AI-focused MS Computer Science student at Pace University with a proven track record in machine learning and software development. Published IEEE researcher specializing in NLP and personality classification models. I architect and build full-stack applications that leverage machine learning to solve real-world challenges.
Seeking AI/ML engineering roles in innovative, data-driven teams.
- Building AI-powered platforms for career guidance and personalized recommendations
- Advancing NLP and LLM-based solutions for real-world applications
- Deploying scalable ML systems on AWS and Streamlit Cloud
Currently deepening my expertise in:
- 🧠 Machine Learning & Deep Learning architectures
- 🔍 Natural Language Processing applications
- ☁️ AWS cloud infrastructure for ML deployment
- 📊 Data analysis & visualization pipelines
I'm passionate about turning complex data into actionable insights and building intelligent systems that drive business value.
- Co-authored: "Personality Prediction Using ML and NLP" – Published in IEEE ICCUBEA 2023
Technologies: Python, XGBoost, Streamlit, OpenAI, AWS
- Engineered an end-to-end ML pipeline using XGBoost and NLP, achieving 78% accuracy and 89% ROC AUC on 6,200+ real resume-job pairs
- Automated model deployment and real-time inference, integrating OpenAI GPT-4 for personalized career recommendations
- Launched a full-stack ML app on Streamlit Cloud with robust error handling, CI/CD, and version control
👉 GitHub | Live Demo
Technologies: AWS, React, Node.js, OpenAI
- Orchestrated a full-stack recipe app on AWS (EC2, RDS, S3), achieving 100% uptime and $0 monthly cost
- Integrated OpenAI API for AI-powered recipe generation and personalized suggestions
- Built a scalable React frontend on AWS S3 with secure API integration
👉 GitHub | Live Demo
Technologies: CatBoost, TF-IDF, Streamlit
- Created a TF-IDF + CatBoost-based model with 83% accuracy
- Deployed an interactive Streamlit UI for live predictions and model insights
👉 IEEE Paper
Technologies: MySQL, Python
- Designed a donor platform with indexed search algorithms, reducing lookup time by 30%
Technologies: Python, Random Forest
- Built a Random Forest classifier to optimize class schedules and reduce conflicts by 20%
- Integrated predictions into a web application for real-time automated schedule optimization
- Pace University Masters Scholarship (Aug 2023)
- Code Runner Organizer, ACM RAIT (June 2022): Led a team of 20 volunteers, achieving record attendance of 500+ participants