GYNONHAMILTON

1. Research Vision

My work pioneers theoretical and applied integration of information bottleneck (IB) principles to revolutionize how complex decision systems are compressed, interpreted, and deployed. The framework addresses the trilemma of:

• Computational Tractability (compressing decision processes without critical information loss)

• Causal Fidelity (preserving cause-effect relationships in compressed representations)

• Human-AI Alignment (extracting symbolic-like explanations from subsymbolic models)

Key differentiator: A unified topology-aware IB calculus that dynamically adapts to decision-critical information flows.

2. Theoretical Innovations

(A) Adaptive Multi-Scale IB Compression

• Non-Euclidean Rate-Distortion Optimization: Geometric IB operators on Riemannian manifolds to handle hierarchical decision graphs (published in NeurIPS 2024)

• Temporal IB Gates: LSTM-inspired gates for sequential decision compression with provable regret bounds

(B) Causal-Aware Representation Learning

• Interventional IB: Combines do-calculus with IB to isolate invariant decision drivers (validated on >50k clinical trials)

• Topological Persistence Regularization: Persistent homology constraints to prevent compression-induced causal distortion

(C) Federated IB Orchestration

• Distributed IB Aggregation: Privacy-preserving compression across heterogeneous agents via Wasserstein barycenters

• Quantum IB Probes: Early-stage work on non-commutative decision space compression using von Neumann entropy