Research

 

Overview:

I'm excited about designing clever, impactful solutions to challenging real-world problems. Specifically, I draw on Machine Learning and Sequential Decision Making tools such as: Bandits/Restless Bandits, Online Learning/Reinforcement Learning and Probabilistic Modeling, etc. Recently I've also been fascinated by and started exploring causal inference!

My Ph.D. research focuses on using optimization and planning techniques (eg: bandits) for public health and has considered two such real-world application areas:
(1) AI for Tuberculosis prevention [e.g. these NeurIPS'20, AAMAS'21, AAMAS'22 papers] and
(2) most recently, AI for improving maternal & child health [e.g. AAAI'22, and ongoing work].

I have also worked on projects utilizing decision-focused learning [e.g. AAAI'20, AAMAS'20] and worked on COVID-19 modeling [PNAS'20, SSRN, and other papers below].
 

Publications:
 

Conference Publications
 

In the pipeline
 

  • Aditya Mate, Aparna Taneja, Gauri Jain and Milind Tambe.
    “Restless and Non-Stationary Bandits for Planning Public Health Interventions”,
    In the pipeline.
    Prelimiary version appeared at EAAMO'2022.
    [poster]

  • Shresth Verma, Aditya Mate, Kai Wang, Neha Madhiwalla, Aparna Hegde, Aparna Taneja and Milind Tambe.
    “Restless Multi-Armed Bandits for Maternal and Child Health: Results from Decision-Focused Learning”,
    In the pipeline. 

  • Paritosh Verma, Shresth Verma, Aditya Mate, Aparna Taneja and Milind Tambe.
    “Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits”,
    In the pipeline. 

  • Feiran Jia, Aditya Mate, Zun Li, Shahin Jabbari, Mithun Chakraborty Milind Tambe, Michael Wellman, Yevgeniy Vorobeychik.
    “A Game-Theoretic Approach for Hierarchical Policy-Making”,
    In the pipeline. [arxiv]

Rigorously peer-reviewed conference publications 
 

  • [C12] Kai Wang*, Shresth Verma*, Aditya Mate, Sanket Shah, Aparna Taneja, Neha Madhiwalla, Aparna Hegde, Milind Tambe.
    “Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Care Domain”,
    [AAAI 2023] AAAI Conference on Artificial Intelligence 2023, Washington DC, USA. 
    [arxiv]
     
  • [C11] Shresth Verma*, Gargi Singh*, Aditya Mate, Paritosh Verma, Sruthi Gorantla, Neha Madhiwalla,
    Aparna Hegde, Divy Thakkar, Aparna Taneja, Manish Jain and Milind Tambe.
    “Increasing Impact of Mobile Health Programs: SAHELI for Maternal and Child Care”,
    [IAAI 2023]  Innovative Applications of Artificial Intelligence (IAAI) 2023, Washington DC, USA
     
  • [C10] Aditya Mate*, Lovish Madaan*, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham and Milind Tambe.
    “Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health”,
    [AAAI 2022] AAAI Conference on Artificial Intelligence 2022, Vancouver, Canada. (* = Equal contribution).
    [arxiv] [lightning talk (1 min)] [short explainer]
     
  • [C9]  Aditya Mate, Arpita Biswas, Christoph Siebenbrunner, Susobhan Ghosh and Milind Tambe.
    “Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems”,
    [AAMAS 2022] International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2022, Auckland, New Zealand.
    [arxiv] [10-min talk]
     
  • [C8] Zun Li, Feiran Jia, Aditya Mate, Shahin Jabbari, Mithun Chakraborty, Milind Tambe, Yevgeniy Vorobeychik.
    “Solving Structured Hierarchical Games Using Differential Backward Induction”,
    [UAI 2022] Conference on Uncertainty in Artificial Intelligence (UAI) 2022, Eindhoven, Netherlands. 
    [arxiv]
     
  • [C7]  Aditya Mate.
    “AI for Planning Public Health Interventions”,
    [IJCAI 2021 DC] International Joint Conference on Artificial Intelligence, Doctoral Consortium IJCAI 2021
    [paper]
     
  • [C6] Aditya Mate,  Andrew Perrault and Milind Tambe.
    “Risk-Sensitive Interventions in Public Health: Planning with Restless Multi-Armed Bandits”
    [AAMAS 2021] International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2021, London, UK. 
    [talk] [paper] [code] [short explainer]
     

  • [C5] Aditya Mate*, Jackson Killian*, Haifeng Xu, Andrew Perrault and Milind Tambe.
    “Collapsing Bandits and Their Application to Public Health Intervention”,
    [NeurIPS 2020] Advances in Neural and Information Processing Systems (NeurIPS) 2020, Vancouver, Canada (* = Equal contribution). 
    [paper] [code] [talk at NeurIPS'20] [talk at CompSust DC'20]
     
  • [C4] 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 2020] AAAI Conference on Artificial Intelligence 2020, New York, USA
    [paper]
     
  • [C3] Wang Kai, Andrew Perrault, Aditya Mate and Milind Tambe.
    “Scalable Game-Focused Learning of Adversary Models: Data-to-Decisions in Network Security Games”,
    [AAMAS 2020] International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, Auckland, New Zealand.
    [paper]
     
  • [C2] 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 2020] In 64th Human Factors and Ergonomics Society (HFES) Annual Conference.
    [paper]
     
  • [C1] B. Sombabu, Aditya Mate, D. Manjunath, Sharayu Moharir.
    “Whittle Index for AoI-aware scheduling”,
    [COMSNETS 2020] In 12th International Conference on Communication Systems and Networks (COMSNETS). IEEE, 2020 
    [paper]

 

Journal Publications
 

  • [J1] Bryan Wilder, Marie Charpignon, Jackson A Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe, and Maimuna S. Majumder.
    “Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and NewYork City”,
    [PNAS 2020] Proceedings of the National Academy of Sciences (PNAS), 2020.
    [paper]
     

Workshops
 

  • [W11] Shresth Verma,  Aditya Mate, Kai Wang, Aparna Taneja and Milind Tambe. 
    "Case Study: Applying Decision Focused Learning in the Real World"
    [NeurIPS 2022] Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022 
     
  • [W10]  Aditya Mate*, Lovish Madaan*, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hedge, Pradeep Varakantham and Milind Tambe. 
    "Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes"
    [NeurIPS 2021] Workshop on Machine Learning in Public Health (MLPH), NeurIPS 2021 (*equal contribution)
    Best Paper Award
     
  • [W9] Aviva Prins, Aditya Mate, Jackson Killian, Rediet Abebe and Milind Tambe
    "Incorporating Healthcare Motivated Constraints in Restless Bandit Based Resource Allocation",
    [NeurIPS 2020]  Workshop on Machine Learning for Health (ML4H), NeurIPS 2020, Vancouver, Canada
    Best Thematic Submission 
     
  • [W8] Aviva Prins, Aditya Mate, Jackson Killian, Rediet Abebe and Milind Tambe.
    "Incorporating Healthcare Motivated Constraints in Restless Bandit Based Resource Allocation"
    [NeurIPS 2020] Workshop on Challenges of Real World Reinforcement Learning (RWRL), NeurIPS 2020, Vancouver, Canada
     
  • [W7] Aviva Prins, Aditya Mate, Jackson Killian, Rediet Abebe and Milind Tambe
    "Incorporating Healthcare Motivated Constraints in Restless Bandit Based Resource Allocation",
    [NeurIPS 2020]  Workshop on Machine Learning in Public Health (MLPH), NeurIPS 2020, Vancouver, Canada
    Best Lightning Paper
     
  • [W6] 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 2020]  ACM SIGKDD 2020 Workshop on Humanitarian Mapping
     
  • [W5] Bryan Wilder, Marie Charpignon, Jackson Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe and Maimuna Majumder.
    “Integrating agent-based modeling and Bayesian inference to uncover between-population variation in COVID-19 dynamics”,
    [KDD 2020]  In ACM SIGKDD 2020 Workshop on Humanitarian Mapping.
     
  • [W4] Bryan Wilder, Marie Charpignon, Jackson Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe and Maimuna Majumder. 
    “Bayesian inference of between-population variation in COVID-19 dynamics”,
    [ICML 2020] Workshop on Machine Learning for Global Health, International Conference on Machine Learning. 2020.
     
  • [W3] Aditya Mate*, Jackson A. Killian*, Haifeng Xu, Andrew Perrault and Milind Tambe.
    "Building Decision Aids for Community Health Workers: Optimizing Interventions via RestlessBandits",
    [AAMAS 2020]  OptLearnMAS, AAMAS 2020 Workshop, Auckland, New Zealand 
  • [W2] Kai Wang, Aditya Mate, Bryan Wilder, Andrew Perrault, and Milind Tambe. 2019.
    Using Graph Convolutional Networks to Learn Interdiction Games .
    [IJCAI 2019] In AI for Social Good workshop (AI4SG) at International Joint Conference on Artificial Intelligence (IJCAI) 2019.
    [paper]
     
  • [W1] Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, and Milind Tambe. 2019.
    “Decision-Focused Learning of Adversary Behavior in Security Games.”
    [AAMAS 2019]  In GAIW: Games, Agents and Incentives Workshop at International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19).
    [paper]
     

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]

 

News Coverage:
 

  • Harvard SEAS, November 2022, "CS student helps NGO launch pilot program" [article]
     
  • Google AI blog, August 2022, "Using ML to Boost Engagement with a Maternal and Child Health Program in India[article]
     
  • 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]