Daniel M. Faissol

According to our database, Daniel M. Faissol authored at least 16 papers between 2013 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Machine Learning Models Assisting the Development of Antibody Therapeutics and Vaccines - an Emerging Trend.

Felipe Leno da Silva, Mikel Landajuela, Edwin A. Saada, Piyush Karande, Sudeep Sarma, Igor D'Angelo, Simone Conti, Daniel M. Faissol

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Language model-accelerated deep symbolic optimization.

Felipe Leno da Silva, Andre R. Goncalves, Sam Nguyen, Denis Vashchenko, Ruben Glatt, Thomas Desautels, Mikel Landajuela, Daniel M. Faissol, Brenden K. Petersen

Neural Comput. Appl., November, 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, Alexander Ladd, Ahmet Can Solak, Thomas Desautels, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela

CoRR, May, 2025

Design of cross-reactive antigens with machine learning and high-throughput experimental evaluation.

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

Frontiers Bioinform., 2025

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

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

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

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

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

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 Deep Reinforcement Learning Enhanced Genetic Programming Seeding.

T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Single Episode Policy Transfer in Reinforcement Learning.

Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

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

2018

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

2016

Physics and optimal routing for urban radiation source search.

Richard Wheeler, Daniel M. Faissol, Claudio Santiago, Thomas Baginski, Karl Nelson

Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2016

2013

Exploitation of Ambiguous Cues to Infer Terrorist Activity.

Kevin S. Ni, Daniel M. Faissol, Thomas Edmunds, Richard Wheeler

Decis. Anal., 2013