Keivan Faghih Niresi
Fields of expertise
Inverse problems, Graph representation learning, Deep learning, Signal processing, Distribution networks, and Hyperspectral imaging
BiographyKeivan is a PhD student at École Polytechnique Fédérale de Lausanne (EPFL). He joined the Intelligent Maintenance and Operations Systems (IMOS) Lab under the supervision of Prof. Olga Fink in February 2023.
His current research interests lie primarily in geometric deep learning, signal processing, and inverse problems, with application to network science, energy systems, and intelligent infrastructure.
Prior to joining EPFL, he obtained his master's degree from the Institute of Communications Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua University (NTHU) where he conducted research in convex and non-convex optimization, statistical signal processing, deep learning, and hyperspectral imaging under the supervision of Prof. Chong-Yung Chi. He also had the opportunity to work as a machine learning engineer intern at PranaQ, where he focused on developing signal processing and feature extraction algorithms for biomedical signals such as photoplethysmogram (PPG) and electrocardiogram (ECG).