Mentored a student on Protein Folding Using Variational Annealing over four weeks. Guided problem formulation, a tested HP lattice-folding environment, REINFORCE baselines and benchmarks, and the final report and presentation.
Research proposal
Accelerated Convergence in Glassy Energy Landscapes via Variational Learning
The proposal explores gradient-estimating and learning-based optimization for protein folding and spin-glass models. Instead of adjusting a candidate solution alone, it treats combinatorial optimization as learning a probability distribution over discrete configurations.
Two questions guide the proposed work:
- How efficiently do REINFORCE and Bayesian optimization explore and converge in highly non-convex energy landscapes?
- Can a variational algorithm generalize to held-out problem instances without retraining?
The proposed outcomes are an optimization algorithm, an implementation benchmarked against derivative-free methods, and a technical report documenting the methodology and results.
Content sample: The variational learning loop
