Profile Picture

Jackson A Killian

  jkillian at



  [Google Scholar]

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Dec 17: Accepted to AAMAS! Work extending Restless Bandits past the paradigm of "to act or not to act" by allowing for multiple action choices per arm. Relevant for planning in domains where multiple intervention options are available to one resource-constrained planner (e.g., community health care). Paper forthcoming!

Sept 25: Accepted to NeurIPS! Co-first author work with Aditya Mate developing theory for Collapsing Bandits, a new subclass of Restless Bandits. Simulation evaluations on a real-world-derived public health challenge. Extensions to follow [preprint]

Sept 24: Accepted to PNAS! Our COVID-19 modeling work of between-population variation of disease dynamics by accounting for country-specific demographics, contact patterns, comorbidities, and household structures [publication] [code]

April 30: Live interview on ABC-7 WJLA about our work modeling COVID-19 [video]

April 28: (COVID-19) See our collaborative work with The Daily Beast to evaluate Georgia's proposed reopening policies. Great experience working the journalists on The Daily Beast team! [article] [preprint]

Jackson presenting at workshop in India

I am a PhD student in Computer Science at Harvard University. Advised by Prof. Milind Tambe, I study how AI techniques can solve challenges in public health, with a technical focus in machine learning and sequential decision making. Using these approaches, I have developed predictive models and sequential planning tools for community health workers in India combatting tuberculosis. Part of this work I conducted from Banglore while on a research internship with Microsoft Research India, where I was grateful to be advised by Dr. Amit Sharma. Currently, I am very proud and fortunate to be supported by a 2019-'22 National Science Foundation Graduate Research Fellowship.

Previously, I have worked on building agent-based models for simulating non-pharmacological interventions for preventing the spread of COVID-19 and designed machine learning models for detecting sobriety using mobile health data (check out the dataset!) During my undergraduate degree at The Ohio State Univeristy, I studied computational genetics as a Pelotonia fellow.

Outside of research, I co-direct the Harvard AI in Healthcare Group, a cross-sectional organization that brings together students, researchers and entrepreneurs from across disciplines to explore opportunities and hear from leading experts in this exciting space. I also co-organize the Harvard CRCS Rising Stars Workshop which seeks to highlight and provide networking and mentorship opportunities to budding junior researchers dedicated to studying AI for Social Impact.


* equal contribution
  • AAMAS 2021: Killian JA, Perrault A, Tambe M. Beyond "To Act or Not to Act": Fast Lagrangian Approaches to General Multi-Action Restless Bandits. 20th International Conference on Autonomous Agents and Multiagent Systems. 2021 May. [publication forthcoming]
  • NeurIPS 2020: Mate A*, Killian JA*, Xu H, Perrault A, Tambe M. Collapsing Bandits and Their Application to Public Health Interventions. Neural Information Processing Systems (NeurIPS). 2020 December. [NeurIPS preprint]
  • PNAS 2020: Wilder B, Charpignon M, Killian JA, Ou HC, Mate A, Jabbari S, Perrault A, Desai A, Tambe M, Majumder MS. Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City. Proceedings of the National Academy of Sciences. 2020 September 24. [publication]
  • SSRN 2020: Killian JA, Charpignon M, Wilder B, Perrault A, Tambe M, Majumder MS. Evaluating COVID-19 Lockdown and Reopening Scenarios For Georgia, Florida, and Mississippi. Available at SSRN 3598744. 2020 May 12. [workshop paper]
  • SSRN 2020: Mate A, Killian JA, Wilder B, Charpignon M, Awasthi A, Tambe M, Majumder MS. Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States. Available at SSRN 3575207. 2020. [preprint]
  • KDD 2019: Killian JA, Wilder B, Sharma A, Choudhary V, Dilkina B, Tambe M. “Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. doi: 10.1145/3292500.3330777 [publication]
  • IJCAI-KDH 2019: Killian JA, Passino K, Nandi A, Madden D, Clapp J. “Learning to Detect Heavy Drinking Episodes Using Smartphone Accelerometer Data” Proceedings of the 4th International Workshop on Knowledge Discovery in Healthcare Data. 2019. [publication v1] [publication v2] [dataset]
  • BMC Genomics 2018: Killian JA, Topiwala T, Pelletier A, Frankhouser D, Yan P, Bundschuh R. "FuSpot: A Web-based Tool for Visual Evaluation of Fusion Candidates" BMC Genomics. 2018. 19:139. doi: 10.1186/s12864-018-4486-3 [publication] [slides] [website]
  • Thyroid 2018: He H, Li W, Yan P, Bundschuh R, Killian JA, Labanowska J, Brock P, et al. "Identification of a recurrent LMO7-BRAF fusion in papillary thyroid carcinoma" Thyroid. 2018. doi: 10.1089/thy.2017.0258. [publication]


  • Aria Bendix. "Four Days of Work, Followed by 10 Days of Lockdown Could Help Prevent Another Wave of Infections." Business Insider France, May 25, 2020. [article]
  • Leah Burrows. "What is the Right Strategy to Limit the Spread of COVID-19?" Medical Xpress, May 4, 2020. [article]
  • "Models for the Spread of COVID-19." Live interview on ABC-7 WJLA. April 30, 2020. [video]
  • Amanda Mull. "Georgia’s Experiment in Human Sacrifice." The Atlantic, April 29, 2020. [article]
  • William Bredderman and Olivia Messer. "New Model Shows How Deadly Lifting Georgia’s Lockdown May Be." Daily Beast, April 28, 2020. [article]
  • Subhra Priyadarshini. "Model Finds 'Middle Ground' for India's Lockdown Exit." Nature India. April 27, 2020. [article]
  • Pelotonia. "Pelotonia Investment Report (2017)." The James Ohio State University Comprehensive Cancer Center. May 2017. [report]