Exploitation of Ambiguous Cues to Infer Terrorist Activity. - Kevin S. Ni; Daniel M. Faissol; Thomas Edmunds; Richard Wheeler (Decis. Anal., 2013)

Physics and optimal routing for urban radiation source search. - Richard Wheeler; Daniel M. Faissol; Claudio Santiago; Thomas Baginski; Karl Nelson (MFI, 2016)

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis. - Brenden K. Petersen; Jiachen Yang; Will S. Grathwohl; Chase Cockrell; Claudio Santiago; Gary An; Daniel M. Faissol (CoRR, 2018)

Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. - Brenden K. Petersen; Jiachen Yang; Will S. Grathwohl; Chase Cockrell; Claudio Santiago; Gary An; Daniel M. Faissol (J. Comput. Biol., 2019)

Single Episode Policy Transfer in Reinforcement Learning. - Jiachen Yang; Brenden K. Petersen; Hongyuan Zha; Daniel M. Faissol (ICLR, 2020)

Discovering symbolic policies with deep reinforcement learning. - Mikel Landajuela; Brenden K. Petersen; Sookyung Kim; Cláudio P. Santiago; Ruben Glatt; T. Nathan Mundhenk; Jacob F. Pettit; Daniel M. Faissol (ICML, 2021)

Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. - T. Nathan Mundhenk; Mikel Landajuela; Ruben Glatt; Cláudio P. Santiago; Daniel M. Faissol; Brenden K. Petersen (NeurIPS, 2021)

Improving exploration in policy gradient search: Application to symbolic optimization. - Mikel Landajuela; Brenden K. Petersen; Soo K. Kim; Cláudio P. Santiago; Ruben Glatt; T. Nathan Mundhenk; Jacob F. Pettit; Daniel M. Faissol (CoRR, 2021)

Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. - T. Nathan Mundhenk; Mikel Landajuela; Ruben Glatt; Cláudio P. Santiago; Daniel M. Faissol; Brenden K. Petersen (CoRR, 2021)

Reinforcement Learning for Adaptive Mesh Refinement. - Jiachen Yang; Tarik Dzanic; Brenden K. Petersen; Jun Kudo; Ketan Mittal; Vladimir Z. Tomov; Jean-Sylvain Camier; Tuo Zhao; Hongyuan Zha; Tzanio V. Kolev; Robert W. Anderson; Daniel M. Faissol (AISTATS, 2023)

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. - Jiachen Yang; Ketan Mittal; Tarik Dzanic; Socratis Petrides; Brendan Keith; Brenden K. Petersen; Daniel M. Faissol; Robert W. Anderson (AAMAS, 2023)

DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces. - Jacob F. Pettit; Chak Shing Lee; Jiachen Yang; Alex Ho; Daniel M. Faissol; Brenden K. Petersen; Mikel Landajuela (AAAI, 2025)

Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics. - Conor F. Hayes; Felipe Leno da Silva; Jiachen Yang; T. Nathan Mundhenk; Chak Shing Lee; Jacob F. Pettit; Claudio Santiago; Sookyung Kim; Joanne Taery Kim; Ignacio Aravena Solis; Ruben Glatt; Andre R. Goncalves; et al. (CoRR, 2025)