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Jian-Xun Wang

Associate Professor

Sibley School of Mechanical and Aerospace Engineering

Jian Xun Wang holding a coffee cup standing in a lap
Jian Xun Wang holding a coffee cup standing in a lap

Biography

Dr. Wang is an Associate Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University, starting in 2025. Previously, he held the position of Robert W. Huether Collegiate Associate Professor in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. He received his Ph.D. in Aerospace Engineering from Virginia Tech in 2017 and completed a postdoctoral training at UC Berkeley before joining Notre Dame as a tenure-track Assistant Professor in 2018. Dr. Wang has a multidisciplinary research background that spans Scientific Machine Learning, Bayesian Data Assimilation, Differentiable Programming, Uncertainty Quantification, and Computational Fluid Dynamics. He directs the Computational Mechanics & Scientific AI Lab (CoMSAIL), which is funded by multiple agencies such as NSF, NIH, ONR, AFOSR, DARPA, Google, and others.

Research Interests

Dr. Wang’s research focuses on computational science and engineering, leveraging the synergy of advanced AI/ML, heterogeneous data, numerical techniques, and high-performance GPU computing. His work emphasizes the deep integration of AI/ML techniques with physics-based models and advanced numerical methods, aiming to revolutionize computational modeling in the era of big data and significantly enhance predictive simulation capabilities. His research aims to improve the understanding, prediction, and control of complex physical systems, with applications spanning personalized biomedicine, aero/hydrodynamics, and advanced manufacturing.

  • Artificial Intelligence
  • Statistics and Machine Learning
  • Data Science
  • Scientific Computing
  • Computational Fluid Dynamics
  • Computational Mechanics
  • Turbulence
  • Aerodynamics and Aeroacoustics
  • Biomechanics and Mechanobiology
  • Biomedical Engineering
  • Signal and Image Processing

Teaching Interests

Computational Fluid Dynamics, Data Assimilation, Scientific Machine Learning, Numerical Methods, Bayesian Learning, Uncertainty Quantification, Fluid Mechanics.

Select Publications

Select Awards and Honors

  • ONR Young Investigate Program (YIP) Award 2023
  • NSF CAREER Award 2021
  • TAML Highest Citation Paper Award 2021

Education

  • M.S. (Ocean Engineering), Virginia Tech 2016
  • Ph.D. (Aerospace Engineering), Virginia Tech 2017
  • Postdoc (Bioengineering), UC Berkeley 2018