Madhav Marathe

Madhav Marathe Headshot
Division Director, Network Systems Science and Advanced Computing
Distinguished Professor in Biocomplexity, Biocomplexity Institute
Professor of Computer Science, School of Engineering and Applied 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. 

His prior positions include Professor of Computer Science and Director of the Network Dynamics and Simulation Science Laboratory within the Biocomplexity Institute of Virginia Tech and a team leader of research and computing in the Basic and Applied Simulation Science group, Computer and Computational Sciences Division at the Los Alamos National Laboratory. He is a Fellow of the American Association for the Advancement of Science (AAAS), Society for Industrial and Applied Mathematics (SIAM), Association for Computing Machinery (ACM) and Institute of Electrical and Electronics Engineers (IEEE). Dr. Marathe has published more than 350 articles in peer reviewed journals, conferences and workshops. 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 20 doctoral students, 20+ MS students and 10+ 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 the networks and dynamics; and (v) high-performance services-based computing solutions that can be delivered seamlessly to end users and policy makers. 

Full CV here

Research Interests

  • Science of massively interacting networked systems
  • Machine learning and artificial intelligence
  • Computational epidemiology, computational immunology, and computational sustainability
  • Modeling and simulation
  • Data analytics
  • Theoretical computer science, including complexity theory, and algorithmics


Postdoctoral Fellow, CIC-3 Group, Los Alamos National Laboratory
Ph.D. in Computer Science, University at Albany, SUNY
B. Tech in Computer Science and Engineering, Indian Institute of Technology Madras

Awards and Honors

  • 2018 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
  • 2017 Finalist, IEEE SCALE Challenge, CCGRID
  • 2017 National Energy Research Scientific Computing Center NERSC Award (joint with A Boatel, 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
  • 2016 Finalist, Best Paper Award, ACM Supercomputing conference
  • 2016 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
  • 2015 Fellow, American Association for the Advancement of Science (AAAS) for contributions to high performance computing and network science
  • 2014 Winner, AAMAS Blue Sky Ideas Best Paper Award
  • 2014 Fellow, Association for Computing Machinery (ACM) for contribution to high performance computing algorithms and software environments for simulating and analyzing socio-technical systems
  • 2013 Fellow, Institute of Electrical and Electronics Engineers (IEEE) for contributions to socio-technical network science
  • 2013 Invited participant, Computing Community Consortium Leadership in Science Policy Institute organized by the Computing Research Association
  • 2011-12 Inaugural George Michael Distinguished Scholar, Lawrence Livermore National Laboratory
  • 2010 Award for Research Excellence, Virginia Bioinformatics Institute, Virginia Tech
  • 2006 Best Paper Award, International Conference on Distributed Computing Systems
  • 2004 Distinguished Alumni Award, University at Albany
  • 2004 Achievement Award, Los Alamos National Laboratory

Tutorials and Short Courses

  • Mathematical and Computational Foundations of Infectious Disease Epidemiology. A 4-hour tutorial providing an overview of the state-of-the-art in mathematical and computational epidemiology, which have typically not been studied from a multi-disciplinary perspective. International Conference on Systems Biology (ICSB), Blacksburg, VA, 6-12 August 2017. Presented jointly with A Vullikanti and B Lewis.
  • Generating Synthetic Populations for Social Modeling. A 4-hour tutorial on the foundations and methods for generating synthetic agents. Autonomous Agents and Multi-agent Systems International conference (AAMAS), Sao Paulo, Brazil, 8-12 May 2017.  Presented jointly with S Swarup.
  • Generating Synthetic Populations for Social Modeling. A 4-hour tutorial on the foundations and methods for generating synthetic agents. IJCAI, New York, NY, 9-15 July 2016.  Presented jointly with S Swarup.
  • Generating Synthetic Populations for Social Modeling. A 4-hour tutorial on the foundations and methods for generating synthetic agents. Autonomous Agents and Multi-agent Systems International conference (AAMAS), Singapore, 9-10 May 2016.  Presented jointly with S Swarup.
  • Computational Epidemiology and Public Health Policy Planning. A 4-hour tutorial that provides an overview of the state-of-the-art in computational epidemiology from a multi-disciplinary perspective. 30th Annual conference on Artificial Intelligence (AAAI), Phoenix, AZ, 13 February 2016. Presented jointly with A Vullikanti and N Ramakrishnan.
  • Computational Epidemiology. A 3-hour course on this state-of-the-art multi-disciplinary research area, including data mining, machine learning, high performance computing, and theoretical computer science, as well as math, economics, and statistics. Knowledge Discovery and Data Mining (ACM KDD) conference, New York, NY, 24-27 August 2014. Presented jointly with A Vullikanti and N Ramakrishnan.

Selected Publications

  • Eubank S, Guclu H, Kumar A, Marathe M, Srinivasan A, Toroczkai Z, Wang N (2004). Modelling disease outbreaks in realistic urban social networks. Nature, 429(6988): 180-184. 
  • Barrett C, Jacob R, Marathe M (2000). Formal language constrained path problems. SIAM Journal on Computing (SICOMP), 30(3): 809-837. 
  • Hunt III H, Marathe M, Radhakrishnan V, Stearns R (1998). The complexity of planar counting problems. SIAM Journal on Computing (SICOMP), 27(4): 1142-1167.
  • Eubank S, Kumar A, Marathe M, Srinivasan A, Wang N (2004). Structural and algorithmic aspects of massive social networks. Proceedings of the 15thAnnual ACM-SIAM Symposium on Discrete Algorithms (SODA’04), 718-727. 
  • Adiga A, Kuhlman C, Marathe M, Ravi SS, Vullikanti A (2019) PAC learnability of node functions in networked dynamical systems. Proceedings of the 36thInternational Conference on Machine Learning, 82-91.
  • Wang L, Chen J, Marathe M (2019) DEFSI: Deep learning based epidemic forecasting with synthetic information. Proceedings of the 30thInnovative Applications of Artificial Intelligence(IAAI). 
  • Kumar A, Marathe M, Parthasarathy S, Srinivasan A (2005). Approximation algorithms for scheduling on multiple machines.  Proceedings of 46thAnnual IEEE Symposium on Foundations of Computer Science (FOCS'05), 254-263. Complete version: Journal of the ACM, 2009,56 (5): 28:1-28:31.
  • Kumar A, Marathe M, Parthasarathy S, Srinivasan A (2005). Algorithmic aspects of capacity in wireless networks. Proceedings of the 2005 ACM International Conference on Measurements and Modeling of Computer Systems (SIGMETRICS’05), 33: 133-144.
  • Marathe M, Vullikanti A (2013). Computational epidemiology. Communications of the ACM (CACM), 56(7): 88-96.
  • Hunt III HB, Marathe M, Radhakrishnan V, Ravi S, Rosenkrantz D, Stearns R (1998). NC approximation schemes for NP- and PSPACE-hard problems for geometric graphs. Journal of Algorithms, 26 (2): 238-274. 
  • Yeom J, Bhatele A, Bisset K, Bohm E, Gupta A, Kale L, Marathe M, Nikolopoulos D, Schulz M, Wesolowski L (2014). Overcoming the scalability challenges of epidemic simulations on blue waters. Proceedings of the 28th IEEE International Symposium on Parallel and Distributed Processing Symposium (IPDPS), 755-764.
  • Alam M, Khan M, Vullikanti A, Marathe M (2016). An efficient and scalable algorithmic method for generating large–scale random graphs. Proceedings of the IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC’16), 372-383.
  • Parikh N, Hayatnagarkar H, Beckman R, Marathe M, Swarup S (2016). 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), 30 (6): 1148-1174.
  • Bisset K, Chen J, Deodhar S, Feng X, Ma Y, Marathe M (2014). Indemics: An interactive high-performance computing framework for data intensive epidemic modeling. ACM Transactions on Modeling and Computer Simulation (TOMACS), 24(1): 4:1-4:32.
  • Han B, Hui P, Kumar A, Marathe M, Shao J, Srinivasan A (2012). Mobile data offloading through opportunistic communications and social participation. IEEE Transactions on Mobile Computing11 (5): 821-834.