Eric Lindgren
PhD Student In Computational Physics And Machine Learning
Gothenburg, Västra Götaland County, Sweden
Summary
👤
Senior🎓
Top SchoolEric Lindgren is a PhD student in Computational Physics and Machine Learning at Chalmers with nine years of hands-on experience blending physics, ML and software development to model molecular interactions for applications like more efficient solar cells. He holds an M.Sc. in Physics and engineering-physics undergraduate training, has taught machine learning and computational physics at bachelor and master levels, and completed exchange studies at Seoul National University. His research and engineering work spans GPU-accelerated molecular dynamics and integrating message-passing neural networks into simulation frameworks, reflecting practical expertise in high-performance ML on scientific workloads. He has applied graph-theoretic ML to brain-structure data during his master’s project and contributed platform features for neutron-scattering data processing at ESS, showing a knack for making advanced analysis accessible. Based in Gothenburg, Eric combines academic rigor with production-oriented coding and a demonstrated interest in e-learning and reproducible scientific software.
9 years of coding experience
Exchange Studies (Graduate) - College of Physics and Astronomy Physics Computer Science, Exchange Studies (Graduate) - College of Physics and Astronomy Physics Computer Science at Seoul National UniversityMaster's degree Physics, Master's degree Physics at Chalmers University of Technology
English, French, koreanska, Swedish
Github Skills (9)
cuda10
gpu10
molecular-simulation10
dynamics10
gnn9
neural-network9
machine-learning9
c-language8
cprogramming-language8
Programming languages (11)
Github contributions (5)
brucefan1983/GPUMD
Feb 2022 - Jan 2023
Graphics Processing Units Molecular Dynamics
Role in this project:
ML Engineer
ML EngineerContributions:68 reviews, 38 commits, 14 PRs in 11 months
Contributions summary:Eric primarily contributed to the development of a Graphics Processing Units Molecular Dynamics (GPGPU-MD) simulation. They focused on implementing and refining a message-passing neural network (GNN) layer within the project, specifically for a modified version of the potential. This includes setting up the appropriate data structures, performing calculations on the GPU and debugging issues related to memory alignment. The user's commits also suggest a focus on integrating the GNN component within the broader simulation framework, including code for determining forces.
pythonneuroevolutionsimulationnatural-evolution-strategiesunits
elindgren/Enable-Studio
Apr 2017 - Dec 2017
Contributions:37 commits, 6 PRs, 28 pushes in 8 months
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