I am a first year Ph.D. student in the School of Computational Science and Engineering at Georgia Tech advised by Professor Bistra Dilkina.

I am interested in using machine learning, optimization, and simulation techniques to create models that give better insights into the interactions of complex systems. An example of this is modeling the migration patterns of populations under different scenarios of sea level rise in order to understand and better plan for accelerated urban growth.

Education

Georgia Institute of Technology, 2015-Current
Ph.D. Computational Science and Engineering

University of Mississippi, 2011-2015
B.S. Computer and Information Science
Minor in Mathematics for Engineers
GPA: 3.92

Skills

  • Python (including NumPy, Scikit-learn, Matplotlib, Basemap, Fiona, Shapely and otherpac k ages)
  • Java, C#, HTML/CSS/Javascript, PHP
  • Familiar with using geospatial datasets
  • Familiar with Linux, use Ubuntu Linux as main OS
  • Graduate classes: Algorithms, Machine Learning, Computational Sustainability, Modeling and Simulation,Deep Learning

Projects

This is a list of some of the projects that I have worked on.

  • Optimization with Integrated Transportation and Land Use Models advised by Dr. Bistra Dilkina
    • The goal of this project is to see if it is possible to influence where people live in an urban environment by changing the transportation networks with the purpose of achieving sustainability goals.
    • We have coupled the recently released version of UrbanSim with MATSim to create a modeling framework in which to study this problem.
    • I created several tools to visualize geographic and road network data to test the models.
  • Triangle Densest k-Subgraph problem with Integer Linear Programmingadvised by Dr. Bistra Dilkina
    • Finding the Triangle Densest Subgraph of size k is a NP-hard problem that is useful for finding quasi-cliques in a graph.
    • We are investigating finding and approximating hard instances of this problem with an Integer Linear Programming approach and comparing the performance against greedy heuristic based algorithms.
  • Vertex Cover solvers, Class ProjectCSE 6140 - Algorithms, Fall 2015
    • I implemented Branch and Bound and Simulated Annealing algorithms in Python to solve the Vertex Cover problem.
    • Tested the above algorithms with algorithms that team members implemented on common graphs from networking literature.
  • Cellular automata networks for predicting weatheradvised by Dr. Dawn Wilkins
    • This was my undergraduate honors thesis project. I examined simulating climate variables with cellular automata models.
    • I was interested if adding in long range connections to the cellular automata model could improve the accuracy of the model by learning the influences certain climate indicators (like El Nino) have on local weather.
    • During the project I automated the training of over 10,000 neural networks on the Mississippi Center for Supercomputer Research's cluster.
  • Automating measurements of Space Plantsadvised by Josh Vandenbrink, Ph.D.
    • This was my Senior year capstone project. I was tasked with creating a framework that automated the data collection process from images of seedlings grown on the International Space Station.
    • A lab in the UM Department of Biology received groups of 80+ images, showing the growth of up to 10 seedlings per image over time, then had to measure each seedling in each image by hand with a graphics program.
    • I created a Python program that facilitated faster manual measurements and automatically performed OCR, perspective transformations, and image registration on these image groups to standardize the measurements as much as possible.
  • Satire detection, Class ProjectCSCI 517 - NLP, Spring 2014
    • The objective of this project was to make an algorithm that could detect whether a text was satirical or not.
    • I scraped a corpus of articles from satirical news websites and regular news websites, then trained several common classifiers on parts of speech, n-grams, and bag of words features on the corpus.
    • Found that the classifiers achieved high accuracy by overfitting to the high number of quotations used in satirical news articles.
  • Face keypoints, Class ProjectENGR 691 - Machine Learning, Spring 2014
    • This problem was from the ``Facial Keypoints Detection'' Kaggle Competition, given an input image of a face, output where certain facial keypoints are.
    • I attempted to use train a classifier to label the output from automatic keypoint detection methods from the OpenCV library.
    • I also attempted a direct regression based approach using a neural network and local binary pattern images which performed better.
  • Face recognition with limited training samplesadvised by Dr. Jianxia Xue
    • This project was focused around the problems involved with automating classroom attendance using face recognition with a single training sample for each individual.
    • I created a web application with a Python backend that performed online facial detection and recognition via a HTML5 webcam access.
    • We examined improving the standard Local Binary Pattern Histogram approach for face recognition and using active learning to improve recognition accuracy.