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raghvendra5688/README.md

Hi, I’m Raghvendra Mall! 👋

🧬 Applied AI | IEEE Senior Member | Computational Biology Leader

With 15+ years of experience in data science, complex networks, and computational biology, I specialize in providing data-driven solutions across diverse domains like healthcare, life sciences, materials science, and enterprise AI. My passion is translating advanced research into real-world impact.

  • IEEE Senior Member | IEEE CS Top 30 Early Career Professional
  • 100+ peer-reviewed articles (h-index: 39) in top-tier journals (TNNLS, Pattern Recognition Letters, Nature Medicine, Nature, Cell, Science Immunology, and more)
  • 19+ ML tools designed & deployed for actionable insights from big data
  • DREAM Challenge wins | Kaggle Top 3% finisher | International partnerships & cross-disciplinary leadership

🔬 Core Competencies

  • Leadership: Team Building | Budget & KPI Management | Stakeholder Communication
  • Deep Learning: Transformers, CNN, LSTM, GNN, TabPFN, LLM Fine-tuning (LoRA, LoftQ, RAG, Multi-modality, SFT)
  • AI Libraries: PyTorch, HuggingFace, PyG, scikit-learn, H2O, CrewAI, LangChain, Gradio, Streamlit
  • Computational Biology: Regulatory genomics, network analysis, disease module identification
  • NGS Analysis: RNA-Seq, single-cell multiomics, spatial transcriptomics, immunomics, metagenomics
  • Bioinformatics Tools: Salmon, Kallisto, STAR, HiSAT2, RSEM, Seurat, Scanpy, Snakemake, and more
  • Translational Medicine: Drug repurposing (COVID-19, AML, lung cancer), biomarker discovery
  • Biomedical Applications: ML for sleep efficacy, glucose monitoring, and more

💡 Skills & Tech

  • Network Analysis: Pagerank, community detection, Cytoscape, Gephi, ggraph
  • LLMs: Claude CLI, LLAMA, GPT, Qwen (local & API)
  • Programming: Python, R, C++
  • Environments: AWS, GCP, HPC (LSF), Docker

🚀 Featured Projects


🌎 Connect with Me


Profile Views


“Passionate about pushing the boundaries of AI x Bio for societal benefit.”

Open to advisory/consulting roles — especially in generative AI and life sciences. Let’s build something impactful!

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  1. PT-AML2.0 PT-AML2.0 Public

    Predict AML 2.0

    Python

  2. PEARL PEARL Public

    PEARL: Parameter-Efficient Adaptation with Retrieval-Augmented Learning for Molecular Property Prediction

    Python 1

  3. Drug-Repurposing Drug-Repurposing Public

    A Consensus of In-silico Sequence-based Modeling Techniques for Compound-Viral Protein Activity Prediction for SARS-COV-2

    Python 2 2

  4. Druggability Druggability Public

    Consensus of ML, Computational Biology and Chemistry approaches for druggability of FDA approved drugs against all human proteins.

    Jupyter Notebook 1

  5. sameerkhurana10/DSOL_rv0.2 sameerkhurana10/DSOL_rv0.2 Public

    deep protein solubility prediction

    Python 41 14

  6. BCrystal BCrystal Public

    BCrystal: An Interpretable Sequence-Based Protein Crystallization Predictor

    R 4 4