS Parikh N, Hayatnagarkar H, Beckman R, Marathe M, Swarup S
  • Overcoming the scalability challenges of epidemic simulations on blue waters. Proceedings of the 28th IEEE International Symposium on Parallel and Distributed Processing Symposium (IPDPS), 2014, 755-764. ABS Yeom J, Bhatele A, Bisset K, Bohm E, Gupta A, Kale L, Marathe M, Nikolopoulos D, Schulz M, Wesolowski L
  • INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling. ACM Transactions on Modeling and Computer Simulation (TOMACS), 2014, 24(1):4:1-4:32. ABS Bisset K, Chen J, Deodhar S, Feng X, Ma Y, Marathe M
  • Algorithmic aspects of capacity in wireless networks. Proceedings of the 2005 ACM SIGMETRICS International Conference on Measurements and Modeling of Computer Systems, 2005, 33(1):133-144. ABS Vullikanti A, Marathe M, Parthasarathy S, Srinivasan A
  • The complexity of planar counting problems. SIAM Journal on Computing (SICOMP), 1998, 27(4): 1142-1167. ABS Hunt III H, Marathe M, Radhakrishnan V, Stearns R
  • Privacy-first health research with federated learning. Nature, Digital Medicine. 2021 Sep 7;4(1):pp. 1-8. ABS Sadilek A, Liu L, Nguyen D, Kamruzzaman M, Rader B, Ingerman A, Mellem S, Kairouz P, Nsoesie E, MacFarlane J, Vullikanti A, Marathe M, Eastham P, Brownstein J, Howell M, Hernandez J
  • Learning the Behavior of a Dynamical System via a “Twenty Questions” Approach. Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI) ABS Adiga A, Kuhlman C, Marathe M, Stearns R, Ravi SS, Rosenkrantz D
  • Learning Coalition-Based Interactions in Networked Social Systems. Proceedings of the Association for the Advancement of Artificial Intelligence Conference (AAAI), 3138-3145. ABS Adiga A, Kuhlman C, Marathe M, Ravi SS, Rosenkrantz D, Stearns R, Vullikanti A
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