Keivan Faghih Niresi

I am a Ph.D. candidate at École Polytechnique Fédérale de Lausanne (EPFL) within the Intelligent Maintenance and Operations Systems (IMOS) Laboratory, supervised by Prof. Olga Fink. My Ph.D. thesis focuses on developing data-driven computational sensing methods by leveraging signal processing and machine learning on graphs. During my doctoral studies, I also conducted a research visit at the University of Cambridge within the Data, Vibration, and Uncertainty (DVU) Group, working under the supervision of Prof. Alice Cicirello.

Prior to joining EPFL, I earned my master’s degree from the Institute of Communications Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua University (NTHU) in Taiwan, where I conducted research in convex and non-convex optimization, robust statistics, deep learning, and hyperspectral imaging at the Wireless Communications and Signal Processing (WCSP) Laboratory under the supervision of Prof. Chong-Yung Chi. I also had the opportunity to work as a machine learning engineer intern at PranaQ, where I focused on developing mathematical algorithms for multimodal biomedical data.

The foundation of my research lies in advancing computational sensing through the development of mathematical frameworks and algorithms. I am particularly interested in topics at the intersection of machine learning, signal processing, and computational mathematics, such as inverse problems, graph representation learning, domain adaptation, physics-informed learning, uncertainty quantification, and optimization.

For a complete list of publications, check my Google Scholar page.