Nick Winovich

Scientific Machine Learning

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Scientific Machine Learning

Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185


Nick’s research focuses on the intersection of machine learning, probability theory, and partial differential equations with an emphasis on scientific and engineering applications. Prior to joining Sandia, he directed his research toward the construction of operator networks (neural networks designed to approximate operators rather than functions) endowed with predictive uncertainty estimates to help gauge the accuracy of model predictions. His current research is concentrated on the development of reinforcement learning techniques aimed to help guide policies and design strategies involving complex physical systems.


B.A. in Mathematics, University of Notre Dame, May 2012

M.S. in Mathematics, University of Oregon, May 2015

Ph.D. in Mathematics, Purdue University, August 2021



Nickolas Winovich, (2021). Deep Operator Network with Predictive Uncertainty Publication ID: 75936

Mohamed Ebeida, Ahmed Abdelkader, Nina Amenta, Drew Kouri, Ojas Parekh, Cynthia Phillips, Nickolas Winovich, (2020). Novel Geometric Operations for Linear Programming Publication ID: 71776

Nickolas Winovich, Ahmad Rushdi, Eric Phipps, Jaideep Ray, Guang Lin, Mohamed Ebeida, (2019). Rigorous Data Fusion for Computationally Expensive Simulations Publication ID: 64705

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