Lectures related to Maximum Flow: Theory and Applications

Explores network sampling consistency, models, and graph dynamics in real-life scenarios.

Explores concurrent programming theory and practice, covering system models, cache coherence, and graph processing.

Covers probabilistic methods in combinatorics, monochromatic edges, 2-colorable graphs, and good 2-colorings.

Delves into graph-enhanced machine learning, focusing on fraud detection, malware detection, and recommendation systems.