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Madhav Marathe

  • Executive Director
  • Distinguished Professor in Biocomplexity, Biocomplexity Institute
  • Professor of Computer Science, School of Engineering and Applied Science
Madhav Marathe


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, 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 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 the networks and dynamics; and (v) high-performance services-based computing solutions that can be delivered seamlessly to end users and policy makers. 


    • 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
  • 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

    • 2021 KDD Best Paper Award, Applied Data Science Track
    • 2021 Trinity Challenge Finalist (top 16 out of 350+)
    • 2019 Distinguished Professor
    • 2018 FellowSociety 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 FellowAmerican 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 FellowAssociation for Computing Machinery (ACM) for contribution to high performance computing algorithms and software environments for simulating and analyzing socio-technical systems
    • 2013 FellowInstitute 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

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
Network Systems Science and Advanced Computing
Hurt B; Adiga Ani; Marathe M; Barrett C . 2021 Winter Simulation Conference (WSC). IEEE. 2021;
Network Systems Science and Advanced Computing
Borchering R; Viboud C; Howerton E; Smith C; Truelove S; Runge M; Reich N; Contamin L; Levander J; Salerno J; van Panhuis W; Kinsey M; Tallaksen K; Obrecht F; Asher L; Costello C; Kelbaugh M; Wilson S; Shin L; Gallagher M; Mullany L; Rainwater-Lovett K; Lemaitre J; Dent J; Grantz K; Kaminsky J; Lauer S; Lee E; Meredith H; Perez-Saez J; Keegan L; Karlen D; Chinazzi M; Davis J; Mu K; Xiong X; Pastore Y Piontti A; Vespignani A; Srivastava A; Porebski P; Venkatramanan S; Adiga Aniruddha; Lewis B; Klahn B; Outten J; Schlitt J; Corbett P; Telionis A; Wang L; Peddireddy A; Hurt B; Chen J; Vullikanti A; Marathe M; Healy J; Slayton R; Biggerstaff M; Johansson M; Shea K; Lessler J . Morbidity and Mortality Weekly Report. Centers for Disease Control and Prevention. 2021; 70(19):719-724
Network Systems Science and Advanced Computing
Chang S; Wilson M; Lewis B; Mehrab Z; Dudakiya K; Pierson E; Koh P; Gerardin J; Redbird B; Grusky D; Marathe M; Leskovec J . KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery. 2021; :2632-2642
Network Systems Science and Advanced Computing
Eubank S; Eckstrand I; Lewis B; Venkatramanan S; Marathe M; Barrett C . Bulletin of Mathematical Biology. Springer Link. 2020; 82(4):52
Network Systems Science and Advanced Computing
Adiga Ani; Dubhashi D; Lewis B; Marathe M; Venkatramanan S; Vullikanti A . Journal of the Indian Institute of Science. Springer. 2020; 100:793-807
Network Systems Science and Advanced Computing
Bisset K; Chen J; Deodhar S; Feng X; Ma Y; Marathe M . ACM Transactions on Modeling and Computer Simulation (TOMACS). 2014; 24(1)
In the News

In alignment with the University of Virginia’s goal to move its research from prominence to preeminence, deans, faculty, and researchers from across Grounds got together to participate in the formal launch of the Contagion Science program, an initiative funded by the University as part of its Prominence-to-Preeminence STEM initiative.


Contagions, severe weather, natural disasters, civil unrest – whatever data scientists are forecasting using network models, simulation-based methods are often the most effective, according to researchers at the University of Virginia Biocomplexity Institute and Princeton University, whose findings were published in the paper, “Fundamental Limitations on Efficiently Forecasting Certain Epidemic Measures in Network Models,” by PNAS (Proceedings of the National Academy of Sciences).

Epidemic Response

Researchers from UVA’s Biocomplexity Institute and School of Engineering and Applied Science, working with a team of multi-disciplinary scientists from around the world, have spent the last two years developing highly advanced computational models designed to inform policy makers, save lives and prepare for future global epidemics.


For much of the pandemic, the Biocomplexity Institute has been modeling the likely trajectory of COVID-19 in Virginia using mobility data, case rates, vaccination numbers and a slew of other statistics that can predict how, where and how fast the virus will continue to spread. With many Virginians — and public health officials — already preparing for holiday gatherings, understanding potential risks could be crucial for decision-making.

Virginia Mercury logo - black

This article discusses how the University of Virginia’s Biocomplexity Institute is helping local health officials select mobile vaccine sites. Our data dashboards and reports include information on mobility, drawn from anonymized cell phone data collected by a company called SafeGraph, showing where and when Virginians were traveling to help understand the impact of safety restrictions.

Virginia Mercury logo - black

Rather than a shut-down, what if we knew which places to close during a pandemic to curtail the spread of a virus and lessen the economic impact to a locality? A model showing how this can be done, created by researchers at the University of Virginia Biocomplexity Institute and Stanford and Northwestern universities, has won a Best Paper Award in the Applied Research track at the recently concluded ACM KDD conference.


When the coronavirus reached Virginia, public health officials worried there would be so many patients, they would need to start building field hospitals right away. But a team of University of Virginia scientists, part of the Biocomplexity Institute, told the state to wait. The governor’s stay-at-home order, quarantines and social distancing that began in March 2020 could slow the disease’s spread.

The Virginian-Pilot Black logo
Biocomplexity Institute News

Fox is considered a pioneer in the fields now known as computational and data science, and he has brought his considerable influence and expertise to the University of Virginia, where he joined the faculty of the Biocomplexity Institute and the School of Engineering and Applied Science’s Department of Computer Science in July.

Biocomplexity Institute News

On January 29, during a virtual ceremony over Zoom, the University of Virginia honored and recognized faculty members for their outstanding contributions to their fields and the impact of their research and scholarly activities at the annual Research Achievement Awards.

UVAToday (black)

Researchers across Grounds joined the effort to defeat COVID-19—even as the world plunged abruptly into a new reality of shutdowns and social distancing—bringing their expertise together to understand the virus and prevent and treat infection.

UVA logo (black)

The number of positive COVID-19 cases in Virginia has quadrupled in the last week. But experts say the daily numbers released by the Department of Health is still an undercount exacerbated by some of the country’s lowest per capita testing.

VPM (black)

The many unknowns about COVID-19 – including its precise origin, how long the disease incubates, and when during the incubation people become contagious – have made data collection, and therefore, identification of effective intervention methods challenging. In response to this global pandemic, researchers from the University of Virginia’s Biocomplexity Institute have developed a set of visualization and analytical web applications to help provide a better understanding of the epidemic’s scope and aid in bringing the outbreak to a swift conclusion.

Network Science

The University of Virginia’s Biocomplexity Institute was recently awarded a five-year $4 million collaborative grant from the U.S. National Science Foundation (NSF) to build a self-sustaining cyberinfrastructure (CINES – pronounced “science”) to serve as an open-source, web-based repository for developing, trading, analyzing, and sharing network science resources. 


Agricultural trade is crucial in delivering food to consumers worldwide. The benefits range from lower prices to greater variety in our food supply, and most importantly, the ability to reduce food insecurity across the globe. But, as international trade increases, so does the spread of invasive and destructive agricultural pests that can threaten food production and even destabilize our global food supply.   


As international trade and human mobility increase, so does the spread of invasive and destructive agricultural pests, worsened by climate change and the intensive agricultural practices occurring globally. As part of a project funded by the U.S. Department of Agriculture (USDA), researchers from the University of Virginia’s Biocomplexity Institute are focusing their attention on one pest in particular – the South American tomato leafminer or Tuta absoluta. 

Biocomplexity Institute News

One of the University of Virginia’s most ambitious research efforts is beginning to take shape. In the past several months, UVA announced and introduced the Biocomplexity Institute and Initiative, which commenced operations in the UVA Research Park in September 2018, and most recently, appointed five individuals to join Executive Director Dr. Christopher L. Barrett on the senior leadership team.