Ioannis Koutis

Ioannis Koutis is an Associate Professor in the Department of Computer Science at the New Jersey Institute of Technology (NJIT). His research focuses on algorithms, graph theory, and machine learning, with a particular emphasis on spectral methods and computational science. Koutis has been actively involved in federal grants and collaborative projects, including work on space weather forecasting, linear regression algorithms, and spectral network alignment funded by the National Science Foundation and the United States-Israel Binational Science Foundation.

His research interests span spectral graph theory, combinatorial optimization, and the application of machine learning techniques to complex systems. Notable contributions include advancements in hypergraph partitioning, gene clustering in co-expression networks, and the development of efficient algorithms for large-scale graph problems. Koutis has led or co-led seven federally funded projects, emphasizing interdisciplinary applications in computer vision, bioinformatics, and computational social science.

Publications highlight innovations in temporal knowledge graphs, neural network pruning, and spectral sparsification. Collaborations include partnerships with institutions like UC Irvine, Rensselaer Polytechnic Institute, and the University of Patras. His work bridges theoretical foundations with practical applications, addressing challenges in VLSI design, space weather modeling, and educational technologies.

Awards and recognitions include his h-index of 13 and over 1,700 citations, reflecting significant scholarly impact. Teaching focuses on advanced topics in machine learning and deep learning, contributing to NJIT's graduate curriculum in data science and computer science.