About me

Hello, and thanks for stopping by!

My name is Keivan Faghih Niresi (Persian: کیوان فقیه نیرسی). Since February 2023, I have been pursuing a Ph.D. at the Intelligent Maintenance and Operations Systems (IMOS) Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. My doctoral thesis focuses on reliable data-driven computational sensing methods through signal processing and machine learning on graphs, under the supervision of Prof. Olga Fink.

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 and imaging through the development of innovative algorithms and mathematical frameworks. 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, and optimization. These methods are applied to diverse applications, including low-level vision (image restoration, fusion, compression, etc.), Internet of things, remote sensing, intelligent infrastructures, and smart cities. My research emphasizes two key aspects: 1) exploring the fundamental mathematical principles of sensing, and 2) pursuing application-driven projects through collaborations with experts from various disciplines such as electrical engineering, computer science, civil and environmental engineering, applied mathematics, mechanical engineering, and physics.

Featured Publications

	Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things Keivan Faghih Niresi, Hugo Bissig, Henri Baumann, and Olga Fink IEEE Internet of Things Journal, 2024 IEEE Xplore | PDF | Code
	Robust Hyperspectral Inpainting via Low-Rank Regularized Untrained Convolutional Neural Network Keivan Faghih Niresi, and Chong-Yung Chi IEEE Geoscience and Remote Sensing Letters, 2023 IEEE Xplore | PDF | Code
	Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution Keivan Faghih Niresi, and Chong-Yung Chi IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 IEEE Xplore | PDF | Code | Open Remote Sensing