Madhav Marathe is an endowed Distinguished Professor of Biocomplexity, Executive Director of the Biocomplexity Institute, 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 30-year professional career, he has established and led many large transdisciplinary projects and groups. His areas of expertise include digital twins, network science, artificial intelligence, multi-agent systems, 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 500 articles in peer-reviewed journals, conferences, and workshops. Mentoring and training next-generation scientists has been his lifelong 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 team 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 policymakers.
- Research Interests
Science of massively interacting networked systems
Machine learning and artificial intelligence
Computational epidemiology, computational immunology, and computational sustainability
Multi-agent systems
High-performance computing
Modeling and simulation
Data analytics
Theoretical computer science, including complexity theory, and algorithmics
Digital twins- Short Courses and Tutorials
- Marathe M, Ramakrishnan N, Vullikanti A. 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.Marathe M and Swarup S. 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). São Paulo, Brazil, 8-12 May 2017.LinkMarathe M and Swarup S. 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. Link
- Education
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- Honors and Awards
2023 Member, Virginia Academy of Science, Engineering, and Medicine (VASEM)
2023 Fellow, Asia-Pacific Artificial Intelligence Association (AAIA)
2023 Honorary Doctoral Degree conferred by Chalmers University “for outstanding achievements in computer science, AI, and network science and his contributions to interdisciplinary research and technology.”
2023 Distinguished Researcher Award, University of Virginia for excellence in research through significant discoveries and scholarship.
2022 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, IEEE BigData
2022, “Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources,” Adiga A, Kaur G, Hurt B, Wang L, Porebski P, Venkatramanan S, Lewis B, Marathe M
2022 Best Student Paper award, International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response, Li G, Li A, Marathe M, Srinivasan A, Tsepenekas L, Vullikanti A.
2022 International Award of Excellence (IPM Team), for diverse and innovative programs for reducing crop losses caused by pests and damage to natural ecosystems, by adopting cost-effective and socially acceptable programs in developing countries thereby advancing livelihood standards, human health, and institution capacity, The IPM Innovation Lab, 10th International IPM symposium
2022 Invited Speaker, National Academies workshop “Anticipating Rare Events of Major Significance”. A report of the workshop was published in 2022 and can be accessed here.
2021 University of Virginia Research Award, in recognition of significant contributions through research and scholarship to the University of Virginia
2021 University of Virginia Research Award, in recognition of significant contributions through research and scholarship to the University of Virginia
2021 KDD Best Paper Award, Applied Data Science Track
2021 Trinity Challenge Finalist (top 16 out of 350+)
2019 Distinguished Professor
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