40+ publications with 900+ citations and an H-index of 17
15 first-author papers, including the 2024 arXiv preprint "Interpolating Neural Network: A Novel Unification of Machine Learning and Interpolation Theory"
INN work accepted by Nature Communications (2025)
Holds 4 patents
Secured $1.3M in research funding
Demonstrated 1000x speedup, 95% energy reduction, and infinite scalability in scientific AI simulations
Research Experience
Founder & CTO of HIDENN-AI, developing AI solutions for engineering and scientific computing
Creator of the Interpolating Neural Network (INN), unifying machine learning and interpolation theory for ultra-fast, scalable, and sustainable simulations
Developed the JAX-FEM GPU solver for large-scale high-fidelity simulations
Conducted research on CAD-compatible analysis (Isogeometric Analysis, IGA) enabling seamless design-analysis integration
Work includes physics-informed neural networks, surrogate modeling frameworks, multi-scale simulations, and GPU-based parallel computing