Dr. Fan earned his PhD in Computer Science from the University of Texas at Dallas. His research interests lie broadly in algorithms, computational geometry, and data science.
During his PhD studies, Dr. Fan was interested in how techniques from geometry can be applied to data processing and analysis. His PhD thesis work focused on metric violation and similarity. The metric violation distance problem concerns minimally modifying the data to make it metric, given a data may not be metric, that is, finding the nearest metric data set. For the similarity aspect, Dr. Fan studied the fundamental computational task of assessing the similarity of ordered data sets, such as two trajectories.
Dr. Fan’s previous postdoctoral research centered on the design of algorithms for clustering problems that arise in the context of machine learning.
In the Theory of Algorithms Lab at Bar-Ilan University, Dr. Fan focuses on problems in algorithm theory and data science related to data metrics, clustering, similarity, and metric learning. He looks forward to collaborating with researchers from a wide spectrum of areas.