Christos Faloutsos is a Fredkin Professor of Computer Science at Carnegie Mellon University with a courtesy appointment in Electrical and Computer Engineering. He also serves as an Amazon Scholar, combining academic excellence with industry application. Born in Athens, Greece, Faloutsos received his PhD from the University of Toronto and has built a distinguished career in computer science. He joined Carnegie Mellon University in 1998 as an Associate Professor, became a Full Professor in 2000, and was awarded the prestigious Fredkin Professorship in Artificial Intelligence in 2020.
Faloutsos is celebrated for his pioneering research on data mining using fractals and power laws, which revealed how traditional data assumptions often fail while demonstrating the effectiveness of self-similarity patterns across diverse datasets. His work has successfully modeled internet and social network topologies, galaxy formations, and video data, leading to breakthroughs in dimensionality reduction and anomaly detection. This research paradigm has enabled significant advances in identifying suspicious activities in online networks and analyzing time-evolving graph structures as tensors. His contributions to the field were recognized with the ACM SIGKDD Innovation Award in 2010, cementing his status as a visionary in data science.
With over 400 refereed articles, 17 book chapters, and three monographs to his name, Faloutsos has profoundly shaped modern approaches to large-scale data analysis. He has mentored numerous PhD students, eight of whom have received KDD or SCS dissertation awards, demonstrating his exceptional influence on the next generation of researchers. As an ACM Fellow who has received 28 best paper awards including seven test of time awards, his leadership extends through service on the SIGKDD executive committee and numerous invited lectures worldwide. Currently, Faloutsos continues to advance the frontiers of data mining with emphasis on graph analysis, temporal anomaly detection, and tensor methods for evolving network structures.
