Desmarais

Andrew O'Brien

Technical Advisor, New York
PHONE: 212-808-2926 FAX: 212-351-3401
aobrien@desmaraisllp.com
Andrew O’Brien is a Technical Advisor at Desmarais LLP, where he supports the firm’s intellectual property litigation practice with a focus on complex technologies in artificial intelligence, computer science, and machine learning. He brings deep technical expertise developed through advanced academic research and prior litigation experience at a top-tier IP practice.

Prior to joining Desmarais, Andrew was a Technical Advisor in the IP litigation group at Ropes & Gray LLP, where he assisted in all stages of patent litigation, including developing invalidity and non-infringement arguments, drafting IPR petitions and responses, and collaborating with technical experts. He also contributed to thought leadership initiatives, including podcast content on AI and digital health, and was an active member of the firm’s Artificial Intelligence Working Group.

Andrew earned his Ph.D. in Computer Science from Drexel University, where his research focused on causality, machine learning, and symbolic regression. He authored multiple peerreviewed publications on interpretable AI, neuromorphic systems, and causal discovery. He also holds a joint M.S. in Economics and Computer Science, and a B.S. in Computer Science, both conferred summa cum laude.

Andrew is currently preparing to sit for the Patent Bar and plans to attend law school in the fall of 2025.

Prior Experience

  • Technical Advisor, Ropes & Gray LLP, New York, New York, 2024-2025

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News & Publications

  • O’Brien, R. Weber, and E. Kim, “Basis learning for dynamical systems in the presence of incomplete scientific knowledge,” 2023 IEEE Sixth International Conference on Artificial and Knowledge Engineering (AIKE), Laguna Hills, CA, USA (2023)
  • G. Parpart, C. Gonzalez Rivera, T. Stewart, E. Kim, J. Rego, A. O’Brien, S. Nesbit, G. Kenyon, and Y. Watkins, “Dictionary learning with accumulator neurons,” 2023 IEEE Sixth International Conference on Artificial and Knowledge Engineering (AIKE), Laguna Hills, CA, USA (2023)
  • O’Brien, R. Weber, and E. Kim, “Investigating causally augmented sparse learning as a tool for meaningful classification,” 2023 IEEE Sixth International Conference on Artificial and Knowledge Engineering (AIKE), Laguna Hills, CA, USA (2023)
  • Cited, “A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations,” ACM Comput. Surv. 55, 5, Article 95 (May 2023)
  • O’Brien, R. Weber, and E. Kim, “Investigating sindy as a tool for causal discovery in time series signals,” ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)(2023)
  • O’Brien and E. Kim, “Toward multi-agent algorithmic recourse: Challenges from a game-theoretic perspective,” The International FLAIRS Conference Proceedings, Volume 35 (May 2022)
  • Onweller, A. O’Brien, E. Kim, and K.F. McCoy, “Distributional semantics of line charts for trend classification,” Advances in Visual Computing: 17 International Symposium, ISVC 2022, San Diego, CA, USA, October 3-5,2022, Proceedings, Part II (2022)
  • S.C. Nesbit, A. O’Brien, J. Rego, G. Parpart, C. Gonzalez, G.T. Kenyon, E. Kim, T.C. Stewart, and Y. Watkins, “Think Fast: Time control in varying paradigms of spiking neural networks,” Proceedings of the International Conference on Neuromorphic Systems 2022, ICONS’22, New York (2022)

EDUCATION

Drexel University, Ph.D. Computer Science, 2023; Thesis: Dynamic Causality: Sparse Symbolic Regression as a Tool to Learn Causal Dynamic Structural Equations with Applications to Counterfactuals

Drexel University, M.S., Joint Economics & Computer Science, 2020; summa cum laude

Moravian University, B.S., Computer Science, Minor in Mathematics, 2018; summa cum laude
 

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