MOTIVATION

AI tools can be used to inform public health policy. For example, predictive analytics can be used to identify risk factors for disease; and optimization frameworks (whether single stage or repeated) can be used to identify when to screen or treat disease, or which risk groups to target given limited resources. Optimization frameworks can also be used to channel limited resources towards at-risk individuals to improve their adherence to healthy habits. We describe several projects and potential project areas below.

Improving Maternal and Child Health Outcomes in Partnership with ARMMAN

This project Improving Maternal and Child Health Outcomes in Partnership with ARMMAN aims at using AI for improving Maternal and Child Health Outcomes by predicting which beneficiaries are at a risk of dropping out from automated health information delivery programs.

Learning Loss Functions for Predict-then-Optimize

Predict-then-Optimize (PtO) is a framework for using machine learning to perform decision-making under uncertainty. As the name suggests, it proceeds in two steps—first, you make predictions about the uncertain quantities of interest and then, second, you make the required decisions assuming that these predictions are accurate. However, these decisions are only optimal if the input predictions are accurate. To evaluate the quality of our decisions for a given prediction, we check how well they would perform on the ground truth values of the quantities of interest (from the dataset) as opposed to the predictions. Let’s use an example to make all these different steps in the PtO pipeline concrete.

HIV Prevention Among Homeless Youth

The HIV Prevention Among Homeless Youth project focuses on the development of decision support systems for homeless youth drop-in center staff, who need to find the most influential homeless youth to raise awareness about HIV (and other STDs) among their peers, and to drive the homeless youth community towards safer behaviors.

Algorithmic Social Interventions for Social Work and Public Health

Combatting COVID-19

The COVID-19 outbreak has caused an unprecedented global reaction with countries taking drastic steps to combat the pandemic. Mathematical modeling and multi-agent based analysis of the pandemic allows better understanding of the disease spread and may help inform policy at the national and regional level. We use tools and modeling techniques from AI to help understand the situation better and design aids that may help policymakers design better solutions in the fight against this pandemic. Read more about Teamcore’s efforts to stop the spread of COVID-19.

Using Machine Learning & Multi-Agent Planning to Fight Tuberculosis

Fighting and Preventing Tuberculosis in India

Tuberculosis is one of the top 10 killers in the world and is especially prevalent in India. AI can help across the entire pipeline of care, from decision support tools for planning active screening routes, to predictive algorithms for resource constrained health workers to deliver targeted interventions to patients.

Using Social Networks for Prevention Interventions

Click here to read more on this initiative.

Substance abuse prevention among homeless youth

Research has consistently documented levels of cocaine, heroin, methamphetamine, alcohol, and marijuana use and abuse among these adolescents that far exceed that of housed adolescents. This Social Networks and Substance Abuse Prevention for Homeless Youth project aims to use algorithms to determine the best group formations to prevent regular use of hard drugs among homeless youth.

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Suicide prevention among active duty military and homeless youth

One of the fundamental questions facing social science is how social networks and the cognitions people have about their networks affect their mental states and mental health. AI techniques present an opportunity to dynamically model social networks and the messages transmitted across those networks to create predictive models of influence unavailable with standard statistical techniques. View Predictive Modeling for Early Identification of Suicidal Thinking.

PROJECT PARTICIPANTS

SPONSORS

RELATED PUBLICATIONS

Yunfan Zhao, Nikhil Behari, Edward Hughes, Edwin Zhang, Dheeraj Nagaraj, Karl Tuyls, Aparna Taneja, and Milind Tambe. 8/3/2024. “Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization.” In International Joint Conference on Artificial Intelligence (IJCAI) 8/3/2024. Jeju Island, South Korea.

Yunfan Zhao, Nikhil Behari, Edward Hughes, Edwin Zhang, Dheeraj Nagaraj, Karl Tuyls, Aparna Taneja, and Milind Tambe. 5/5/2024. “Towards Zero Shot Learning in Restless Multi-armed Bandits: Extended Abstract.” International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Auckland, New Zealand.

Sanket Shah, Arun Suggala, Milind Tambe, and Aparna Taneja. 5/1/2024. “Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning.” International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Auckland, New Zealand.

Panayiotis Danassis, Shresth Verma, Jackson A. Killian, Aparna Taneja, and Milind Tambe. 8/2023. “Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare.” International Joint Conference on Artificial Intelligence (IJCAI).

Jackson A. Killian*, Arpita Biswas*, Lily Xu*, Shresth Verma*, Vineet Nair, Aparna Taneja, Aparna Hegde, Neha Madhiwalla, Paula Rodriguez Diaz, Sonja Johnson-Yu, and Milind Tambe. 2/9/2023. “Robust Planning over Restless Groups: Engagement Interventions for a Large-Scale Maternal Telehealth Program.” In AAAI Conference on Artificial Intelligence.

Aditya Mate. 10/16/2022. “Optimization and Planning of Limited Resources for Assisting Non-Profits in Improving Maternal and Child Health.” INFORMS Doing Good with Good OR.

Jackson A. Killian, Lily Xu, Arpita Biswas, and Milind Tambe. 8/2022. “Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning.” In Uncertainty in Artificial Intelligence (UAI). (Supplementary material)

Vineet Nair, Kritika Prakash, Michael Wilbur, Aparna Taneja, Corrine Namblard, Oyindamola Adeyemo, Abhishek Dubey, Abiodun Adereni, Milind Tambe, and Ayan Mukhopadhyay. 7/2022. “ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria.” In International Joint Conference on AI (IJCAI) 7/2022.

Han-Ching Ou. 3/31/2022. “Sequential Network Planning Problems for Public Health Applications.” PhD Thesis, Computer Science, Harvard University.

Han-Ching Ou*, Christoph Siebenbrunner*, Jackson Killian, Meredith B Brooks, David Kempe, Yevgeniy Vorobeychik, and Milind Tambe. 5/2022. “Networked Restless Multi-Armed Bandits for Mobile Interventions.” In 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022). Online.

Aditya Mate, Arpita Biswas, Christoph Siebenbrunner, Susobhan Ghosh, and Milind Tambe. 5/2022. “Efficient Algorithms for Finite Horizon and Streaming RestlessMulti-Armed Bandit Problems.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Aditya Mate*, Lovish Madaan*, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham, and Milind Tambe. 2/2022. “Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health.” In AAAI Conference on Artificial Intelligence. Vancouver, Canada.

Aditya Mate*, Lovish Madaan*, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham, and Milind Tambe. 12/2021. “Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes.” In MLPH: Machine Learning in Public Health NeurIPS 2021 Workshop.

Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, and Milind Tambe. 7/25/2021. “Contingency-Aware Influence Maximization: A Reinforcement Learning Approach.” In Conference on Uncertainty in Artificial Intelligence.

Aditya Mate, Andrew Perrault, and Milind Tambe. 5/7/2021. “Risk-Aware Interventions in Public Health: Planning with Restless Multi-Armed Bandits.” In 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). London, UK.

Jackson A Killian, Andrew Perrault, and Milind Tambe. 5/2021. “Beyond “To Act or Not to Act”: Fast Lagrangian Approaches to General Multi-Action Restless Bandits.” In 20th International Conference on Autonomous Agents and Multiagent Systems.

Ankit Bhardwaj*, Han Ching Ou*, Haipeng Chen, Shahin Jabbari, Milind Tambe, Rahul Panicker, and Alpan Raval. 11/2020. “Robust Lock-Down Optimization for COVID-19 Policy Guidance.” In AAAI Fall Symposium.

Aniruddha Adiga, Lijing Wang, Adam Sadilek, Ashish Tendulkar, Srinivasan Venkatramanan, Anil Vullikanti, Gaurav Aggarwal, Alok Talekar, Xue Ben, Jiangzhuo Chen, Bryan Lewis, Samarth Swarup, Milind Tambe, and Madhav Marathe. 6/5/2020. “Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics”. Publisher’s Version

Aditya Mate*, Jackson A. Killian*, Haifeng Xu, Andrew Perrault, and Milind Tambe. 12/5/2020. “Collapsing Bandits and Their Application to Public Health Interventions.” In Advances in Neural and Information Processing Systems (NeurIPS) 12/5/2020. Vancouver, Canada. Publisher’s Version

Jackson A. Killian, Marie Charpignon, Bryan Wilder, Andrew Perrault, Milind Tambe, and Maimuna S. Majumder. 8/24/2020. “Evaluating COVID-19 Lockdown and Business-Sector-Specific Reopening Policies for Three US States.” In KDD 2020 Workshop on Humanitarian Mapping. Publisher’s Version

Amulya Yadav, Leandro Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, and Heather Carmichael. 2015. “Preventing HIV Spread in Homeless Populations Using PSINET.” In Conference on Innovative Applications of Artificial Intelligence (IAAI-15).

Amulya Yadav, Ece Kamar, Barbara Grosz, and Milind Tambe. 2016. “HEALER: POMDP Planning for Scheduling Interventions among Homeless Youth (Demonstration).” In International conference on Autonomous Agents and Multiagent Systems.

Amulya Yadav, Hau Chan, Albert Jiang, Eric Rice, Ece Kamar, Barbara Grosz, and Milind Tambe. 2016. “POMDPs for Assisting Homeless Shelters – Computational and Deployment Challenges.” In AAMAS 2016 IDEAS Workshop.

Leandro Soriano Marcolino, Aravind Lakshminarayanan, Amulya Yadav, and Milind Tambe. 2016. “Simultaneous Influencing and Mapping Social Networks (Extended Abstract).” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Amulya Yadav, Hau Chan, Albert Xin Jiang, Haifeng Xu, Eric Rice, and Milind Tambe. 2016. “Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2016.

Amulya Yadav, Aida Rahmattalabi, Ece Kamar, Phebe Vayanos, Milind Tambe, and Venil Loyd Noronha. 2017. “Explanations Systems for Influential Maximizations Algorithms.” In 3rd International Workshop on Social Influence Analysis.

Amulya Yadav, Hau Chan, Albert Xin Jiang, Haifeng Xu, Eric Rice, and Milind Tambe. 2017. “Maximizing Awareness about HIV in Social Networks of Homeless Youth with Limited Information.” In International Joint Conference on Artificial Intelligence (IJCAI).

Bryan Wilder, Amulya Yadav, Nicole Immorlica, Eric Rice, and Milind Tambe. 2017. “Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network.” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).

Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, and Darlene Woo. 2017. “Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application.” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).

Aditya Mate, Jackson A. Killian, Bryan Wilder, Marie Charpignon, Ananya Awasthi, Milind Tambe, and Maimuna S. Majumder. 4/13/2020. “Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States.” SSRN. Publisher’s Version

Bryan Wilder, Nicole Immorlica, Eric Rice, and Milind Tambe. 2018. “Maximizing Influence in an Unknown Social Network.” In AAAI conference on Artificial Intelligence (AAAI-18).

Bryan Wilder, Han Ching Ou, Kayla de la Haye, and Milind Tambe. 2018. “Optimizing network structure for preventative health.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).

Bryan Wilder, Laura Onasch-Vera, Juliana Hudson, Jose Luna, Nicole Wilson, Robin Petering, Darlene Woo, Milind Tambe, and Eric Rice. 2018. “End-to-End Influence Maximization in the Field.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).

Lily Hu, Bryan Wilder, Amulya Yadav, Eric Rice, and Milind Tambe. 2018. “Activating the ‘Breakfast Club’: Modeling Influence Spread in Natural-World Social Networks.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).

Amulya Yadav, Ritesh Noothigattu, Eric Rice, Laura Onasch-Vera, Leandro Marcolino, and Milind Tambe. 2018. “Please be an influencer? Contingency Aware Influence Maximization.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).

Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, and Darlene Woo. 2018. “Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth.” In International Joint Conference on Artificial Intelligence (IJCAI).

Eric Rice, Amanda Yoshioka-Maxwell, Robin Petering, Laura Onasch-Vera, Jaih Craddock, Milind Tambe, Amulya Yadav, Bryan Wilder, Darlene Woo, Hailey Winetrobe, and Nicole Wilson. 2018. “Piloting the Use of Artificial Intelligence to Enhance HIV Prevention Interventions for Youth Experiencing Homelessness.” Journal of the Society for Social Work and Research, Volume 9, Number 4., 9, 4.

Aida Rahmattalabi, Anamika Barman Adhikari, Phebe Vayanos, Milind Tambe, Eric Rice, and Robin Baker. 2019. “Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study).” In Strategic Reasoning for Societal Challenges (SRSC) Workshop at International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19).

Jackson Killian, Bryan Wilder, Amit Sharma, Vinod Choudhary, Bistra Dilkina, and Milind Tambe. 8/4/2019. “Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data.” In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 8/4/2019.

Bryan Wilder, Marie Charpignon, Jackson A Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe, and Maimuna S. Majumder. 9/24/2020. “Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City.” Proceedings of the National Academy of Sciences. Publisher’s Version

Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, Alpan Raval, and Milind Tambe. 5/9/2020. “Who and When to Screen Multi-Round Active Screening for Network Recurrent Infectious Diseases Under Uncertainty.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-20).