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This page gives an overview of my professional and academic career. If you want a condenced one-page version, then see my resume instead. This page will elaborate more of whats on the resume and additionally describe classes I've taken.

Education

Master's of Science - Computer Science: January 2023 - December 2024, University of North Texas

• GPA: 3.875
• Thesis: Reducing the Amount of Duplicated Values in the GPU L2 Cache during Lowered Convolution (in progress); advisor: Dr. Hui Zhao

Bachelor of Science - Computer Science: August 2020 - December 2022, University of North Texas

• Dean's List, 2020
• President's List, 2021 - 2022
• Honor's College: 2020 - 2022
• GPA: 3.96
• Magna Cum Laude
• Thesis: Syntax as a Tool of Thought; advisor: Dr. Barrett Bryant

Coursework

Highlighted classes:

• Master's Thesis
• Computer Architecture
• Deep Learning
• Parallel Processing
• Bachelor Thesis
• Natural Language Processing

Extended Summary

• Master's Thesis
• Embedded Hardware
• Computer Architecture
• Empirical Analysis
• Deep Learning
• Introduction to Computer Security
• Parallel Processing
• Programming Languages (Graduate)
• Analysis of Computer Algorithms
• Bachelor Thesis
• Introduction to Compilers
• Programming Languages (Undergraduate)
• Database Administration
• Fundamentals of Database Systems
• Natural Language Processing
• Algorithms
• Introduction to Computer Networks
• Social Issues in Computing
• Mathematical Modeling
• Systems Programming
• Software Engineering
• Data Structures
• Digital Logic Design
• Proposal Writing
• Technical Writing
• Assembly and Computer Organization
• Foundations of Data Structures
• Foundations of Computing
• Introduction to Computer Science

Skills

Programming Languages and Tools

• Advanced proficiency
  • C
  • C++
  • CUDA
  • GPGPU-Sim
  • Python
  • NumPy
  • PyTorch
  • Java
  • C#
• Intermediate proficiency
  • HTML
  • CSS
  • Javascript
  • MySQL
  • PostgreSQL
  • R
  • Lisp
  • Lex/Yacc
  • PHP

Operating Systems and Platforms

• TACC/Lonestar6 Supercomputer
• Linux (Ubuntu, Arch, Ubuntu Server)
• Windows
• Amazon AWS
• Docker
• Splunk
• Kubernetes

Soft skills

• Communication
• Teamwork
• Adaptability
• Problem-solving

Research

Posters

• Applying Transfer Learning to Defect Graph Neural Networks for Defect Formation Energy Predictions
• Graduate assistant; presenting on October 4th during UNT's Research Day (poster, report, GitHub).
• Guided two undergraduate students in UNT's Summer AI REU by holding weekly meetings and managing tasks for each student to complete.
• Showed how transfer learning could be used to improve the performance of a deep learning model in calculating the defect formation energy of crystals with one atom removed.
• Used real-world reference information from physics to identify a theoretical basis for the improvement before implementing an improvement on an existing model.
• GPU Implementations of Image Recognition Neural Network Architectures
• Main author; presented July 2022 during UNT's REU Showcase (poster, report, GitHub).
• Collaborated with another undergraduate student to show how GPU implementations of different deep-learning operations can lead to faster inference with a much lower overall latency compared to equivalent Python and C versions.
• Obtained uncompressed model weights from Keras for different deep-learning models including Alexnet, LeNet, and VGG16, and ported those weights through a custom C library for comparison.
• Utilized NVIDIA tensor cores to show how the matrix-multiplication operation can be further accelerated, including how 2d convolution can be lowered into matrix-multiplication and accelerated the same way.
• Crafted figures that clearly depict the performance improvements of each method.

Papers

• Deputy NoC: A Case of Low Cost Network-on-Chip for Neural Network Accelerations on GPUs
• Coauthor; submitted, waiting for a response from reviewers.
• Maintained the benchmark suite GitHub, adding new management utilities for simplifying bulk-processing of tests.
• Extracted the computation graph of the YOLOv5l image-recognition model and replaced 2D convolution with a version that uses CUTLASS.
• Scraped and preprocessed the ImageNet dataset and sorted the images into different buckets based on each baseline model's confidence in recognizing the image classification.
• Elastic-Float: Lossy Cache Compression for Cost Effective Neural Network Acceleration
• Coauthor; submitted, waiting for a response from reviewers.
• Maintained a GitHub repository containing the benchmark for which the GPU modification was measured against.
• Turned popular deep-learning models including AlexNet, ResNet, and VGG, into a computation graph of individual operations, which allowed us to inject our own replacement operations with new functionality.
• Assisted in collecting data for figures and managing tests to be executed on a supercomputer.
• Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis
• Coauthor; accepted, ISPASS 2023
• Refactored the GPU gene clustering benchmark to use CUDA Dynamic Parallelism, improving the overall performance by 24.2% and allowing us to analyze the utilization of CDP in a GPU simulator.
• Organized tests that would run on the simulator and collected data.

Work Experience

Teaching Assistant: [UNT] May 2023 - July 2023; January 2024 - current

• Created assignments and labs that meet the course objectives and allow the student to demonstrate their knowledge of the subject.
• Held office hours for students to request 1-on-1 tutoring on the course subject.
• Led weekly recitations for each class section with personally-created lecture material to aid students in learning the big-picture ideas of the course.
• Graded assignments, proctored students, and assisted the professor wherever required.
• Assembly and Computer Organization
• Foundations of Cybersecurity
• Data Mining
• Software Engineering
• Network Administration

Instructional Assistant: [UNT] January 2023 - May 2023; August 2023 - December 2023

• Graded assignments on a regular schedule, and met with students individually to justify grading decisions.
• Held office hours for students to request 1-on-1 tutoring on the course subject.
• Proctored students during exams and assisted the professor throughout the course whenever required.
• Assembly and Computer Organization
• Computer Networks

Student Researcher: [UNT] August 2022 - current

• Modified a GPU simulator with new microarchitectural changes that represent new kinds of GPUs that could be made to improve performance on different types of programs.
• Implemented new kinds of deep-learning models, including those that utilize Large Language Models (LLMs) with formatted English datasets and Graph Neural Networks (GNNs) with datasets from materials science.
• Managed the execution of tests that demonstrate the effects of the research project compared to a baseline, and created figures that effectively display the data collected.
• Kept up to date with the latest research in computer architecture, deep learning, and materials physics to help think of new changes that could improve on established baselines.
• Collaborated with PhD students, Master's students, undergraduate students, and professors both inside and outside of UNT.

NSF/UNT Deep Learning REU: May 2022 - July 2022

• Showed how utilizing GPUs for deep-learning workloads leads to massive improvements in performance (poster, report, GitHub).
• Presented the results of the project to an audience, justifying the motivation for the project, the history of using GPUs in deep learning, and explanations for the results.
• Learned how to successfully complete a research project with the help of graduate students and professors.

Online Private Lessons Instructor: [iD Tech] January 2021 - June 2022

• Taught over 600 private lessons to students in Algebra, Geometry, Calculus, Game Programming, Data Structures, and Algorithms.
• Helped students complete long-term projects, meeting regularly to assist in solving complex, domain-specific problems.