Optimization

Key dimensions that have helped me understand multi-arm bandit problems: action space, problem structure, external information, reward mechanism, and learner feedback.

Discover how NEAT and HyperNEAT changed neuroevolution by automatically designing neural network architectures and scaling them through geometric patterns.

A sophomore year exploration of evolutionary algorithms applied to Atari games, implementing NEAT-inspired speciation mechanisms for Cartesian Genetic Programming to protect topological innovation while introducing custom crossover operators (subgraph, aligned-node) for evolving neural network policies.

An in-depth guide to GANs: how two neural networks compete to generate realistic data, the math behind it, and the evolution of objective functions that stabilize training.

A sophomore year deep dive into functional metaprogramming, replicating FFTW’s genfft metaprogram logic in Haskell to generate straight-line optimized C kernels for FFTs, using symbolic DAG representation and algebraic simplification to understand how abstract algebra translates into efficient machine code.

A freshman-year automation tool solving the university scheduling constraint satisfaction problem through web scraping Drexel’s Term Master Schedule and implementing recursive backtracking algorithm to generate every valid schedule permutation satisfying user-defined hard and soft constraints, used throughout undergrad 2016-2020.