Shresth Verma

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I am a second-year PhD student at Harvard University advised by Prof. Milind Tambe. I’m interested in reinforcement learning and multi-agent systems for planning and optimization with applications in public health. My current work focuses on

  • Fair and robust algorithms for sequential resource allocation
  • Transparency and alignment in LLMs for planning tasks
  • Decision-Focused Learning

Previously, I spent two wonderful years at Google Research India, working in the AI for Social Good lab where I was grateful to be advised by Dr. Aparna Taneja. I developed robust bandit algorithms for planning targeted health interventions which in turn improve health literacy and medical adherence in underserved communities in India.

Before that, I was a Data Scientist at United Health Group where I worked in the Chief Medical Officer’s team for modeling readmission risks for millions of beneficiaries. I also developed tools to visualize patient’s wellness journey using data obtained from the world’s largest healthcare graph database.

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2023 - Present 2021 - 2023 2020 - 2021

News

Jul 24, 2024 Our work on Group Fairness in Decision-Focused Learning has been accepted at UAI 2024!
Mar 24, 2024 I’ll be attending Data Study Group at The Alan Turing Institute as a Facilitator!
Feb 5, 2024 Got accepted into Harvard’s Spring 2024 Technical AI Safety Fellowship!
Nov 10, 2023 Our paper about sequential allocation of multiple kinds of resources got accepted at IAAI 2024!
Jun 15, 2023 I’m starting PhD in CS at Harvard University advised by Prof. Milind Tambe!
Mar 1, 2023 Our work on developing an index for sequential resource allocation in non-markovian environments is accepted at IJCAI 2023!
Jan 1, 2023 Our work on field study of Decision Focused Learning has been accepted at AAMAS 2023!

Selected Publications

2024

  1. IAAI’24
    Improving Health Information Access in the World’s Largest Maternal Mobile Health Program via Bandit Algorithms
    Arshika Lalan*, Shresth Verma*, Paula Rodriguez Diaz, Panayiotis Danassis, Amrita Mahale, and 4 more authors
    In AAAI Conference on Artificial Intelligence, 2024

2023

  1. IJCAI’23
    Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare
    Panayiotis Danassis, Shresth Verma, Jackson A. Killian, Aparna Taneja, and Milind Tambe
    In International Joint Conference on Artificial Intelligence, 2023
  2. AAAI’23
    Scalable decision-focused learning in restless multi-armed bandits with application to maternal and child health
    Kai Wang*, Shresth Verma*, Aditya Mate, Sanket Shah, Aparna Taneja, and 3 more authors
    In AAAI Conference on Artificial Intelligence, 2023
  3. AAAI’23
    Robust planning over restless groups: engagement interventions for a large-scale maternal telehealth program
    Jackson A Killian*, Arpita Biswas*, Lily Xu*, Shresth Verma*, Vineet Nair, and 6 more authors
    In AAAI Conference on Artificial Intelligence, 2023
  4. AAMAS’23
    Restless Multi-Armed Bandits for Maternal and Child Health: Results from Decision-Focused Learning.
    Shresth Verma, Aditya Mate, Kai Wang, Neha Madhiwalla, Aparna Hegde, and 2 more authors
    In International Conference on Autonomous Agents and Multi Agent Systems, 2023
  5. IAAI’23
    Increasing impact of mobile health programs: SAHELI for maternal and child care
    Shresth Verma*, Gargi Singh*, Aditya Mate, Paritosh Verma, Sruthi Gorantla, and 6 more authors
    🏆Best Deployed Application🏆
    In AAAI Conference on Artificial Intelligence, 2023

2022

  1. AAAI’22
    Field study in deploying restless multi-armed bandits: Assisting non-profits in improving maternal and child health
    Aditya Mate*, Lovish Madaan*, Aparna Taneja, Neha Madhiwalla, Shresth Verma, and 4 more authors
    In AAAI Conference on Artificial Intelligence, 2022
  2. TSRML-NeurIPS’22
    Case study: Applying decision focused learning in the real world
    Shresth Verma, Aditya Mate, Kai Wang, Aparna Taneja, and Milind Tambe
    In Workshop on Trustworthy and Socially Responsible Machine Learning at NeurIPS, 2022

2021

  1. AAMAS’21
    Towards Sample Efficient Learners in Population based Referential Games through Action Advising
    Shresth Verma
    In International Conference on Autonomous Agents and Multi Agent Systems, 2021

2020

  1. CoDS-COMAD’20
    Deep reinforcement learning for single-shot diagnosis and adaptation in damaged robots
    Shresth Verma, Haritha S Nair, Gaurav Agarwal, Joydip Dhar, and Anupam Shukla
    In ACM International Joint Conference on Data Science and Management of Data, 2020
  2. LaREL-ICML’20
    Emergence of Multilingualism in Population based Referential Games
    Shresth Verma
    In Workshop on Language in Reinforcement Learning at ICML, 2020
  3. AAAI’20
    Emergence of Writing Systems through Multi-Agent Cooperation (Student Abstract)
    Shresth Verma, and Joydip Dhar
    In AAAI Conference on Artificial Intelligence, 2020