Passivity Tools for Hybrid Learning Rules in Large Populations
A generalized passivity framework for hybrid and discontinuous learning rules in large-population games.
My work spans formal methods for learning-enabled control, compositional cyber-physical systems design, and optimization for electronic design automation.
View the full record on Google ScholarA generalized passivity framework for hybrid and discontinuous learning rules in large-population games.
An exact, pruning-aware transformation from discrete-output neural controllers to soft decision trees that can substantially reduce verification time.
An automated, equivalence-preserving conversion from ReLU neural controllers to verification-friendly soft decision trees.
A practical workflow for distilling deep-RL controllers into simpler decision-tree and kernel models that are easier to validate and deploy.
A timing-aware routing framework that jointly manages topology, congestion, and TDM ratios in multi-FPGA prototyping systems.