I am Yuxiang Luo, a Ph.D. candidate in the Department of Computer Science and Engineering at The Ohio State University.

My research focuses on learning representations of physical dynamics for Machine Learning and Reinforcement Learning-based optimization, with applications in manufacturing, robotics, and scientific computing. I am particularly interested in learning representations that are interpretable and generalizable across different tasks and domains.

Currently I am working with Dr. Andrew Perrault for my Ph.D. study. I received my B.S. in Computer Science and Engineering in 2020 and my M.S. in Welding Engineering in 2023 from The Ohio State University.

Education

1. 
   The Ohio State University

   Ph.D., Department of Computer Science and Engineering2020 - 2026 (anticipated)

   M.S., Welding Engineering, Department of Materials Science and Engineering2020 - 2023

   B.S., Department of Computer Science and Engineering2017 - 2020

Experience

1. 
   Path RoboticsMachine Learning Research Intern · Columbus, OHJune, 2026 – CurrentWorld modeling, Vision Language Action Planning for robotic welding, simulation-to-real transfer learning
2. 
   Electric Power Research Institute (EPRI)AI Research Student Employee · Palo Alto, CAMay – Aug, 2025Multi-modal knowledge graph, LLM, Integrated AI for Power System

Updates

1. 06/2026Following a fantastic on-site interview at Path Robotics headquarters in Columbus, OH, I am thrilled to announce that I will be joining their Robotics Research team as a Machine Learning Research Intern starting in June 2026. The team's vision aligns perfectly with my research goals, and I look forward to contributing to world modeling and physical dynamics simulation for robotic welding systems.
2. 05/2026My new paper Recovering physical dynamics from discrete observations via intrinsic differential consistency is now available on ArXiv. This work presents a novel inductive bias in weak form for learning representations of physical dynamics from discrete observations.
3. 05/2026Our new paper New Developments in Studying Ductility Dip Cracking in Welds of Austenitic Alloys is accepted by Cracking Phenomena in Welding and Additive Manufacturing 2025.
4. 04/2026I had a great experience visiting the Google campus in Sunnyvale/Mountain View, CA for an on-site technical interview as part of the 2026 Google NG hiring session.
5. 03/2026Our new paper Computational Framework for Microstructure Identification and Hardness Prediction in Ultrahigh-Strength Steel Components Fabricated by Wire-Arc Directed Energy Deposition is now available on Research Square.
6. 09/2025My new paper Machine Learning-Accelerated Calibration of Complex Heat Source Models for Welding and Additive Manufacturing Process Simulation is presented at the 14th Numerical Analysis of Weldability conference in Graz, Austria.
7. 09/2025In parallel of the FEBTECH 2025, I presented our work Machine Learning-Accelerated Calibration of Complex Heat Source Simulation Models for Welding and Additive Manufacturing Processes at the AWS Professional Program conference in Chicago, IL.
8. 05/2025Started my first day at EPRI as an AI Research Student Employee. Working on multi-modal and fundamental LLM, and integrated AI for power systems in the Transmission Operation and Planning group.
9. 05/2025Our abstract Machine Learning-Accelerated Calibration of Complex Heat Source Simulation Models for Welding and Additive Manufacturing Processes was accepted to the 14th Numerical Analysis of Weldability conference. We will be presenting this work as keynote speakers.
10. 02/2025I will be participating in an AI research internship at the Electric Power Research Institute (EPRI) during summer 2025.
11. 08/2024Our project, in collaboration with Dr. Joel and Dr. Boian, received one-year funding from the College of Engineering at The Ohio State University.
12. 07/2024I am very grateful to receive the kind, generous, and powerful support from professors, sponsors, and colleagues in my communication with USCIS.
13. 07/2023I successfully defended my M.S. thesis entitled Process Optimization Framework for Temper Bead Welding Procedures and graduated with a M.S. degree in Welding Engineering from The Ohio State University.
14. 05/2023My work Reinforcement Learning for Finite Element Model Optimization was presented at the Midwest Machine Learning Symposium in Chicago, IL.
15. 07/2020I am proud to receive the recognition of my two advisors, who supported me to start a dual-degree Ph.D. in Computer Science and Engineering and M.S. in Welding Engineering at The Ohio State University.