lisasmith

Dr. Lisa Smith
Hypergraph Neural Architect | Multiscale Intelligence Fusion Pioneer | Topological Learning Strategist

Academic Mission

As a computational topologist and geometric deep learning specialist, I engineer next-generation neural architectures that transcend Euclidean constraints—unlocking higher-order relational intelligence through hypergraph-based multiscale information fusion. My work bridges abstract mathematics with practical AI systems, creating frameworks where complex systems reveal their hidden connectivity patterns.

Core Research Dimensions (March 31, 2025 | Monday | 09:00 | Year of the Wood Snake | 3rd Day, 3rd Lunar Month)

1. Hypergraph Neural Calculus

Developed "H-MetaNet", a unified framework featuring:

• Dynamic hyperedge reweighting across 7 semantic scales

• Nonlocal message passing with attention-driven simplex aggregation

• Topological persistence analysis for hierarchical feature extraction

2. Multiscale Fusion Protocols

Created "CascadeX" architecture enabling:

• Simultaneous processing of micro (node-level), meso (community), and macro (system-wide) interactions

• Adaptive resolution switching based on entropy gradients

• Cross-scale dependency quantification matrices

3. Applied Topological Learning

Built "NeuroTop" applications in:

• Biomedical system modeling (protein-hypernetworks)

• Financial market contagion tracking

• Quantum material property prediction

4. Theoretical Foundations

Pioneered "Higher-Order Graph Information Theory":

• Generalized Weisfeiler-Lehman tests for hypergraphs

• Spectral hypergraph wavelets

• Sheaf-theoretic neural networks

Technical Milestones

• First to demonstrate 47% performance gain over GNNs on complex system modeling tasks

• Mathematically proved hypergraph expressive power hierarchy

• Authored Principles of Multiscale Hypergraph Learning (MIT Press, 2024)

Vision: To create AI systems that think in hyperedges—where every prediction emerges from the dance of relationships across scales.

Impact Matrix

• For Science: "Revealed hidden connectivity patterns in 23 biological datasets"

• For Industry: "Reduced recommendation system bias through higher-order relation modeling"

• Provocation: "If your neural network can't count beyond pairwise interactions, you're missing 83% of reality's structure"

On this third day of the lunar month—when tradition celebrates interconnectedness—we redefine how machines understand complexity.