I’m a data-driven developer passionate about building tools that bridge technology and real-world impact. Below are highlights of my work—feel free to explore, collaborate, or reach out!

A Unified Platform for Drug Combination Analysis

• What it does: Aggregates data from PubChem, DrugBank, and PubMed APIs, cross-references academic articles via PubMed Central, and generates evidence-based insights for potential drug synergies.
• Tech Stack: Python, PostgreSQL, Elasticsearch, Streamlit (frontend), and custom ETL pipelines.
• Key Features:
  • Automated extraction of drug interaction data from unstructured PDFs using PyMuPDF and spaCy.
  • Risk-scoring algorithm for combinations, powered by Scikit-learn clustering.
  • Real-time visualization of molecular interactions with RDKit and Plotly.

❗ Medical Disclaimer:

SynergyMed Explorer is a research tool only. It does NOT provide medical advice. Always consult a licensed healthcare professional before making treatment decisions.

NLP-Driven Sustainability Insights from Twitter (2012–2022)

• What it does: Analyzes 10M+ tweets (Twitter/X) about sustainability, climate change, and ESG trends using NLP to map public sentiment and emerging topics.
• Tech Stack: Python, Tweepy (historical data), Apache Spark (distributed processing), Hugging Face Transformers (BERT for sentiment), and Tableau for dashboards.
• Key Features:
  • Topic modeling with BERTopic to identify shifts in sustainability discourse (e.g., rise of "circular economy" post-2018).
  • Geospatial analysis of tweet origins with GeoPandas and Folium.
  • Interactive timeline showing correlations between global events (e.g., COP summits) and tweet volume.

I believe in collaborative innovation! Here’s where I’ve pitched in:

• PyHealth (v2.3.0): Enhanced FHIR-standard medical data parsers for oncology datasets.
• Pandas: Optimized read_xml() performance for nested XML drug databases.