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pritampanda15/README.md

Pritam Kumar Panda

Bioinformatician @ Stanford | AI Research Scientist (LLMs, Deep Learning) for Protein Modeling & Drug Discovery | Next-Gen Intelligent Systems & Open-Source Scientific Software Developer | Google Certified Professional | Nextflow Ambassador

LinkedIn Twitter Google Scholar Portfolio Stanford Profile YouTube Channel


About Me

Research Scientist with 8+ years of experience building scalable ML systems and GenAI infrastructure for healthcare and biological applications. Specialized in ML infrastructure, multimodal foundation models, and production-grade data pipelines, translating research into reliable, evaluated systems deployed in real-world health settings.

I'm currently working as a Postdoctoral Scholar at Stanford University School of Medicine, specializing in AI-driven protein design and molecular modeling for battlefield medicine applications. As a Research Scientist with 8+ years of experience, I advance computational biology through the integration of algorithmic innovation and scalable deployment.

I create open-source tools with strong familiarity in full-stack frameworks, uniting scientific research with robust software engineering. My expertise spans automation, performance profiling, and workflow optimization for high-throughput biological data—driving faster therapeutic discovery and measurable R&D impact. I define research frameworks and evaluation methodologies that shape community standards, lead cross-sector collaborations, and mentor emerging scientists. I architect end-to-end workflow systems from foundation models to active learning pipelines transforming computational advances into precision medicine breakthroughs.


Code Time

Profile Views

🐱 My GitHub Data

📦 3.7 MB Used in GitHub's Storage

🏆 44 Contributions in the Year 2026

🚫 Not Opted to Hire

📜 68 Public Repositories

🔑 22 Private Repositories

I'm a Night 🦉

🌞 Morning                405 commits         ███░░░░░░░░░░░░░░░░░░░░░░   12.07 % 
🌆 Daytime                558 commits         ████░░░░░░░░░░░░░░░░░░░░░   16.63 % 
🌃 Evening                206 commits         ██░░░░░░░░░░░░░░░░░░░░░░░   06.14 % 
🌙 Night                  2187 commits        ████████████████░░░░░░░░░   65.17 % 

📅 I'm Most Productive on Saturday

Monday                   322 commits         ██░░░░░░░░░░░░░░░░░░░░░░░   09.59 % 
Tuesday                  203 commits         ██░░░░░░░░░░░░░░░░░░░░░░░   06.05 % 
Wednesday                276 commits         ██░░░░░░░░░░░░░░░░░░░░░░░   08.22 % 
Thursday                 318 commits         ██░░░░░░░░░░░░░░░░░░░░░░░   09.48 % 
Friday                   292 commits         ██░░░░░░░░░░░░░░░░░░░░░░░   08.70 % 
Saturday                 1775 commits        █████████████░░░░░░░░░░░░   52.89 % 
Sunday                   170 commits         █░░░░░░░░░░░░░░░░░░░░░░░░   05.07 % 

📊 This Week I Spent My Time On

🕑︎ Time Zone: America/Los_Angeles

💬 Programming Languages: 
Markdown                 1 hr 3 mins         ███████████░░░░░░░░░░░░░░   45.00 % 
TeX                      29 mins             █████░░░░░░░░░░░░░░░░░░░░   21.12 % 
Python                   18 mins             ███░░░░░░░░░░░░░░░░░░░░░░   13.34 % 
YAML                     12 mins             ██░░░░░░░░░░░░░░░░░░░░░░░   09.17 % 
JSON                     7 mins              █░░░░░░░░░░░░░░░░░░░░░░░░   05.20 % 

🔥 Editors: 
VS Code                  2 hrs 20 mins       █████████████████████████   100.00 % 

🐱‍💻 Projects: 
PandaDock                1 hr 5 mins         ████████████░░░░░░░░░░░░░   46.91 % 
nf-core-vina             37 mins             ███████░░░░░░░░░░░░░░░░░░   26.55 % 
biomethod                32 mins             ██████░░░░░░░░░░░░░░░░░░░   23.12 % 
ssn-notation             4 mins              █░░░░░░░░░░░░░░░░░░░░░░░░   03.42 % 

💻 Operating System: 
Mac                      2 hrs 20 mins       █████████████████████████   100.00 % 

I Mostly Code in Python

Python                   41 repos            ████████████░░░░░░░░░░░░░   49.40 % 
HTML                     13 repos            ████░░░░░░░░░░░░░░░░░░░░░   15.66 % 
TypeScript               5 repos             ██░░░░░░░░░░░░░░░░░░░░░░░   06.02 % 
Swift                    4 repos             █░░░░░░░░░░░░░░░░░░░░░░░░   04.82 % 
JavaScript               4 repos             █░░░░░░░░░░░░░░░░░░░░░░░░   04.82 % 

Timeline

Lines of Code chart

Last Updated on 07/01/2026 18:47:02 UTC

Most Utilised Skills

Top languages

Operating systems

Workflow Managers

Fun Frameworks

Version control tools

Dev Ops

CI/CD

Cloud & Workload managers

SGE


Educational Outreach

Created comprehensive tutorial series on YouTube covering:

  • AI-driven drug design
  • NGS analysis workflows
  • Quantum chemistry simulations
  • Molecular dynamics
  • Machine learning in bioinformatics

Impact: Empowering global learners to adopt computational research tools effectively

Connect With Me

I'm always open to collaboration opportunities in:

  • 🧬 AI-driven drug discovery
  • 🔬 Computational biology pipelines
  • 🤖 Foundation models for biological systems
  • 📊 Multi-omics data integration
  • 🚀 Open-source scientific software

Contact:

Pinned Loading

  1. Seqcore Seqcore Public

    High-performance biological sequence analysis library for Python. A unified, GPU-accelerated library for genomics, proteomics, structural biology, and drug design.

    Python 5 2

  2. PandaDock PandaDock Public

    PandaDock: A Physics-Based Molecular Docking using Python

    Python 89 18

  3. Oncology-Drug-Response-Prediction-System_DL-RAG Oncology-Drug-Response-Prediction-System_DL-RAG Public

    This project implements a clinical decision support system that uses Deep Learning (DL) to predict cancer cell line response to various drugs and employs a Retrieval-Augmented Generation (RAG) pipe…

    Python 3 2

  4. ADMET-Prediction-System-Graph-Neural-Networks-with-RAG ADMET-Prediction-System-Graph-Neural-Networks-with-RAG Public

    A deep learning system that predicts Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties from molecular structures. The system combines Graph Neural Networks with a RAG…

    Python 12 1

  5. Literature-Intelligence Literature-Intelligence Public

    Track, index, and analyze scientific papers from PubMed and bioRxiv for specific drug targets, compounds, or therapeutic areas.

    Python 7 4

  6. Drug-Repurposing-Intelligence-System Drug-Repurposing-Intelligence-System Public

    An AI-powered system for discovering novel drug-disease relationships using Relational Graph Convolutional Networks (R-GCN) and Retrieval-Augmented Generation (RAG). This project integrates heterog…

    Python 6 1