Keivan Faghih Niresi
+41 21 695 50 90
Office: GC A3 445
EPFL › ENAC › IIC › IMOS
Website: https://IMOS.epfl.ch
Expertise
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).
Education
Master of Science
| Communications Engineering2020 – 2022 National Tsing Hua University
Bachelor of Science
| Electrical Engineering2015 – 2019 University of Guilan
Selected publications
Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion
Keivan Faghih Niresi
Published in IEEE Internet of Things Journals in 2025
Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things
Keivan Faghih Niresi, Hugo Bissig, Henri Baumann, Olga Fink
Published in IEEE Internet of Things Journal in 2024
Robust Hyperspectral Inpainting via Low-Rank Regularized Untrained Convolutional Neural Network
Keivan Faghih Niresi, Chong-Yung Chi
Published in IEEE Geoscience and Remote Sensing Letters in 2023
Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution
Keivan Faghih Niresi, Chong-Yung Chi
Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing in 2022
Teaching & PhD
CIVIL-332
Course Book
CIVIL-426
Course Book