Vahideh Vahidifar

Vahideh Vahidifar is a Research Fellow in the Department of Mathematics at the University of Sussex's School of Mathematical and Physical Sciences, holding a Global Talent Endorsement from EPSRC/UKRI. Her academic journey includes a PhD from Iran University of Science and Technology (2018-2022) and a Research Associate position at Iran's National Elites Foundation (2020-2022).

Her research spans the intersection of pure mathematics and applied computational fields, with core expertise in Nonlinear PDEs, Geometric Analysis, and Geometric Deep Learning. Key interests include Riemannian Manifold Learning, Graph Neural Networks, Calculus of Variations, and applications in Material Sciences. Her work connects abstract mathematical frameworks with practical robotics and AI systems.

Analysis of her 12 recent publications (2021-2025) reveals a strong focus on gradient estimates for nonlinear parabolic/elliptic equations on metric measure spaces, with significant contributions to Ricci flow theory and geometric analysis. Parallel work applies mathematical principles to surgical robotics, particularly tremor detection and Leap Motion-controlled systems.

• Global Talent Endorsement by EPSRC/UKRI (2022)

Vahidifar maintains active international collaborations across the UK, Canada, and USA. Her robotics research demonstrates practical translation of mathematical frameworks into medical device control systems, particularly in surgical robotics where she has developed novel approaches using Leap Motion sensors and Kalman filtering. Current work bridges geometric analysis with emerging AI architectures like Graph Neural Networks.

She leads research in geometric foundations of machine learning while maintaining strong ties to mechanical engineering applications, particularly in smart materials and continuum mechanics. Her laboratory work integrates theoretical mathematics with physical robotics systems for surgical applications.