Viet Tran is a doctoral researcher based in Aachen with nine years of experience at the intersection of machine learning and neuroscience, specializing in data- and energy-efficient algorithms inspired by brain function. At Forschungszentrum Jülich and RWTH Aachen he has developed biologically plausible learning rules and architectures for artificial neural networks while also applying statistical and deep learning methods to NLP. His work blends rigorous academic training—a top-ranked MS and BS in Data Science and Computer Science—with hands-on research engineering across graduate and staff roles. Viet is active in the NeuroAI community (Dendritic Learning Group @PGI-15) and contributes to interdisciplinary efforts like AI Grid to bridge theory and practical systems. Notably, his focus on dendritic-inspired learning highlights an uncommon emphasis on neurally plausible mechanisms rather than only performance-driven models. He brings a practical researcher’s mindset: translating neuroscientific insight into efficient, implementable ML algorithms.
9 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Data Science, 1.0 (with distinction), Master of Science - MS, Data Science, 1.0 (with distinction) at RWTH Aachen University
Exchange semester, M.Sc. Computer Science, Exchange semester, M.Sc. Computer Science at University of Waterloo
Computation and work management system for time-constrained cluster environments.
Contributions:4 PRs, 69 pushes, 4 branches in 7 months
slurmcomputationmanagement-systemdaskconstrained
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