About me

Hey, I'm Brian Naklycky, a software engineer with expertise in machine learning, data engineering, and quantum computing. I hold a Master's in Computer Science from UT Austin and a Bachelor's from USF.

My technical skills include Python, C++, C, SQL, and various tools like Unix/Linux, Jenkins, GitHub, Snowflake, and AWS. I've interned at Toyota Financial Services, where I significantly improved data pipeline efficiency.

I've contributed to academic research in quantum network communication and developed a novel transfer learning method called BUS for efficient large language model training. With experience in data engineering, machine learning, and web development, I'm well-equipped to tackle complex challenges in software engineering and data science. My ability to communicate complex ideas effectively makes me valuable in both academic and industry settings.

Feel free to read my articles, experience, and projects below.

Articles

Work Experience

Graduate Teaching Assistant

Assisted in teaching a graduate-level Natural Language Processing course, covering foundational models to modern machine learning and AI architectures, wh...

Data Engineering Internship at Toyota

Achieved a 99.9% increase in efficiency by reducing time-to-delivery for new data pipelines from thousands of hours to minutes. Developed a Python scrip...

Teaching Assistant

Served as a Teaching Assistant for CNT 4104 Computer Networks Lab for IT, focusing on the OSI model and network protocol implementation. Guided students...

Research Assistant

Conducted innovative research on healthcare network infrastructure vulnerabilities, leveraging machine learning techniques to analyze and predict network ...

Web Developer

Designed and developed a responsive, multi-device compatible website for the USF Quantum Initiative using HTML, CSS, and JavaScript. Implemented ADA com...

Projects

EverywhereGPT

This project utilizes OpenAI’s API along with certain python libraries to allow you to use ChatGPT wherever you are typing. You can prompt an LLM at the push...

Bootstrapped Model UpScaling (BUS)

Developed BUS (Bootstrapped Model UpScaling), an innovative method combining nGPT architecture with fractional HyperScaling to enhance Large Language Mode...

Philosophical Similarity

Collected, cleaned, and analyzed a dataset of classic philosophical texts using GPT-2, demonstrating proficiency in natural language processing and data p...

NLP Model Improvement

Developed an academic machine learning replication paper that improved upon and scrutinized a state-of-the-art model’s performance on the Stanford Natural...

Deep Reinforcement Learning Agent

Led a team in developing a deep reinforcement learning agent for SuperTux Ice Hockey, implementing a Deep Q Network (DQN) algorithm using PyTorch. Achie...

Warehouse Inventory Management

Led a team in developing a comprehensive inventory management website for store owners, showcasing project management and leadership skills. Architected...

Image Classification with CNNs

Developed a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset, demonstrating proficiency in deep learning and computer visio...

Traveling Salesman Problem Solutions

Developed a comprehensive suite of 6 algorithms to tackle the Traveling Salesman Problem (TSP), a classic NP-hard optimization challenge:

Education

Masters of Computer Science from the University of Texas at Austin | Dec. 2024
Bachelors of Computer Science from the University of South Florida | Dec. 2022