Daniel M. Faissol

Portrait of  Daniel M. Faissol

  • Title
    Program Lead for predictive design of biologics
  • Email
  • Phone
    (925) 423-2544
  • Organization

Daniel Mello Faissol

Data and Decision Sciences Section, Computational Engineering Division, LLNL

Dr. Faissol is the program lead for predictive design of biologics at LLNL and leads LLNL’s initiative in deep reinforcement learning for national security applications. He currently serves on the LLNL Data Science Institute Council and as the lead for the computational and data science area within the LLNL Bioengineering Center. Dr. Faissol joined LLNL as postdoc in 2008 and served as group leader of the Operations Research and System Analysis group from 2013 to 2017. His current primary area of focus is in developing novel approaches for integrating artificial intelligence, simulation, and experimental evaluations to accelerate design and scientific discovery, primarily applied to antibody and vaccine antigen design.

Ph.D., Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 2008

M.S., Economics, Georgia Institute of Technology, Atlanta, GA, 2008

B.S., Electrical Engineering, University of California, Los Angeles, CA, 2003

  • Zhu, F., Bourguet, F., Bennett, D., Lau, E., Arrildt, K., Segelke, B., Zemla, A., Desautels, T., Faissol, D., Large-scale application of free energy perturbation calculations for antibody design Scientific reports 12, no. 1 (2022): 1-14.
  • Silva, F., Petersen, B., Desautels, T., Nguyen N., Vaschenko, D., Faissol, D.,  Improving Symbolic Optimization by Leveraging Language Models. Accepted at Workshop on Interpretable ML in Healthcare at International Conference on Machine Learning (ICML) 2021. Under review at ICML main proceedings
  • Mundhenk, T., Larma, B., Glatt, R., Santiago, C., Faissol, D., Petersen, Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. To appear in 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
  • Pettit, Jacob & Petersen, Brenden & Cockrell, Robert & Larie, Dale & Silva, Felipe & An, Gary & Faissol, Daniel. (2021). Learning Sparse Symbolic Policies for Sepsis Treatment. Workshop on Interpretable ML in Healthcare at International Conference on Machine Learning (ICML) 2021.
  • Larma, M. L., Petersen, B. K., Kim, S. K., Santiago, C. P., Glatt, R., Mundhenk, T. N., ... & Faissol, D. M. (2021). Improving exploration in policy gradient search: Application to symbolic optimization. Mathematical Reasoning in General Artificial Intelligence Workshop, International Conference on Learning Representations 2021.
  • Kim, S., Larma, M, Petersen, B., Santiago, C., Glatt, R., Mundhenk, T., Pettit, J., Faissol, D. Discovering symbolic policies with deep reinforcement learning. Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021
  • Yang, Jiachen, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal, Vladimir Tomov, Jean-Sylvain Camier et al. "Reinforcement Learning for Adaptive Mesh Refinement." arXiv preprint arXiv:2103.01342 (2021).
  • Desautels, Thomas, Adam Zemla, Edmond Lau, Magdalena Franco, and Daniel Faissol. "Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing." BioRxiv (2020).
  • Yang, J., Petersen, B., Zha, H., & Faissol, D. (2019). Single Episode Policy Transfer in Reinforcement Learning. arXiv preprint arXiv:1910.07719. International Conference on Learning Representations, April, 2020
  • Petersen, B. K., Yang, J., Grathwohl, W. S., Cockrell, C., Santiago, C., An, G., & Faissol, D. M. (2018). Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis.  International Conference on Machine Learning Workshop on Computational Biology, 2018, Journal of Computational Biology, 2019. https://arxiv.org/abs/1802.10440
  • Wheeler, Richard, et al. "Physics and optimal routing for urban radiation source search." Multisensor Fusion and Integration for Intelligent Systems (MFI), 2016 IEEE International Conference on. IEEE, 2016.
  • Ni, Kevin, D. Faissol, T. Edmunds, R. Wheeler, 2013, Exploitation of ambiguous cues to infer terrorist activity. Decision Analysis. 10(1)
  • Kirkizlar, H. E., D. Faissol, P. Griffin, and J. Swann, 2010, Timing of Testing and Treatment for Asymptomatic Diseases. Mathematical Biosciences, 226 (1), 28-37
  • Faissol, D., P. Griffin, and J. Swann, 2009, Bias in Markov Models of Disease. Mathematical Biosciences, 220(2):143156.
  • Edmunds, T., D. Faissol, and Y. Yao., 2009, Simulation Model of Mobile Detection Systems. Transactions of the American Nuclear Society, 100:307308.
  • Faissol, D., J. Swann, P. Griffin, B. Kolodziejski and T. Gift (2007). The Role of Bathhouses and Sex Clubs in HIV Transmission: Findings from a Mathematical Model. Journal of AIDS. 44(4): 386-394.