Dimitrios Katselis

Dimitrios Katselis is a Teaching Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, where he is affiliated with the Coordinated Science Laboratory. Recently promoted to this position, he instructs core undergraduate and graduate courses including ECE 310 Digital Signal Processing, ECE 311 Digital Signal Processing Lab, ECE 313 Probability with Engineering Applications (jointly MATH 362), ECE 314 Probability in Engineering Lab, ECE 534 Random Processes, ECE 543 Statistical Learning Theory, and ECE 562 Advanced Digital Communication. His teaching emphasizes foundational and advanced topics in probability, signal processing, and machine learning, contributing significantly to the ECE curriculum.

Katselis joined the University of Illinois at Urbana-Champaign in February 2014. Previously, he served as a Research Scientist at the KTH Royal Institute of Technology in Sweden from 2010 to 2013 and held a visiting research position at the Australian Research Council Centre of Excellence for Complex Dynamic Systems and Control at the University of Newcastle in 2013. His research interests span stochastic systems and control, applied probability, system identification, machine learning, and signal processing. Katselis has authored or co-authored 47 research outputs, including 18 journal articles and 23 conference papers. Notable publications include the highly cited "FBMC physical layer: a primer" (Phydyas, 2010, 847 citations), "Preamble-based channel estimation in OFDM/OQAM systems: A review" (Signal Processing, 2013, 300 citations), "Preamble-based channel estimation for CP-OFDM and OFDM/OQAM systems: A comparative study" (IEEE Transactions on Signal Processing, 2010, 112 citations), and recent works such as "Finite Sample Analysis for Structured Discrete System Identification" (IEEE Transactions on Automatic Control, 2023), "On the Consistency of Maximum Likelihood Estimators for Causal Network Identification" (IEEE Control Systems Letters, 2022), "Spectral Clustering and Labeling for Crowdsourcing with Inherently Distinct Task Types" (Transactions on Machine Learning Research, 2025), "On Modelling and State Estimation of DC Motors" (Actuators, 2025), and "A note on the structure of the filtering recursion for finite HMMs" (Statistics and Probability Letters, 2026). His work has influenced advancements in signal processing, communications, and machine learning applications.

Professional Email: katselis@illinois.edu