Seshadhri Comandur

C. Seshadhri (Seshadhri Comandur) is a Professor of Computer Science at the University of California, Santa Cruz's Baskin School of Engineering. He earned his Ph.D. from Princeton University and completed postdoctoral research at IBM Almaden Labs. His primary research spans theoretical computer science, graph algorithms, data mining, and sublinear algorithms, with applications in network analysis and scalable computation.

His work focuses on graph property testing, monotonicity testing, subgraph counting, and combinatorial algorithms. Research integrates randomization, high-dimensional analysis, and optimization techniques to solve problems in large-scale network datasets. Key interests include developing efficient algorithms for complex graph structures and exploring limitations of low-dimensional embeddings.

Publications demonstrate consistent focus on algorithmic efficiency in graph analysis, with recent work advancing sublinear-time methods, spectral decompositions, and combinatorial dichotomies. Trends show strong emphasis on theoretical foundations of data mining and rigorous complexity analysis.

• Invited Lecture at ICDT 2023
• Best Paper at WSDM 2020
• 2019 SDM/IBM Early Career Data Mining Research Award
• Best Paper at WWW 2017
• Best Paper at ICDM 2015
• Best Paper Finalist at WWW 2015
• Best Student Paper at KDD 2013
• Best Research Paper at SDM 2013
• 2013 Sandia Employee Recognition Award

Advises PhD/MS students and postdocs including Sabyasachi Basu, Daniel Paul Pena, Shweta Jain, and Andrew Stolman. Leads the PTReview blog on property testing. Current group includes postdocs and graduate students working on algorithms and complexity.