Summary
Nikan Doosti is a machine learning researcher and PhD candidate at Aalto University with eight years of experience applying deep learning to physical simulation, computational design, and digital fabrication—particularly AI-driven process integration for metal additive manufacturing. He has a proven track record from industry ML engineering to academic research, including a published master's thesis from the Max Planck Institute on physics-informed deep learning for topology optimization. Comfortable spanning end-to-end pipelines, he has led data architecture and low-data modeling efforts in product settings and has founded technical projects for industrial automation and educational software. Nikan is unusually persistent about applying ML to scientific and engineering problems (he began ML work in 2017 despite no formal curriculum), and is actively exploring extensions into drug design and healthcare where physics-aware AI can make tangible impact.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Aalto University
Master of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at Iran University of Science and Technology
Bachelor of Science - BS, Computer Engineering, 18.64/20.0, Bachelor of Science - BS, Computer Engineering, 18.64/20.0 at University of Guilan