Kevish Napal

Hi! I’m Kevish, a freelance AI researcher and data scientist with a background in applied mathematics (PhD, École Polytechnique; MSc, Sorbonne University). I develop and apply machine learning models to complex scientific and industrial problems, with a recent focus on biotech and computational biology.

My experience spans deep learning, mathematical modeling, and data-driven optimization, combining strong theoretical foundations with practical implementation skills. I’ve worked on projects ranging from multimodal architectures for early cancer detection to AI models for genomics, emphasizing model interpretability, data efficiency, and real-world impact.

Before moving into AI, I spent four years in academic research in Boulder and Sheffield, collaborating with engineers on inverse problems and imaging with waves. This period honed my ability to translate mathematical ideas into concrete solutions and to thrive in multidisciplinary environments. My work was recognized internationally, earning me an invitation to the Isaac Newton Institute in Cambridge.

Today, I collaborate with startups and research teams on AI-driven innovation. I’m particularly interested in applied research roles that bridge data science, software engineering, and mathematical modeling - ideally within small, fast-moving teams tackling ambitious technical challenges.

Always open to discussions around AI, quantitative modeling, and scientific computing, feel free to connect!