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Jackson A Killian

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  [killian-34]


I am a machine learning scientist at Harbinger Health with 7 years of experience in AI research. At Harbinger, I design ML architectures to characterize cancer signal in blood using DNA sequencing data, supporting the development of a simple and non-invasive blood test for early-stage cancer.


I completed my PhD in computer science at Harvard University in 2023 as a National Science Foundation Fellow advised by Milind Tambe. My research focused on the intersection of machine learning and sequential decision making, with an applied focus on helping community health workers solve challenges in public health (such as tuberculosis medication adherence and diabetes care management). Part of this work I conducted on research internships with Microsoft Research India (with Amit Sharma), Google Research (with Philip Nelson and Manish Jain) and Verily (with Yugang Jia).


During my undergraduate degree at The Ohio State Univeristy (2018), I conducted research in computational biology as a Pelotonia cancer research fellow advised by Ralf Bundschuh and Pearlly Yan. I also designed a study to collect data and train a machine learning model for detecting sobriety using mobile health data (check out the dataset!)


Publications

  • JMIR 2024: Killian JA, Jain M, Jia Y, Amar J, Huang E, Tambe M. New Approach to Equitable Intervention Planning to Improve Engagement and Outcomes in a Digital Health Program: Simulation Study. Journal of Medical Internet Research (JMIR) Diabetes. 2024 March. [publication]
  • IJCAI 2023: Gordon L, Behari N, Collier S, Bondi-Kelly E, Killian JA, Ressijac C, Boucher P, Davies A, Tambe M. Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats. International Joint Conference on Artificial Intelligence. 2023 August. [preprint] [publication]
  • IJCAI 2023: Danassis P, Verma S, Killian JA, Taneja A, Tambe M. Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare. International Joint Conference on Artificial Intelligence. 2023 August. [preprint] [publication]
  • AAMAS 2023: Biswas A, Killian JA, Rodriguez Diaz P, Ghosh S, Tambe M. Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks. Conference on Autonomous Agents and Multiagent Systems. 2023 May. [preprint] [publication]
  • AAAI 2023: Killian JA,* Biswas A,* Xu L,* Verma S,* Nair V, Taneja A, Hegde A, Madhiwalla N, Rodriguez Diaz P, Johnson-Yu S, Tambe M. Robust Planning over Restless Groups: Engagement Interventions for a Large-Scale Maternal Telehealth Program. AAAI Conference on Artificial Intelligence. 2023 Feb. [publication]
  • AAAI 2023: Rodriguez Diaz P, Killian JA, Xu L, Taneja A, Suggala AS, Tambe M. Flexible Budgets in Restless Bandits: A Primal-Dual Algorithm for Efficient Budget Allocation. AAAI Conference on Artificial Intelligence. 2023 Feb. [publication]
  • UAI 2022: Killian JA, Xu L, Biswas A, Tambe M. Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning. Conference on Uncertainty in Artificial Intelligence. 2022 August. [publication]
  • AAMAS 2022: Ou HC, Siebenbrunner C, Killian JA, Brooks MB, Kempe D, Vorobeychik Y, Tambe M. Networked Restless Multi-Armed Bandits for Mobile Interventions. Conference on Autonomous Agents and Multiagent Systems. 2022 May. [publication]
  • KDD 2021: Killian JA, Biswas A, Shah S, Tambe M. Q-Learning Lagrange Policies for Multi-Action Restless Bandits. Conference on Knowledge Discovery & Data Mining. 2021 August. [publication] [code]
  • AIES 2021: Bondi E,* Xu L,* Acosta-Navas D, Killian JA. Envisioning Communities: A Participatory Approach Towards AI for Social Good. AAAI/ACM Conference on AI, Ethics, and Society. 2021 July. [publication]
  • AAMAS 2021: Killian JA, Perrault A, Tambe M. Beyond "To Act or Not to Act": Fast Lagrangian Approaches to General Multi-Action Restless Bandits. Conference on Autonomous Agents and Multiagent Systems. 2021 May. [publication] [code]
  • 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. [publication] [code]
  • 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. 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. http://ceur-ws.org/Vol-2429/paper6.pdf [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]

Media

  • 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]
* equal contribution