Anh Tran

Anh Tran

Data Scientist at John Hancock / Manulife

Hi, I’m Anh Tran, currently a Data Scientist at John Hancock / Manulife in Boston, MA (September 2025 – present).

Prior to joining John Hancock, I served as a Senior Member of Technical Staff at Sandia National Laboratories. I earned my Ph.D. in Mechanical Engineering from the George W. Woodruff School at Georgia Institute of Technology in December 2018, advised by Prof. Yan Wang. In January 2019, I joined Sandia National Laboratories in Albuquerque, NM as a postdoctoral appointee, mentored by Dr. Tim Wildey.

I subsequently held Senior Member of Technical Staff positions in the Optimization and Uncertainty Quantification Department (Sep 2020 – Mar 2022) and the Scientific Machine Learning Department (Mar 2022 – Jul 2025), both within Sandia’s Center for Computing Research. You can learn more about my past work on my Sandia profile.

In addition to my Ph.D., I hold an M.S. in Mathematics from Georgia Southern University, and both B.S. and M.S. degrees in Mechanical Engineering from Georgia Institute of Technology.

🔨 Technical Skills

  • Languages: Python, C++, Jupyter, Fortran, Bash, SQL, R, MATLAB
  • Frameworks: PyTorch, TensorFlow, Hugging Face Transformers, Scikit-learn, Scikit-image, NumPy, SciPy, OpenCV, JAX
  • Concepts: Generative AI, Supervised/Unsupervised DL, LLMs, Reinforcement Learning, Bayesian ML, Active Learning, Gaussian Process / Bayesian Optimization, Bagging/Boosting Ensemble Learning, Graph Neural Network, Neural Operators, Reduced-Order Models, Inverse Problems, Bayesian Optimal Experimental Design, Data Visualization
  • Workflow: Linux, Git, SLURM, HPC environments
  • Applications: Scientific Computing, Simulation Modeling, Engineering Design, Sequential Decision-Making, Scientific Discovery, Inverse Problems, Parametric PDEs, CFD, FEM, ICME

🔧 Applied Expertise

  • Computer: Deep Learning, Generative AI, Bayesian Machine Learning, High-Performance Computing
  • Mathematics: Applied Mathematics, Optimization, Numerical Methods, PDE, Uncertainty Quantification
  • Statistics: Bayesian Inference, Sensitivity Analysis, Probabilistic Modeling, Stochastic Processes
  • Engineering: Mechanical Engineering, Computational Mechanics, Materials Science

💼 Employment History

  • Senior Member of Technical Staff, Sandia National Laboratories Sep 2020 – July 2025

  • Postdoctoral Appointee, Sandia National Laboratories Jan 2019 – Sep 2020

  • Technical Consultant, KSB/GIW Industries, Inc. Jul 2019 – Dec 2019

  • Graduate Research Assistant, Georgia Institute of Technology Sep 2014 – Dec 2018

  • Hydraulic Research Intern, KSB/GIW Industries, Inc. May 2013 – Dec 2018

  • Graduate Assistant, Georgia Southern University Aug 2012 – May 2014

💻 Research

My research bridges scientific machine learning, uncertainty quantification, optimization, and computational mechanics to enable data-driven discovery and predictive modeling in engineering and materials science. I develop interpretable, physics-informed AI frameworks - ranging from Gaussian processes to generative AI models - for solving inverse problems, accelerating simulations, and optimizing complex systems under uncertainty. My work has been applied to domains such as computational fluid dynamics, additive manufacturing, materials design, and biomechanics, leveraging high-performance computing and rigorous statistical modeling.

📝 Publications

Please see my Google Scholar for a complete list of publications.

🏆 Honors and Awards

  • 2025.04 Panelist, National Academies study “Frontiers of Statistics: 2035 and Beyond,” IMSI UQ for Materials Science Workshop (link)
  • 2025.02 Sandia Spot Award (CIS/External Review Board – poster)
  • 2024.09 ASME News highlight, CIE/IDETC Hackathon (link)
  • 2022.08 Sandia Thunderbird Kudos Team Award – CSRI Summer Proceedings reviews
  • 2021.11 Reviewer With Distinction Award, ASME Journal of Mechanical Design (link)
  • 2021.08 Best Paper Award, ASME IDETC/CIE – Advanced Modeling and Simulation (AMS)
  • 2021.07 Editors’ Choice Paper Award (9/189), ASME Journal of Biomechanical Engineering (link)
  • 2020.11 Reviewer of the Year, ASME Journal of Computing and Information Science in Engineering (link)
  • 2020.08 Best Paper Award, ASME IDETC/CIE – Computer and Information in Engineering (CIE)
  • 2019.08 Best Paper Award, ASME IDETC/CIE – Advanced Modeling and Simulation (AMS)
  • 2019.05 Travel Awards – MUMS Transition Workshop & SPUQ
  • 2010.03 Finalist (Top 1%), MCM/COMAP Mathematical Contest in Modeling (Problem A) (link, link).
  • 2010.08 6th Place, US National Collegiate Mathematics Championship, Pittsburgh, PA
  • 2009.04 2nd Place, Mercer MUURMaC Undergraduate Research Conference (link)
  • 2009–2010 Recipient, Gulfstream Aerospace Scholarship
  • 2008–2009 Champion, Departmental Math Problem Solving Contest, Georgia Southern University
  • 2009.12 Putnam Exam, 19 points (link)
  • 2005.06 Bronze Award, Singapore Mathematical Olympiad (Senior Section)

🎓 Education

  • Ph.D. in Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 2014–2018

    Dissertation: Multiscale uncertainty quantification for physics-based data-driven materials design and optimization

  • M.S. in Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 2014–2018

  • M.S. in Mathematics, Georgia Southern University, Statesboro, GA 2012–2014

    Thesis: Adaptive state feedback control of Lorenz systems

  • B.S. in Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 2010–2011