Peetak Mitra, Ph.D.


3333 Coyote Hill Drive, Palo Alto, California, USA 94304

E-mail: pmitra [at] parc [dot] com

Connect with me via LinkedIn or Twitter!

I am a Member of Research Staff/ Research Scientist at the fabled Silicon Valley R&D company Palo Alto Research Center, formerly Xerox PARC. The focus of my work at PARC is in developing Scientific Machine Learning tools and models for various applications including climate, energy, prognostics and personalized healthcare. The work is funded by generous grants from Xerox, DARPA, NASA among other funding agencies.

I earned my Ph.D. from the University of Massachusetts Amherst in June 2021. At UMass, I worked in the area of Machine Learning and predictive fluid modeling and I was advised by David Paul Schmidt. I completed part of my dissertation research at the Los Alamos National Laboratory, where I worked on developing machine learning frameworks to model geophysical turbulence processes, a critical driver of energy exchange in the oceans and atmosphere. I spent the summer of 2020 working (remotely) with TOTAL S.A. R&D, in developing scalable Machine Learning codes using TensorFlow and PyTorch.

The other major thrust of my research during my PhD was within the ICEnet Consortium, an industry-funded data-driven consortium that I co-founded with my doctoral advisor Prof. David P. Schmidt. ICEnet leverages the power of Artificial Intelligence (AI) to develop predictive machine learning algorithms to improve engine design, helping create fuel-efficient engines capable of meeting stricter environmental regulations. Among the partners for ICEnet include leading engine CFD software makers/users such as SIEMENS-CD/Adapco, Cummins, Convergent Science and AVL as well as NVIDIA and MathWorks.

For my early Ph.D. research I worked on developing reduced order models (ROM) for advanced propulsion systems, working in close collaboration with U.S. Department of Energy national laboratories - Argonne National Laboratory and Sandia National Laboratories, in a $2.1 million industry funded project, Spray Combustion Consortium. Models that I have developed have been implemented within commercial software code Converge CFD, and is in use by major automotive engine makers from around the world.

Apart from my research, I am a part of the core-team at Climate Change AI, a volunteer network that catalyzes impactful work at the intersection of machine learning and climate change and until very recently led the monthly newsletters. I co-organize and lead workshops at major conferences and events including at NeurIPS 2022, AAAI Fall Symposium 2022 and ICLR 2020.

I serve on the advisory board of the Big Data program at Cal State University - East Bay. I have regularly served as a reviewer for prestigious machine learning journals and conferences such as Nat. Machine Intelligence, and NeurIPS, among others.

Recent News:

  • Upcoming invited talk at MIT workshop on AI and climate change, 10/06-10/10

  • Upcoming invited talk at the Monterey Data Conference, 08/30

  • Interview with the Data Stand Up podcast

  • Upcoming invited talk at 16th U.S. National Congress on Computational Mechanics, 7/21.

  • Talk on using Coarse Graining for Fluid flow modeling accepted at NVIDIA-GTC 2022, will be available 4/21.

  • Presented talk at SIAM CSE 2021: Learning Operators from Data Symposium 2021, Dallas, TX, 'Non-Intrusive Machine Learning Models for Fluid Simulations'

Community service

As part of my community service, I advise undergraduate and early graduate degree (first year Masters) students. Additionally I regularly review papers from prestigious scientific journals and machine learning conferences, including:

  • Nature Machine Intelligence Journal

  • Neural Networks Journal

  • Atomization and Sprays Journal

  • Neural Information Processing Systems (NeurIPS) Workshops on:

    • Machine Learning for the Physical Sciences 2020,

    • Algorithmic Fairness through the lens of causality and Interpretability, 2020

  • Tackling Climate Change with Machine Learning workshop at the International Conference on Learning Representations (ICLR) 2020 and a meta-reviewer for the International Conference on Machine Learning (ICML) 2021