My Curiosities and History

Responsibilities:

• Improved the core value prop of the job board by leveraging data to better match users to jobs.
• Streamlined processes and increased scalability.

Achievements:

• Contrastively finetuned vector search models, paving new avenues of product usage for our Web and API users.
• Utilized LLM agents for scraping over 30k jobs daily from 12 thousand career pages and job boards.
• Created an automation tool to auto-apply users to jobs, increasing monthly revenue by $5k and weekly average usage by 130%.

Designing, training, and integrating ML models related to Quality Assessment, Segmentation, Semantics, and Enhancement. Architecting a zero-click image and video enhancement pipeline for API users. Deploying internal tools for scalable data analysis, model comparison, and data engineering.

Improved job board core value proposition through data-driven matching. Contrastively finetuned vector search models, improving model inference costs and accuracy. Created automation tools increasing monthly revenue by $5k and weekly usage by 130%. Utilized LLM agents for scraping over 30k jobs daily from 12 thousand career pages and job boards.

Successfully completed my role as ML Engineering Co/Op where I prototyped Generative Agents Architecture built on LLMs and incorporated contrastive learning into text classification, improving accuracy from 93% to 99%.

Graduated with a Bachelor of Science in Computer Science, with concentration in Robotics and AI. Achieved Cum Laude honors with a GPA of 3.7/4.0.

Selected to lead effort for Generative AI development, Data Science & Data Engineering @ CAE USA. Work on Machine Learning models, natural language processing and intelligent agents. Brought various representation learning and deep learning techniques to the table; developed pretraining pipelines for BERT models, created a text augmentation library for text classification samples created via structured generation. Expanded LLM software capabilitites, made general purpose software for tackling various language understanding problems.

Organized IEEE's 2023 Fall Banquet. Showcased our Campus and Engineering footprint in Tampa Bay and at USF. Over 70 students and 15 IEEE professionals attended to learn more about our Organization's recent engineering impact.

Presented and led a technical workshop detailing how to train Nerual Networks in PyTorch with a Google Collab available. Find the resources for this workshop here.

Selected to lead discussion sessions for Data Structures and Program Design in C.

The purpose of this group is to promote the discussion and development of AI on college campuses through the IEEE Student Chapters. Our mission is to enable students to learn and develop with AI technologies and provide an environment where AI project development can occur. Also, the group will participate in one main project a year where students can collaborate on different aspects of software, hardware and engineering. We extended this collaboration to other universities such as ... via open-source software and extension of GPU access.

Educated younger audience in the core concepts involving artificial intelligence. Ran a curriculum based in Python and a custom software library developed by myself.

Topics include: Python, Search, Agents, Game logic principles, supervised learning, reinforcement learning

Travelled and represented USF at the largest VEXU competition in the world.

Team: Terri-BULL Robotics

Planned and oversaw Lab Tour, set up Robot Soccer Competition for the lab visitors.

Participated in workshops, Coding Interview practice, and shared side project development. Introduced to sophisticated robotics systems.

Server featured: Item shop, factions, custom skills, purchasable farms and resource mines, permissions system.