Quantifying Noise in Dimensionality Reduction Methods
Computing the noise propagation in t-stochastic neighbor embeddings (t-SNE) and uniform manifold approximation projection (UMAP).
Projects
Computing the noise propagation in t-stochastic neighbor embeddings (t-SNE) and uniform manifold approximation projection (UMAP).
Proving that model predictive control and regularized online balanced descent with noisy predictions can control dynamical systems.
Finding classes of test-functions that are fooled by random walks on expander graphs.
Identifying Chemicals using Mass-Spec data, aided by t-stochastic neighbor embedding (t-SNE) and Nonnegative Matrix Factorization (NMF).
Using financial machine-learning models to quantify COVID-19 cases as a function of vaccination rates to predict outbreaks.
Using particle swarm optimization and finite element analysis to design/simulate acoustic band-limiting filters for the gravitational wave detector's Cryo-Q upgrade.
Creating a mathematical model for the delays of airplanes at the San Francisco Airport during the closure of runway 85A.
Modeled Wildebeest, Grass, and Rinderpest virus populations in the presence of forest fires using analytical factors of the Leslie Matrix.
Created an unsupervised Hidden Markov model using the Viterbi algorithm to generate links between word-phrases, and trained the HMM to generate novel Shakespearean sonnets.
Implementing a music synthesizer on a piano, with a Karplus-Strong algorithm to modulate frequency resonance and amplitude decay.
Created an optimized dynamic memory allocator using an explicit free list with coalescing and block-splitting techniques to enhance the utility and speed of the allocator.
Used node-extraction and parsing to create an assembly language generator (TCompiler) for a primitive programming language (Tim) that can perform complex computations.
A webpage that predicts whether tomorrow will be Caltech's ditch day. The probabilities used here are based on the past 20 ditch days.
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