Outcome Logic: A Foundational Framework for Concurrent and Probabilistic Program Analysis

This keynote talk by Alexandra Silva from Cornell University introduces Outcome Logic as a foundational framework for analyzing programs that combine randomization with nondeterminism and concurrent behavior. Explore how this innovative approach bridges the gap in analyzing randomized programs that exhibit concurrent behavior, particularly relevant for applications in cryptography and machine learning. Learn how Outcome Logic integrates concurrent and probabilistic separation logics principles to develop new compositional reasoning methods. The presentation, scheduled for the VMCAI conference (January 20-21, 2025) sponsored by ACM SIGPLAN, addresses the challenges of reasoning about mixtures of effects in programs, offering valuable insights for researchers and practitioners working with complex program analysis.