About Me

Hello!

I'm Adam Azeez, a passionate computer science enthusiast currently studying at Georgia Tech. My journey into the world of computer science started with a fascination for the creativity and power that technology offers. Over time, this curiosity has evolved into a deep interest in the intersection of software engineering and machine learning in order to bridge the equip gap of access to technology.

Relevant Coursework

CS 1332

Through the use of java, learned fundmanetal data structures and algorithms in arrays, trees, graphs, and string pattern recognition

CS 6601

Learned advanced algorithms like Tri-directional and Bi-directional A*, game search algorithms like min-max (and alpha beta pruning), and machine learning methods including supervised, unsupervised, dimensionality reduction, and deep learning with TensorFlow

CS 2340

Learned about software design and how to create scalable software at the industry level

CS 3510

Delved into divide and conquer, dynamic programming, graph, and complexity theory algorithms

CS 2110

Learned about Computer at the hardware level along with different low level software such as Circuit Sim, Assembly, and C

Skills & Projects

Skill 2
Java
Skill 2
Python
Skill 2
C
Skill 2
R
Skill 2
SQL/MySQL
Skill 2
Go
Skill 2
React
Skill 2
Node.js
Skill 2
MongoDB
Skill 2
AWS (Lambda, S3, EC2)
Skill 2
Next.js
Skill 2
Plaid
Skill 2
Langchain
Skill 2
OpenAI

Bank Chatbot

● Utilized Plaid API to retrieve tokens for secure bank interactions, enhancing data security by 99.9%
● Developed frontend and login interface with React.js, Bootstrap, and OAuth2, improving authentication speed
● Created RESTful API endpoints for efficient Plaid API communication, thus reducing response time
● Automated data storage scripts for transactions and user info in MySQL
● Built an LLM with OpenAI and LangChain for database interaction, answering bank statement queries with 85% accuracy and growing user base through strategic outreach and waitlists, targeting 8-10% monthly growth

Congress Simplified

● Scraped user-inputed bills using Selenium and connected the frontend and backend with Flask.
● Developed frontend with React.js and deployed using Vercel and Next.js, enhancing user interface responsiveness
● Implemented LLM to summarize scraped bills and displayed voting tables and placed in the top 10% out of over 500 teams in a competition

AI Customer Support

coming soon!

Personal Inventory Manager

● Developed an inventory management system using Firebase
● Designed a responsive website with Next.js and Material UI, allowing users to add, delete, and upload images of items, increasing inventory management efficiency by 50%
● Utilized GPT Vision API to classify images and update Firebase, achieving 90% classification accuracy

Experience

Georgia Pacific/Koch Industries

Data Engineer Intern

● Developed LLMs to translate files using OpenAI and Langchain, saving $175,000 in manual translation costs and and reducing completion time by 96%
● Created an end-to-end CI/CD pipeline with AWS Lambda, EC2, and S3, allowing users to upload files via a React.js frontend, and using Flask to communicate with the backend, improving process efficiency by 89%
● Implemented user authentication with Auth0, enabling different permission levels, enhancing security management

McGrath Lab @ Georgia Tech

Team Lead/Undergraduate Researcher

● Led a team of 4 in researching unsupervised clustering techniques to extract biologically significant behavior expressions, improving data analysis efficiency by 99% (as this was a novel technique in the lab)
● Created clustering models using sc-RNA brain data with DPLYR/Seuratin R, achieving 92% accuracy in identifying neuronal patterns

HeadStarter AI

Software Engineering Fellow

● building scalable projects that use industry-wide skills such as react, Next.js, Firebase, OpenAI, AWS, embeddings, vector DBs, and more

Exasense Technologies

CTO/Founder

● Developed a wirelessly networked IoT system using pandas, numpy, and matplotlib to convert chemical signatures from toxic gasses into readable data, increasing data accuracy by 60%
● Led the creation of ML models with TensorFlow, Keras, and scikit-learn to interpret gasses at ultra-low concentrations, achieving 95% sensitivity; published findings to MIT URTC and SENSORCOMM

resume

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