Active Laser Radar for High Performance Measurements. - John A. Hancock; Dirk Langer; Martial Hebert; Ryan Sullivan; Darin Ingimarson; Eric Hoffmann; Markus Mettenleiter; Christoph Fröhlich (ICRA, 1998)

Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks. - Robert Leaman; Laura Wojtulewicz; Ryan Sullivan; Annie Skariah; Jian Yang 026; Graciela Gonzalez 001 (BioNLP@ACL, 2010)

Efficient Extraction of Protein-Protein Interactions from Full-Text Articles. - Jörg Hakenberg; Robert Leaman; Nguyen Ha Vo; Siddhartha Jonnalagadda; Ryan Sullivan; Christopher Miller; Luis Tari; Chitta Baral; Graciela Gonzalez 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2010)

The DIEGO Lab Graph Based Gene Normalization System. - Ryan Sullivan; Robert Leaman; Graciela Gonzalez 001 (ICMLA (2), 2011)

Text Classification towards Detecting Misdiagnosis of an Epilepsy Syndrome in a Pediatric Population. - Ryan Sullivan; Robert Yao; Randa Jarar; Jeffrey Buchhalter; Graciela Gonzalez 001 (AMIA, 2014)

Finding Potentially Unsafe Nutritional Supplements from User Reviews with Topic Modeling. - Ryan Sullivan; Abeed Sarker; Karen O'Connor; Amanda Goodin; Mark Karlsrud; Graciela Gonzalez 001 (PSB, 2016)

Deep learning methods for segmentation of lines in pediatric chest radiographs. - Ryan Sullivan; Gregory Holste; Jonathan Burkow; Adam M. Alessio (Computer-Aided Diagnosis, 2020)

Multi-class semantic segmentation of pediatric chest radiographs. - Gregory Holste; Ryan Sullivan; Michael D. Bindschadler; Nicholas Nagy; Adam M. Alessio (Image Processing, 2020)

PettingZoo: Gym for Multi-Agent Reinforcement Learning. - Justin K. Terry; Benjamin Black; Nathaniel Grammel; Mario Jayakumar; Ananth Hari; Ryan Sullivan; Luis S. Santos; Clemens Dieffendahl; Caroline Horsch; Rodrigo Perez-Vicente; Niall L. Williams; Yashas Lokesh; et al. (NeurIPS, 2021)

Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments. - Ryan Sullivan; Jordan K. Terry; Benjamin Black; John P. Dickerson (ICML, 2022)

Tracking Large Class Projects in Real-Time Using Fine-Grained Source Control. - Gustavo Rodriguez-Rivera; Jeffrey A. Turkstra; Jordan Buckmaster; Killian LeClainche; Shawn Montgomery; William Reed; Ryan Sullivan; Jarett Lee (SIGCSE (1), 2022)

MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning. - Dongnan Liu; Mariano Cabezas; Dongang Wang; Zihao Tang; Lei Bai 001; Geng Zhan; Yuling Luo; Kain Kyle; Linda Ly; James Yu; Chun-Chien Shieh; Aria Nguyen; et al. (CoRR, 2022)

Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments. - Ryan Sullivan; Justin K. Terry; Benjamin Black; John P. Dickerson (CoRR, 2022)

Gradient Informed Proximal Policy Optimization. - Sanghyun Son 003; Laura Yu Zheng; Ryan Sullivan; Yi-Ling Qiao; Ming C. Lin (NeurIPS, 2023)

Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning. - Joseph Suarez; David Bloomin; Kyoung Whan Choe; Hao Xiang Li; Ryan Sullivan; Nishaanth Kanna; Daniel Scott; Rose S. Shuman; Herbie Bradley; Louis Castricato; Phillip Isola; Chenghui Yu; et al. (NeurIPS, 2023)

Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks. - Ryan Sullivan; Akarsh Kumar; Shengyi Huang; John P. Dickerson; Joseph Suarez (NeurIPS, 2023)

Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning. - Joseph Suárez; Phillip Isola; Kyoung Whan Choe; David Bloomin; Hao Xiang Li; Nikhil Pinnaparaju; Nishaanth Kanna; Daniel Scott; Ryan Sullivan; Rose S. Shuman; Lucas de Alcântara; Herbie Bradley; et al. (CoRR, 2023)

Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (EMNLP (Findings), 2024)

Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training. - Lei Bai 001; Dongang Wang; Hengrui Wang; Michael Barnett 006; Mariano Cabezas; Weidong Cai 001; Fernando Calamante; Kain Kyle; Dongnan Liu; Linda Ly; Aria Nguyen; Chun-Chien Shieh; et al. (Artif. Intell. Medicine, 2024)

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning. - Shengyi Huang; Quentin Gallouédec; Florian Felten; Antonin Raffin; Rousslan Fernand Julien Dossa; Yanxiao Zhao; Ryan Sullivan; Viktor Makoviychuk; Denys Makoviichuk; Mohamad H. Danesh; Cyril Roumégous; Jiayi Weng; et al. (CoRR, 2024)

How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks. - Dongang Wang; Peilin Liu; Hengrui Wang; Heidi Beadnall; Kain Kyle; Linda Ly; Mariano Cabezas; Geng Zhan; Ryan Sullivan; Tom Weidong Cai; Wanli Ouyang; Fernando Calamante; et al. (CoRR, 2024)

Massively Multiagent Minigames for Training Generalist Agents. - Kyoung Whan Choe; Ryan Sullivan; Joseph Suárez (CoRR, 2024)

Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (CoRR, 2024)

Syllabus: Portable Curricula for Reinforcement Learning Agents. - Ryan Sullivan; Ryan Pégoud; Ameen Ur Rahmen; Xinchen Yang; Junyun Huang; Aayush Verma; Nistha Mitra; John P. Dickerson (CoRR, 2024)

Robust Multi-Objective Preference Alignment with Online DPO. - Raghav Gupta; Ryan Sullivan; Yunxuan Li; Samrat Phatale; Abhinav Rastogi (AAAI, 2025)

Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning. - Joseph Suárez; Kyoung Whan Choe; David Bloomin; Jianming Gao; Yunkun Li; Yao Feng; Saidinesh Pola; Kun Zhang; Yonghui Zhu; Nikhil Pinnaparaju; Hao Xiang Li; Nishaanth Kanna; et al. (CoRR, 2025)