Research

Overview:

I'm excited about using AI to solve impactful real-world problems, using techniques from Machine Learning (specifically: Decision-focused Learning) and Sequential Decision Making (specifically techniques like Bandits/Restless Bandits, Reinforcement Learning or Probabilistic Modeling). Recently, I've also been fascinated by and begun exploring causal inference (and here is my latest paper in this area, that I'm very proud of)!

My Ph.D. focuses on application of these AI techniques for tackling public health challenges. I've built novel sequential planning algorithms for solving fundamental computational challenges in real-world contexts such as in tuberculosis prevention (e.g. this NeurIPS'20 paper) and improving maternal & child health (e.g. this AAAI'22 paper). I've also worked on using decision-focused learning for public health (e.g. this AAAI'23 paper) and other applications (e.g. this AAMAS'20 paper) and have also explored COVID-19 modeling (e.g. this PNAS'20 paper).

Transcending traditional research boundaries, I've strived to deploy my research in the real world, which, as of early 2023, has assisted over 100,000 mothers enrolled in this system with our partner NGO.

News Coverage:

Harvard SEAS, "CS student helps NGO launch pilot program" [article]
Google AI blog, "Using ML to Boost Engagement with a Maternal and Child Health Program in India" [article]
Sakal Media House coverage: Middle ground for India's lockdown situation [article]
Nature Asia coverage: "Model finds 'middle ground' for India's lockdown exit" [article]

Publications:

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]

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]

Conference Publications:

Rigorously peer-reviewed conferences

[C14] Aditya Mate, Bryan Wilder, Aparna Taneja and Milind Tambe.
“Improved Policy Evaluation of Randomized Trials of Algorithmic Resource Allocation”,
[ICML 2023] International Conference on Machine Learning, ICML 2023.
[arxiv]

[C13] Shresth Verma, Aditya Mate, Kai Wang, Neha Madhiwalla, Aparna Hedge, Aparna Taneja and Milind Tambe
"Restless Multi-Armed Bandits for Maternal and Child Health: Results from Decision-Focused Learning"
[AAMAS 2023] International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2023, London, UK.

[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 Hedge, 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] [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] [short explainer] [code]
[C5] Aditya Mate*, Jackson Killian*, Haifeng Xu, Andrew Perrault and Milind Tambe.
“Collapsing Bandits and Their Application to Public Health Interventions”,
[NeurIPS 2020] Advances in Neural and Information Processing Systems (NeurIPS) 2020, Vancouver, Canada. (* Equal contribution)
[paper] [code]
[C4] 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]
[C3] 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]
[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] 12th International Conference on Communication Systems and Networks (COMSNETS). IEEE, 2020.
[paper]

Selected Workshop Papers:

[W12] Paritosh Verma, Shresth Verma, Aditya Mate, Aparna Taneja and Milind Tambe.
“Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits”,
[AAAI 2023] Workshop on AI for Social Good (AI4SG) 2023

[W11] Shresth Verma, Aditya Mate, Kai Wang, Neha Madhiwalla, Aparna Hedge, 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. (*equal contribution)

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

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