Vivin Vinod

Postdoctoral Researcher in Machine Learning Methods
University of Wuppertal, Germany

I specialize in combining multifidelity data to generate low-cost high-accuracy ML models. In addition I work with active learning strategies that harness uncertainty quantification to reduce dataset redundancies. Application areas include simulating excited states in light harvesting complexes, and molecular potential energy surfaces. With a multi-disciplinary academic background spanning physics, economics, computer science, and quantum chemistry, I aim to explore interdisciplinary workflows bridging the fidelity gaps in sparse data regimes.

This is my webpage that records my research and career. Feel free to look around!