Milind Tambe
Born
Maharashtra, India
CitizenshipAmerican
Alma materBITS Pilani
Carnegie Mellon University
AwardsAAAI Award for Artificial Intelligence for the Benefit of Humanity (2024)
AAAI Robert S Engelmore Award (2019)
IJCAI John McCarthy Award (2018)
ACM Fellow (2013)
AAAI Fellow (2007)
ACM SIGART Autonomous Agents Research Award (2005)
Scientific career
FieldsArtificial Intelligence
Computer Science
InstitutionsHarvard University
ThesisEliminating combinatorics from production match (1991)
Doctoral advisorAllen Newell
Paul Rosenbloom
Websiteteamcore.seas.harvard.edu/tambe

Milind Tambe is an Indian-American educator serving as a Professor of Computer Science at Harvard University. He also serves as the director of the Center for Research on Computation and Society at Harvard University and the director of "AI for Social Good" at Google Research India.

Career

Fellowships and Awards

Tambe is a fellow of AAAI (Association for Advancement of Artificial Intelligence),[1] as well as ACM (Association for Computing Machinery).[2] He is also a recipient of the IJCAI John McCarthy Award,[3] as well as the ACM SIGART Autonomous Agents Research Award.[4] Additionally, he has been recognized by the AAAI (Association for Advancement of Artificial Intelligence)'s Robert S. Engelmore Memorial Lecture Award and the Christopher Columbus Fellowship Foundation Homeland Security Award. He has also received the Distinguished Alumnus Award from Birla Institute of Technology and Science (BITS).[5]

Previous Positions

Previous to his position at Harvard and Google, he was Helen N. and Emmett H. Jones, Professor of Engineering and a Professor of Computer Science and Industrial and Systems Engineering at the University of Southern California, Los Angeles.[6]

Research

Tambe's work focuses on advancing AI and multi-agent systems for public health, conservation and public safety, with a track record of building pioneering AI systems for social impact. His research focuses on fundamental problems in computational game theory, machine learning, automated planning, intelligent agents, and multi-agent interactions that are driven by these topics, ensuring a virtuous cycle of research and real-world applications. This research has led to significant practical impact, such as the use of the green security games framework to assist wildlife conservation around the world, the use of social networks and machine learning to assist in improving public health outcomes such as HIV prevention, and the use of pioneering security games research for security optimization by agencies, such as the US Coast Guard and the Federal Air Marshals Service.

In terms of public safety and security, the security games framework that Professor Tambe pioneered has been deployed and tested for security optimization, both nationally and internationally, by agencies such as the US Coast Guard and the Federal Air Marshals Service. More specifically, Professor Tambe and team provided the first-ever applications of computational game theory for operational security. The first of these deployments was the ARMOR system of game-theoretic algorithms for security (e.g., counter-terrorism) which started operating at the Los Angeles LAX airport in 2007, deployed by the LAX police division.[7] This work was followed by pioneering deployments of security games for major security agencies such as the Federal Air Marshals Service,[8] the US Coast Guard[9] and the Transportation Security Administration.[10] This research is credited with more than $100 million in savings to US agencies.[11]

In terms of AI for conservation, Tambe and team were the first to apply AI models, specifically machine learning and game theory, for global scale anti-poaching efforts, as part of the PAWS project for wildlife conservation. The PAWS AI system has been deployed in collaboration with wildlife conservation agencies to assist rangers around the world. PAWS has helped rangers in removing 10s of 1000s of traps used to kill endangered wildlife in national parks in countries such Cambodia and Uganda.[12] Furthermore, PAWS is integrated with the SMART software, making PAWS available for use at 100s of national parks around the globe.[13]

Tambe and his team also provided the first large scale applications of social network algorithms for public health. For example, in a recently completed study with 700 youth experiencing homelessness, Tambe and team's algorithms led to a significant reduction in HIV risk behaviors compared to traditional approaches. Other examples include research in collaboration with NGOs for improving maternal health care outcomes, TB prevention and others.

Bibliography

  • Artificial Intelligence and Social Work (with E. Rice) Artificial Intelligence and Social Work, 2018. Cambridge University Press ISBN 1-108-42599-2
  • Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned (1st edition) 2011. Cambridge University Press, ISBN 1-107-09642-1
  • Keep the Adversary Guessing: Agent Security by Policy Randomization 2008. VDM Verlag Dr. Mueller e.K., ISBN 3-639-01925-3

References

  1. ↑ "Elected AAAI Fellows". AAAI (Association for Advancement of Artificial Intelligence). Retrieved January 2, 2012.
  2. ↑ ACM Names Fellows for Computing Advances that Are Transforming Science and SocietyArchived 2014-07-22 at the Wayback Machine, Association for Computing Machinery, accessed 2013-12-10.
  3. ↑ IJCAI Awards
  4. ↑ "The ACM SIGART Autonomous Agents Research Award". ACM/SIGART. Retrieved January 2, 2012.
  5. ↑ "BITS Pilani Distinguished Alumnus Awards 2020".
  6. ↑ "Milind Tambe". USC (University of Southern California, Los Angeles. Archived from the original on 2011-08-28. Retrieved 2012-01-02.
  7. ↑ "A Random Weapon in the War on Terror". Newsweek. Retrieved January 2, 2012.
  8. ↑ "A Tool for Strategic Security Allocation in Transportation Networks" (PDF). AAMAS (International Conference on Autonomous Agents and Multiagent Systems). Retrieved January 2, 2012.
  9. ↑ "Randomizing Boston Harbor security patrols" (Press release). Homeland Security News Wire. Retrieved January 2, 2012.
  10. ↑ Hamill, Sean D. (August 2, 2010). "Research on poker a good deal for airport security". post-gazette. Retrieved January 2, 2012.
  11. ↑ Winterfeldt, Detlof; Farrow, R. Scott; John, Richard S.; Eyer, Jonathan; Rose, Adam Z.; Rosoff, Heather (2020). "Assessing the Benefits and Costs of Homeland Security Research: A Risk-Informed Methodology with Applications for the US Coast Guard". Risk Analysis. Risk Analysis: An international Journal. 40 (3): 450–475. Bibcode:2020RiskA..40..450V. doi:10.1111/risa.13403. PMID 31613022. S2CID 204703326. Retrieved August 29, 2021.
  12. ↑ "This AI hunts poachers". 6 January 2018.
  13. ↑ "Preventing Poaching".
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