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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2510.15217 |
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| author | Alsentzer, Emily Charpignon, Marie-Laure Chen, Bill D'Souza, Niharika Fries, Jason Jiang, Yixing Kashyap, Aparajita Kim, Chanwoo Lee, Simon Mandyam, Aishwarya Mbilinyi, Ashery Mehandru, Nikita Nagesh, Nitish Nuwagira, Brighton Pierson, Emma Pillai, Arvind Sano, Akane Syeda-Mahmood, Tanveer Yadav, Shashank Adhanom, Elias Afza, Muhammad Umar Archer, Amelia Bedi, Suhana Bikia, Vasiliki Chang, Trenton Chen, George H. Chen, Winston Chiang, Erica Choi, Edward Ciora, Octavia Dozie-Nnamah, Paz Elsharief, Shaza Engelhard, Matthew Eshragh, Ali Feng, Jean Fessel, Josh Fleming, Scott Fong, Kei Sen Frost, Thomas Gadgil, Soham Gichoya, Judy Hershkovich, Leeor Im, Sujeong Jain, Bhavya Jeanselme, Vincent Jia, Furong Jin, Qixuan Jin, Yuxuan Kapash, Daniel Kapoor, Geetika Kiafar, Behdokht Kleiner, Matthias Kraft, Stefan Kumar, Annika Kyung, Daeun Liang, Zhongyuan Lin, Joanna Liu, Qianchu Liu, Chang Luan, Hongzhou Lunt, Chris López, Leopoldo Julían Lechuga McDermott, Matthew B. A. Noroozizadeh, Shahriar O'Brien, Connor Oh, YongKyung Ota, Mixail Pfohl, Stephen Pi, Meagan Pias, Tanmoy Sarkar Rocheteau, Emma Sethi, Avishaan Shirakawa, Toru Silver, Anita Simha, Neha Stankeviciute, Kamile Sunog, Max Szolovits, Peter Tang, Shengpu Tang, Jialu Tierney, Aaron Valdovinos, John Wallace, Byron Wang, Will Ke Washington, Peter Weiss, Jeremy Wolfe, Daniel Wong, Emily Yun, Hye Sun Zhang, Xiaoman Zhang, Xiao Yu Cindy Jeong, Hayoung Thakoor, Kaveri A. |
| author_facet | Alsentzer, Emily Charpignon, Marie-Laure Chen, Bill D'Souza, Niharika Fries, Jason Jiang, Yixing Kashyap, Aparajita Kim, Chanwoo Lee, Simon Mandyam, Aishwarya Mbilinyi, Ashery Mehandru, Nikita Nagesh, Nitish Nuwagira, Brighton Pierson, Emma Pillai, Arvind Sano, Akane Syeda-Mahmood, Tanveer Yadav, Shashank Adhanom, Elias Afza, Muhammad Umar Archer, Amelia Bedi, Suhana Bikia, Vasiliki Chang, Trenton Chen, George H. Chen, Winston Chiang, Erica Choi, Edward Ciora, Octavia Dozie-Nnamah, Paz Elsharief, Shaza Engelhard, Matthew Eshragh, Ali Feng, Jean Fessel, Josh Fleming, Scott Fong, Kei Sen Frost, Thomas Gadgil, Soham Gichoya, Judy Hershkovich, Leeor Im, Sujeong Jain, Bhavya Jeanselme, Vincent Jia, Furong Jin, Qixuan Jin, Yuxuan Kapash, Daniel Kapoor, Geetika Kiafar, Behdokht Kleiner, Matthias Kraft, Stefan Kumar, Annika Kyung, Daeun Liang, Zhongyuan Lin, Joanna Liu, Qianchu Liu, Chang Luan, Hongzhou Lunt, Chris López, Leopoldo Julían Lechuga McDermott, Matthew B. A. Noroozizadeh, Shahriar O'Brien, Connor Oh, YongKyung Ota, Mixail Pfohl, Stephen Pi, Meagan Pias, Tanmoy Sarkar Rocheteau, Emma Sethi, Avishaan Shirakawa, Toru Silver, Anita Simha, Neha Stankeviciute, Kamile Sunog, Max Szolovits, Peter Tang, Shengpu Tang, Jialu Tierney, Aaron Valdovinos, John Wallace, Byron Wang, Will Ke Washington, Peter Weiss, Jeremy Wolfe, Daniel Wong, Emily Yun, Hye Sun Zhang, Xiaoman Zhang, Xiao Yu Cindy Jeong, Hayoung Thakoor, Kaveri A. |
| contents | The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dialogue around critical, timely topics at the intersection of machine learning and healthcare. Each roundtable was moderated by a team of senior and junior chairs who fostered open exchange, intellectual curiosity, and inclusive engagement. The sessions emphasized rigorous discussion of key challenges, exploration of emerging opportunities, and collective ideation toward actionable directions in the field. In total, eight roundtables were held by 19 roundtable chairs on topics of "Explainability, Interpretability, and Transparency," "Uncertainty, Bias, and Fairness," "Causality," "Domain Adaptation," "Foundation Models," "Learning from Small Medical Data," "Multimodal Methods," and "Scalable, Translational Healthcare Solutions." |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15217 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025 Alsentzer, Emily Charpignon, Marie-Laure Chen, Bill D'Souza, Niharika Fries, Jason Jiang, Yixing Kashyap, Aparajita Kim, Chanwoo Lee, Simon Mandyam, Aishwarya Mbilinyi, Ashery Mehandru, Nikita Nagesh, Nitish Nuwagira, Brighton Pierson, Emma Pillai, Arvind Sano, Akane Syeda-Mahmood, Tanveer Yadav, Shashank Adhanom, Elias Afza, Muhammad Umar Archer, Amelia Bedi, Suhana Bikia, Vasiliki Chang, Trenton Chen, George H. Chen, Winston Chiang, Erica Choi, Edward Ciora, Octavia Dozie-Nnamah, Paz Elsharief, Shaza Engelhard, Matthew Eshragh, Ali Feng, Jean Fessel, Josh Fleming, Scott Fong, Kei Sen Frost, Thomas Gadgil, Soham Gichoya, Judy Hershkovich, Leeor Im, Sujeong Jain, Bhavya Jeanselme, Vincent Jia, Furong Jin, Qixuan Jin, Yuxuan Kapash, Daniel Kapoor, Geetika Kiafar, Behdokht Kleiner, Matthias Kraft, Stefan Kumar, Annika Kyung, Daeun Liang, Zhongyuan Lin, Joanna Liu, Qianchu Liu, Chang Luan, Hongzhou Lunt, Chris López, Leopoldo Julían Lechuga McDermott, Matthew B. A. Noroozizadeh, Shahriar O'Brien, Connor Oh, YongKyung Ota, Mixail Pfohl, Stephen Pi, Meagan Pias, Tanmoy Sarkar Rocheteau, Emma Sethi, Avishaan Shirakawa, Toru Silver, Anita Simha, Neha Stankeviciute, Kamile Sunog, Max Szolovits, Peter Tang, Shengpu Tang, Jialu Tierney, Aaron Valdovinos, John Wallace, Byron Wang, Will Ke Washington, Peter Weiss, Jeremy Wolfe, Daniel Wong, Emily Yun, Hye Sun Zhang, Xiaoman Zhang, Xiao Yu Cindy Jeong, Hayoung Thakoor, Kaveri A. Machine Learning The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dialogue around critical, timely topics at the intersection of machine learning and healthcare. Each roundtable was moderated by a team of senior and junior chairs who fostered open exchange, intellectual curiosity, and inclusive engagement. The sessions emphasized rigorous discussion of key challenges, exploration of emerging opportunities, and collective ideation toward actionable directions in the field. In total, eight roundtables were held by 19 roundtable chairs on topics of "Explainability, Interpretability, and Transparency," "Uncertainty, Bias, and Fairness," "Causality," "Domain Adaptation," "Foundation Models," "Learning from Small Medical Data," "Multimodal Methods," and "Scalable, Translational Healthcare Solutions." |
| title | Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025 |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.15217 |