Current Work @ Algoverse AI
Optimizing ML Training Pipelines
Real-time gradient flow across loss landscapes • 30% faster convergence
Gradient Descent • Loss Landscape Optimization
Impact By The Numbers
0.0
GPA
0+
Model Configurations
0%
Runtime Reduction
Research Interests
Exploring the intersection of geometry, symmetry, and machine learning
SO(3) Equivariance • Manifold Learning • Geometric Symmetry
∇
Geometric Deep Learning
Neural architectures respecting geometric structure of data
⊕
Sparse Autoencoders
Interpretable features through geometric sparsity constraints
∼
Equivariant Networks
Models preserving symmetries and transformations in data