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.
- 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
Honors and Awards
- 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.
Google.org announced that Madhav Marathe and Anil Vullikanti, researchers at the University of Virginia’s Biocomplexity Institute, were selected to receive funding as part of the company’s efforts to support projects using innovative Al and data analytics to help understand COVID-19 and address its impacts.
The UVA Biocomplexity Institute has received a $1.44M award from the National Science Foundation for a Virtual Organization (VO) that will facilitate communication and collaboration among CISE scientists currently involved in pandemic research through the NSF RAPID program.
As the COVID-19 pandemic continues to escalate in the United States and in many locations around the world, countless questions remain about next steps for mitigation and response. How will various mitigation methods affect the spread? How will those change as the pandemic progresses or regresses?
UVA Today got in touch with six UVA researchers and clinicians who are studying the disease and its effects and asked them what they’ve learned, as well as what it will take to emerge from the pandemic.
Christopher Barrett, executive director, Madhav Marathe, division director, and Bryan Lewis, research associate professor, spoke to S&P Global Market Intelligence about the abilities and limitations of their COVID analytical models.
Researchers have developed a model that uses social-media and search data to forecast outbreaks of Covid-19 well before they occur.
With ten million dollars from the National Science Foundation, computer scientists at the University of Virginia, Virginia Tech and 13 other schools have begun to tackle a massive problem with the power of big data and computers.
COVID-19 deaths and infections could skyrocket in Virginia in the coming months if left unchecked, according to updated modeling from the University of Virginia.
Will our hospitals have the capacity to care for those who become infected? This question remains at the heart of COVID-19 planning and response discussions everywhere as public health officials and policy makers consider how lifting interventions such as stay-at-home orders will impact a potential resurgence of the disease.
Stanford computer scientists are working with the country’s leading epidemiologists and volunteers to collect county-level data about when social-distancing regulations went in place, hoping to inform decisions on when to ease them.