Rate My Professor Lars Kulik

Kulik's research specializations encompass data privacy, data management, and artificial intelligence, with particular emphasis on spatial and spatiotemporal data analysis, time series classification, trajectory data processing, and AI assurance. He is a member of the Artificial Intelligence research group and participates in the AI Assurance Lab at the School of Computing and Information Systems. His scholarly output demonstrates significant influence in the field, with 7,678 citations, an h-index of 47, and an i10-index of 108 on Google Scholar. Key publications include "Efficient generation of simple polygons for characterizing the shape of a set of points in the plane" (214 citations, supported by a National Science Foundation grant), "Ordinal Embedding for Collaborative Filtering: A Unified Regularization for Enhanced Generalization and Interpretability" (supported by a Google grant), "PULSAR: Advancing Interval-Based Time Series Classification to State-of-the-Art Performance" (2025 IEEE International Conference on Data Mining), "An effective and versatile distance measure for spatiotemporal trajectories," "An automated matrix profile for mining consecutive repeats in time series," and "Ontology-driven map generalization" (Journal of Visual Languages & Computing, 2005). Kulik has received research funding from organizations including Google and the National Science Foundation, underscoring his contributions to advancing data systems and privacy-preserving technologies.