I am an Assistant Professor in the Department of Computer and Information Science and Engineering at the University of Florida. My research interests include algebraic and combinatorial methods in computer science and scientific computing. Current projects include the development of the AlgebraicJulia software ecosystem. My faculty page
I earned a Ph.D in Computational Science and Engineering at Georgia Tech. My research focuses on numerical, statistical, and streaming algorithms for data analysis. The applications include complex networks, online media, and observational medical data. My Dissertation Defense was on March 28th 2016.
I have been heavily involved in the developement of the Julia Package LightGraphs.
University of Florida (Gainesville, FL) Assistant Professor, Jan 2021 - Present
- Conduct computer science research
- Teach computer science courses
- Serve the scholastic community
Georgia Tech Research Institute (GTRI) (Atlanta, GA)
Research Engineer II, May 2016 - Dec 2020
- Conduct research into high performance data analysis software
- Technical lead of a multi-year strategic research initiative studying near real-time analysis of news articles and propaganda
- Contributed to DARPA proposal for modeling and simulation of social processes
- Research advisor to several students in Graduate and Undergraduate programs
- Developed a web application for personalized medicine at the hospital scale using Golang, Python, and PostgresDB deployed with Docker.
- Applied machine learning techniques to personalized medicine for predicting adverse patient outcomes presented in a real time Speech to Text physician facing web interface.
- Lead submission to NIJ crime prediction challenge. Predicted crimes in Portland, OR using temporal and geographic features derived from crime statistics and GIS databases
- Lead submission to Fake News Challenge yielding a top 10 performance at identifying fake news articles.
Ionic Security (Atlanta, GA)
Data Scientist, Summer 2015
- Developed data analytics software.
- Designed a service oriented architecture for near real time analysis written in Go and Julia.
- Leveraged time series and network database technologies including Heka, InfluxDB, RabbitMQ, and ElasticSearch.
Lawrence Livermore National Laboratory (Livermore, CA)
Institute for Scientific Computing Research Intern, Summer 2014
- Studied relationship between numerical accuracy of eigensolvers and solution quality of mincut graph partitioning.
- Developed very fast approximate eigensolvers for large graphs.
- Applied probabilistic reasoning to describe numerical computations.
- Presented results at LLNL poster session.
Center for Computing Sciences (Bowie, MD)
Conducted research into Malware structure and similarities
- Studied execution patterns of malicious programs.
- Developed clustering and methods for understanding the structure of malicious programs with graph analytics.
- Built a high performance distributed system for conducting these analyses with ZeroMQ communication.
Georgia Institute of Technology (Atlanta, GA)
Graduate Research Assistant, 2012 - 2016
- ASEE/NDSEG Fellowship 2013 - 2016
- Applied Mathematics
- Data Analysis
- Large Graph Analysis
- High Performance Computing
CNL Software (Indianapolis, IN)
Software Engineering Intern, Summer 2011
- Developed a program to analyze geo-location data for physical security of a building, and present real time data to management.
- C#/.Net Development
University of Florida
CIS 4930⁄6930 Abstraction, Composition, and Computation
Course number(s): CIS4930/6930 Prerequisites: COT3100, MHF3202, or equivalent Description: Compositionality allows us to build abstractions that faithfully represent the behavior of our systems. When abstractions compose, complex systems can be designed by combining simple parts, where the behavior of the whole is governed by the behavior of the parts. Abstraction enables scalable engineering design. In software engineering, good abstractions are a key to reliable software and compositionality is the guiding principle of functional programming. Students will learn to recognize generally applicable patterns in systems from diverse fields of computer science and how to use these patterns to understand abstractions and design patterns. Category theory is a mathematical tool for organizing mathematics, computation, science, and engineering around the principle of composition. Students will learn category theory through examples from functional programming, databases, linear algebra, design of engineered systems, and network science. The course will explore both theoretical and computational aspects of these applied category theory topics.
Georgia Tech Professional Education
- Big Data Analytics with J. Poovey, D. Ediger, and M. Rost. Fall 2016
Teaching Assistant at Georgia Tech
- CSE 6643 Numerical Linear Algebra under Prof. Haesun Park, Spring 2016
- CSE 6220 High Performance Computing under Prof. Srinivas Aluru, Spring 2014
- Rohit Varkey MS CS Georgia Tech
- Pushkar Godbole MS CSE Georgia Tech
- Nate Knauf BS CS Georgia Tech
- Micah Halter BS CS Georgia Tech
Honors Awards and Fellowships
- National Defense Science and Engineering Graduate Fellow 2013 - 2016
- Presidential Fellowship for Graduate Study at Georgia Tech 2012 - 2016
- University Scholar at the University of Florida 2011 - 2012
- Kermit Sigmon Scholarship for service to the mathematical community 2012
- Tau Beta Pi, Engineering Honor Society, Georgia Tech Chapter 2015 - Present
- Phi Beta Kappa, University of Florida Chapter 2012
Leadership and Service
- Tau Beta Pi Atlanta Alumni Chapter President, 2017-2018
- Georgia Tech College of Computing Graduate Student Association VP for the School of CSE, 2015-Present
- Pi Mu Epsilon Chapter President, 2011-2012
- I organized a series of talks for the mathematics students at UF on diverse mathematical topics and skills like LaTeX, programming and technical communication in the field.
- Eagle Scout, 2009-Life
- Some of my technical skills:
- Programming languages (most familiar to least) Julia, Golang, Python, C, Bash, SQL, Matlab, C#/Java
- Computational Data Analysis (pandas, pylab, Jupyter Notebook)
- Web development with Golang and Python (flask)
- Database Applications primarily with PostgreSQL and MongoDB
- Continuous Integration/Deployment, Docker, DC/OS, Kubernetes
- Test Driven Development (TDD)
- Familiar with
*NIXcommand line, git, make, \LaTeX
- Avid Linux User