Fred Hohman
/ CSE Ph.D. Student at GT

Hello, I'm Frederick.

But you can call me Fred. Nice to meet you.

I’m a Ph.D. student studying Computational Science and Engineering at Georgia Tech advised by Polo Chau and Alex Endert.

As a member of the Polo Club of Data Science, our research bridges data mining and machine learning techniques with principles from human-computer interaction and visualization to make interactive tools to help people understand and explore big data.

Here’s my CV.

That's me.
That's me.

I’m an INTJ born on the coast in Melbourne, Florida but currently live in Midtown in Atlanta, Georgia. I’m interested in the combination of math + art, or more specifically, the intersection of data science (machine learning, deep learning, and big data analytics) and visualization (visual analytics, information visualization, and digital design).

Considered by my family and peers to be a resident techie, I enjoy staying current with consumer technology and computer UI/UX design. When I’m not at my desk crunching numbers, I enjoy playing and discovering music, throwing frisbee, and riding motorcycles.


Education

Ph.D. in Computational Science & Engineering
Georgia Institute of Technology, Atlanta, GA
Present — Aug. 2015
Advisor: Polo Chau, Co-advisor: Alex Endert
Research interests: Explainable artificial intelligence, visual analytics, machine learning, deep learning
Qualifying exams passed Nov. 2016
Overall GPA: 4.00/4.00

B.S. in Mathematics, Area of Emphasis in Applied Mathematics
B.S. in Physics
University of Georgia, Athens, GA
Aug. 2011 — May 2015
Thesis: “3D Printing the Trefoil Knot and its Pages”
Overall GPA: 3.84/4.00, Magna Cum Laude

Research Experience

Georgia Institute of Technology, Atlanta, GA
Graduate Research Assistant, School of Computational Science and Engineering
Present — Aug. 2016, Advisor: Polo Chau, Co-advisor: Alex Endert
Member of the Polo Club of Data Science where we bridge data mining and machine learning techniques with principles from human-computer interaction and visualization to make interactive tools to help people understand and explore big data.

NASA Jet Propulsion Lab (JPL), Pasadena, CA
Creative Computer Scientist, Data Visualization Program
Summer 2017
Project: Cyber Visualization
• Intensive joint summer program between NASA JPL, Caltech, and Art Center creating interactive data visualizations for current scientific research.

Pacific Northwest National Laboratory, Richland, WA
National Security Ph.D. Intern, Data Sciences & Analytics Group
Summer 2016, Mentor: Nathan Hodas
Project: Understanding Deep Learning Models Via Visualization
• Developed Python code using Keras to generate images from deep neural networks to explore image classifiers’ ability to learn semantics.
• Research areas: Deep learning, image analysis, visualization.

Georgia Institute of Technology, Atlanta, GA
Graduate Research Assistant, School of Computational Science and Engineering
May 2016 — Aug. 2015, Mentor: Surya Kalidindi
Project: Material Informatics
• Built data-driven surrogate model for computationally expensive material grain growth simulations. Created property-structure linkages using machine learning pipeline to predict material properties. Contributed to direction and code repository of PyMKS package: Materials Knowledge Systems in Python.
• Research areas: Physical data science, material informatics, statistics.

Undergraduate Thesis and Research
University of Georgia, Department of Mathematics, Athens, GA
May 2015 — Jan. 2013, Advisor: David Gay
Project: 3D Printing the Trefoil Knot and its Pages
• Exploring 3D printing in topology. Programmed, designed, and 3D printed 34-piece, color-coordinated, and magnetized 3D puzzle of the trefoil knot fibration illustrating an open-book decomposition. Led 3D printing research and education in mathematics department.
• Research areas: 3D modeling, topology, visualization, mathematical exposition.

REU in Mathematics and Computational Science
Fairfield University, Department of Engineering, Fairfield, CT
Summer 2014, Mentor: Shanon Reckinger
Project: Numerical and Experimental Comparison of Oceanic Overflow
• Directly compared numerical solutions derived from the Navier-Stokes equations to designed experiments performed at the lab-scale to model specific ocean phoneme. Configured MIT General Circulation Model on a linux computer cluster to parallel compute numerical simulations while using MATLAB for pre- and post-processing data visualization.
• Research ares: Computational fluid dynamics, data visualization, applied mathematics.

Teaching

Georgia Institute of Technology, Atlanta, GA
Graduate Teaching Assistant, School of Computational Science and Engineering
Spring 2017, CSE 6242 Data and Visual Analytics Assisted in teaching and administration for Data and Visual Analytics (CSE 6242), a graduate course with 250+ students enrolled.

Contact

Fred Hohman
fredhohman@gatech.edu
Data Analytics & Simulation Lab
Klaus Advanced Computing Building
Georgia Tech
266 Ferst Dr NW
Atlanta, GA 30332

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