Masters of Computer Science in Data Science Graduate from Illinois-Urbana Champaign. Currently taking additional courses beyond the masters requirements. My fall 2019 course is numerical analysis a.k.a. scientific computing.
- STAT420: Statistical Modeling (Prof David Dalpiaz)
- STAT542: Statistical Learning (Prof Feng Liang)
- STAT578: Advanced Bayesian Modeling (Prof Trevor Park)
- CS428: Applied Machine Learning (Prof David Forsyth)
- CS410: Text Analytics and Mining (Prof Cheng Zhai)
- CS498: Cloud Computing
- CS498 DDV: Data Visualization
- IS 532: Data Cleaning (Prof Bertram Ludäscher)
- CS450: Scientific Computing (Prof Micheal Heath) – Fall 2019
Master’s GPA: 3.94
- The Page Rank: A Survey from Markov Chains to Tensors
A survey of page rank algorithms including n-order Markov chains, tensor rankings & Dirichlet ranking. Extends on current papers with additional n-order tensor ranking algorithms.
- Bayesian Analysis of the 115th US Congress Electoral Results
Bayesian hierarchical models related to democrat, republican and household income, inference and Bayesian interpretation.
- Human Chromosome 22 ADAM Processing Pipeline, Exploratory Analysis & Visualization
Chromosome 22 Analysis using output from an ADAM Pipeline to generate genetic variants with clinvar annotations.
- L-Rank: A Page Rank Implementation for the Public Service Domain
Implementation mechanism of a local ranking system based on page rank methods using city data from the public service domain.
- NASA Solar Radiation Linear Models Analysis & Prediction
Analysis, generalized linear modeling, inference & prediction of solar radiation using NASA’s Space Apps data set.
- Commentary: Native predators reduce harvest of reindeer by Sámi Pastoralists
Detailed analytics commentary on the Bayesian analysis in the paper by N. Thompson Hobbs et al.
- Time Series
- Quantitative Finance
- Neural Networks
- Scientific Computing
- Distributions & MC Simulation
- Python, Scipy, Numpy, Pandas
- Keras with Tensor-flow Back-end