Rajat Arora

Rajat Arora

Senior Machine Learning Engineer

Apple

Biography

Dr. Rajat Arora graduated with a Ph.D. in the field of Computational Mechanics from Carnegie Mellon University (CMU). His research at the time focused on numerical analysis, material science, and high performance scientific software development.

Currently, Dr. Arora is a senior machine learning engineer at Apple. Prior to joining Apple, Dr. Arora was a senior member of techical staff at AMD where he worked on optimizing large scale HPC and ML codes on data center GPUs. Prior to joining AMD, he was a research scientist at Siemens Technology where his research focused on developing computational and (physics-informed) machine learning tools to accelerate scientific discovery and engineering design. Dr. Arora worked at Ansys, Inc. before joining Siemens where he made notable enhancements to the core solver of the Twin Builder product.

Interests
  • Machine Learning
  • Software Development
  • HW/SW Co-Design
  • Parallel/Distributed Computing
  • Applied Mathematics
  • High Performance Computing
Education
  • Ph.D. in Computational Mechanics, 2019

    Carnegie Mellon University, USA

  • M.S. in Computational Mechanics, 2018

    Carnegie Mellon University, USA

  • M.Tech. in Mechanical Engineering, 2014

    Indian Institute of Technology Kanpur (IITK), India

  • B.Tech. in Mechanical Engineering, 2014

    Indian Institute of Technology Kanpur (IITK), India

Skills

Python, C/C++, FORTRAN
Data Structures / Algorithm
Machine Learning
Applied Mathematics
High Performance Computing

Experience

 
 
 
 
 
Senior Machine Learning Engineer
Oct 2024 – Present Seattle, Washington
 
 
 
 
 
Senior Member of Technical Staff
Feb 2022 – Oct 2024 Austin, Texas
 
 
 
 
 
Research Scientist
Aug 2020 – Feb 2022 Princeton, New Jersey
 
 
 
 
 
Research & Development Engineer II
Mar 2019 – Jul 2020 Pittsburgh, PA

Accomplish­ments

Coursera
Neural Networks and Deep Learning
Advanced C++ programming
See certificate

Publications

(2022). Self-fields for disconnections with disclination, dislocation and step character. In Preparation.

(2022). Modeling of experimentally observed topological defects inside bulk polycrystals. In Preparation.

(2022). Physics-informed neural networks for modeling rate- and temperature-dependent plasticity. Submitted. Under Review.

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(2022). PhySRNet: Physics informed super-resolution network for application in computational solid mechanics. IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications in conjunction with SC'22.

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(2022). Mechanics of micropillar confined thin film plasticity. Acta Materialia.

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(2021). Machine Learning-Accelerated Computational Solid Mechanics - Application to Linear Elasticity. 1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE).

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(2020). A unification of finite deformation J_2 Von‐Mises plasticity and quantitative dislocation mechanics. Journal of the Mechanics and Physics of Solids.

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(2020). Dislocation pattern formation in finite deformation crystal plasticity. International Journal of Solids and Structures.

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(2020). Finite element approximation of finite deformation dislocation mechanics. Computer Methods in Applied Mechanics and Engineering.

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(2020). Equilibrium shape of misfitting precipitates with anisotropic elasticity and anisotropic interfacial energy. Modelling and Simulation in Materials Science and Engineering.

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