About Me

I’m an engineering student with a strong interest in applied mathematics, data science and AI. I enjoy turning complex problems into practical, data-driven solutions, whether through analysis, modeling, or machine learning. This space is my personal log where I experiment, learn, and build; exploring how data turns into insight and ideas become real-world impact. Each project reflects curiosity, problem-solving, and a constant drive to grow.

  • Programming Languages:
    Python, C, C++, JAVA, SQL, MATLAB, HTML, CSS, JavaScript
  • Data Science:
    Pandas, Tensorflow, NumPy, Matplotlib, Seaborn, PowerBI
  • Competitive Programming:
    see my Codeforces and LeetCode profiles
  • Other Tools:
    Git, Github
  • Internship - National Agricultural Bank (BNA)
    - Built automated data preprocessing pipelines using Python for analytics and accounting tasks
    - Cleaned, transformed, and merged large financial datasets, significantly reducing processing time
    - Prepared high-quality data for modeling and dashboard refreshes through integrated Power BI workflows
  • Current Education
    2nd-year Applied Mathematics and Modeling engineering student at ENSIT, specializing in Data Science and AI.
  • Pre-engineering Degree
    2 years - Maths & Physics at IPEIT
  • NerData ENSIT Club - 2 years
    - Project Manager of the club
    - Conducted ML workshops and mini hackathons
    - Mentored participants in AI and data science projects
  • Community Engagement
    - Active participation in competitive programming contests and AI hackathons
    - Organized club events to foster learning
    - Contributed as part of the organizing committee for club events
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My Projects

HealthCast

Following the participation in the 48h SENECA HACK 2025, Healthcast is an AI-powered app that transforms a simple natural language prompt about your health, lifestyle, and goals into a personalized fitness & nutrition plan. It produces an engaging podcast-style audio file along with a short markdown summary that acts as a quick guide.

AutoInsight

an LLM-powered multi-agent system that automates end-to-end machine learning pipelines from raw data to final insights. Built on a LangGraph-based architecture, it orchestrates specialized agents for EDA, preprocessing, feature engineering, and modeling, while an LLM-driven NLP parser extracts user intent and target variables directly from natural language input. The system ensures robustness through validation mechanisms and generates a structured report summarizing insights, model decisions, and performance.

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