Research

 

Overview:
 

My recent research focusses on using AI to tackle public health problems. Most recently I have been working on building COVID-19 disease spread models using Agent-based modeling (ABM), to analyze and simulate the effect of various lockdown policies in India and inform policy decisions.

I have also been working on tackling public health problems, such as tuberculosis in India using AI. My work involves designing an algorithm to tell health workers which patients they must intervene on, on a given day so as to maximize the benefit of their limited intervention resources. 

Previously, I worked on a project to combat illegal smuggling, using deep learning, by predicting the probability that a smuggler will take a certain path and then using these predictions to optimally allocate the scarce security resources. I have also explored other domains such as game theory for cybersecurity, game focused learning, etc. (please find papers below).

Collaboration: I personally care about high-impact AI research that is useful in the real-world. I am always enthusiastic to collaborate and design cool solutions to real-world problems where AI can help. If you have potential ideas, please feel free to reach out to me at aditya_mate@g.harvard.edu

 

News Coverage:
 

  • Sakal Media House coverage, May 2020: Middle ground for India's lockdown situation [article]
     
  • Nature India coverage, April 2020: "Model finds 'middle ground' for India's lockdown exit" [article]

 

Publications:
 

2020 [Conference Publications]
 

  • Palvi Aggarwal, Omkar Thakoor, Aditya Mate, Milind Tambe, Edward A. Cranford, Christian Lebiere, and Cleotilde Gonzalez.
    “An Exploratory Study of a Masking Strategy of Cyberdeception Using CyberVAN.”
    [HFES-20] In 64th Human Factors and Ergonomics Society (HFES) Annual Conference. [paper]
     
  • Aditya Mate*, Jackson Killian*, Haifeng Xu, Andrew Perrault and Milind Tambe.
    “Collapsing Bandits and Their Application to Public Health Intervention”,
    [Under Submission] Working paper. (* = Equal contribution)
     
  • Wang Kai, Andrew Perrault, Aditya Mate and Milind Tambe.
    “Scalable Game-Focused Learning of Adversary Models: Data-to-Decisions in Network Security Games”,
    [AAMAS-20] International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, Auckland, New Zealand. [paper]
     
  • Perrault Andrew, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina and Milind Tambe.
    “End-to-End Game-Focused Learning of Adversary Behavior in Security Games”, 
    [AAAI-20] AAAI Conference on Artificial Intelligence 2020, New York, USA. [arxiv]
     
  • Bejjipuram Sombabu, Aditya Mate, D. Manjunath, Sharayu Moharir.
    “Whittle Index for AoI-aware scheduling”,
    [COMSNETS-20] In 12th International Conference on Communication Systems and Networks (COMSNETS). IEEE, 2020 [paper]

 

2020 [Workshops]
 

  • Aditya Mate, Jackson A. Killian, Bryan Wilder, Marie Charpignon, Ananya Awasthi, Milind Tambe and Maimuna S. Majumder.
    "Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States",
    [KDD-20]  ACM SIGKDD 2020 Workshop on Humanitarian Mapping

     

2020 [pre-prints]
 

  • Aditya Mate, Jackson A. Killian, Bryan Wilder, Marie Charpignon, Ananya Awasthi, Milind Tambe, and Maimuna S. Majumder. “Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States.” SSRN.  [paper] [code]
     
  • Bryan Wilder, Marie Charpignon, Jackson A Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe, and Maimuna S. Majumder. 4/1/2020. “The Role of Age Distribution and Family structure On COVID-19 Dynamics: A Preliminary Modeling Assessment for Hubei and Lombardy.” SSRN. [paper] [code]