David F. Gleich

David F. Gleich is a Professor and University Faculty Scholar in the Department of Computer Science at Purdue University, with a courtesy appointment in Mathematics. His work focuses on high-performance large-scale matrix computations to analyze data from biology, social networks, and scientific simulations.

• Key research areas include higher-order network analysis, algorithmic anti-differentiation, spectral graph theory, community detection, and energy-efficient computing.
• Recent publications highlight trends in topological analysis of neural networks (2023), higher-order network analysis (2021), and correlation clustering via graph drawing (2020).
• Awards:
  • NSF CAREER Award
  • 2018 SIAM Outstanding Paper Prize
• Teaches graduate courses like CS520 - Computational Methods in Optimization and CS515 - Matrix Computations, with an online/virtual teaching adaptation since 2020.
• Collaborates with student co-authors such as Charles Colley, Yufan Huang, and Omar Eldaghar on network and matrix problems.
• Maintains active presence on social platforms (Bluesky, Mastodon) advocating for open-source tools, clean air, and responsible AI.