## Current

### Loren Anderson

Second year Ph.D. student interested in applied mathematics, including data science, information science, computer vision, and machine learning. Anderson previously completed an internship at Los Alamos National Laboratory on functional programming in computer science in 2015.

### Mauricio Flores

Fifth year Ph.D. student interested in applied mathematics, machine learning, and data science. Flores participated in a data science internship for Kohl's Department Stores in 2017 and previously completed an internship at Mitsubishi Electric Research Laboratories in 2016.

### Alex Gutierrez

Fifth year Ph.D. student interested in machine learning and statistical signal processing. Gutierrez previously completed an internship with the Air Force Research Lab in 2013 on robust methods of imaging in radar. He is currently working on fast imaging methods from spatio-temporal magnetic resonance data as well as on new reconstruction methods from sub-sampled geospatial (satellite) data.

### Madeline Handschy

Fifth year Ph.D. student interested in problems from machine learning. Handschy currently works on a problem modeling the loss surfaces of neural networks using several spin glass models from statistical mechanics to better understand how to train neural networks.

### Vahan Huroyan

Fifth year Ph.D. student interested in mathematical data analysis, machine learning, optimization, and computer vision. Huroyan has one year of work experience in software engineering at Instigate Parallel System Development (2012). Currently, he works on two research projects: the first one aims to solve the principal component analysis (PCA) and robust subspace recovery (RSR) problems for large datasets in distributed settings, and the second one is automatic jigsaw puzzle solver for large puzzles by using GCL techniques.

### Tyler Maunu

Fifth year Ph.D. student interested in problems from machine learning and computer vision. Maunu has completed internships on acoustic modeling at Schlumberger-Doll Research, demand forecasting at Amazon.com, and in the summer of 2016, will work as a visiting scientist at the National Geospatial Intelligence Agency. He is particularly interested in applications and theory of non-convex optimization algorithms within machine learning. Current work involves studying an algorithm for subspace recovery and dimensionality reduction, and in the future is interested in applying related ideas to problems in computer vision.

### Yunpeng Shi

Second year Ph.D. student interested in computer vision and machine learning. Shi's current work focuses on developing robust solvers for "structure from motion" problem and studying their theoretical guarantees. In the future, he would also like to explore more on dimension reduction methods and optimization.

## Previous

### John Goes

Quantitative Research Analyst for U.S. Bank in Asset Liability Management (ALM) and Treasury research quant. Experienced quantitative modeler and research data scientist with a focus in machine learning and modern statistical theory. Goes is interested in a variety of applications, including statistical arbitrage, options pricing, algorithmic trading, behavioral modeling, stochastic modeling, econometric analysis and forecasting.

### Jeff Moulton

Quantitative Analyst at Google. Moulton is passionate about solving complex problems through the use of quantitative analysis and is interested in statistical computing, machine learning, and optimization. He previously completed internships in operations research for the Air Force and data science for Allstate.

### Bryan Poling

Chief Engineer and co-founder of Sentek Systems. Poling specializes in computer vision, specifically in feature tracking, motion segmentation, image stitching, and structure from motion. At Sentek, he leads sensor development and is heavily involved in data analysis and exploitation for agriculture-focused, multi-spectral camera payloads for unmanned aerial vehicles.