Daniel M. Faissol

20 papers A* 5A 2Journal 12Unranked 1
YearRankTypeTitle / Venue / Authors
2026 A* conf
Machine Learning Models Assisting the Development of Antibody Therapeutics and Vaccines - an Emerging Trend.
AAAI
Felipe Leno da Silva, Mikel Landajuela, Edwin A. Saada, Piyush Karande, Sudeep Sarma, Igor D'Angelo, Simone Conti, Daniel M. Faissol
2025 J jnl
Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics.
CoRR
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, Alexander Ladd, Ahmet Can Solak, Thomas Desautels, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela
2025 J jnl
Design of cross-reactive antigens with machine learning and high-throughput experimental evaluation.
Frontiers Bioinform.
Chelsy Chesterman, Thomas Desautels, Luz-Jeannette Sierra, Kathryn T. Arrildt, Adam T. Zemla, Edmond Y. Lau, Shivshankar Sundaram, Jason Laliberte, Lynn Chen, Aaron Ruby, Mark Mednikov, Sylvie Bertholet, Dong Yu, Kate Luisi, Enrico Malito, Corey P. Mallett, Matthew J. Bottomley, Robert A. van den Berg, Daniel M. Faissol
2025 A* conf
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces.
AAAI
Jacob F. Pettit, Chak Shing Lee, Jiachen Yang, Alex Ho, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela
2025 J jnl
Language model-accelerated deep symbolic optimization.
Neural Comput. Appl.
Felipe Leno da Silva, Andre R. Goncalves, Sam Nguyen, Denis Vashchenko, Ruben Glatt, Thomas Desautels, Mikel Landajuela, Daniel M. Faissol, Brenden K. Petersen
2024 J jnl
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces.
CoRR
Jacob F. Pettit, Chak Shing Lee, Jiachen Yang, Alex Ho, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela
2023 A conf
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement.
AAMAS
Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson
2023 A conf
Reinforcement Learning for Adaptive Mesh Refinement.
AISTATS
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
2022 J jnl
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement.
CoRR
Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson
2021 A* conf
Discovering symbolic policies with deep reinforcement learning.
ICML
Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol
2021 J jnl
Improving exploration in policy gradient search: Application to symbolic optimization.
CoRR
Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol
2021 J jnl
Reinforcement Learning for Adaptive Mesh Refinement.
CoRR
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
2021 A* conf
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding.
NeurIPS
T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen
2021 J jnl
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding.
CoRR
T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen
2020 A* conf
Single Episode Policy Transfer in Reinforcement Learning.
ICLR
Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol
2019 J jnl
Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine.
J. Comput. Biol.
Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol
2019 J jnl
Single Episode Policy Transfer in Reinforcement Learning.
CoRR
Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol
2018 J jnl
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis.
CoRR
Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol
2016 — conf
Physics and optimal routing for urban radiation source search.
MFI
Richard Wheeler, Daniel M. Faissol, Claudio Santiago, Thomas Baginski, Karl Nelson
2013 J jnl
Exploitation of Ambiguous Cues to Infer Terrorist Activity.
Decis. Anal.
Kevin S. Ni, Daniel M. Faissol, Thomas Edmunds, Richard Wheeler