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