About Me

Welcome to My Bioinformatics Portfolio

I am an Computational Scientist/Bioinformatician with over 6 years of experience developing AI-enabled scientific workflows, bioinformatics software, data pipelines, and scalable computational solutions for genomics and multimodal biological data.

Holding a Ph.D. in Computational Genomics, my expertise spans molecular biology, cancer biology, and translating complex scientific requirements into production-ready, reusable software capabilities.

Core Expertise & Technical Focus

🧬 Single-Cell & Spatial Analysis

  • Pipeline Leadership: Lead the development of Python-based computational workflows for single-cell RNA-seq, spatial transcriptomics, Perturb-seq, and multiplexed imaging datasets.
  • Method Development: Developed STmut-hires, a modular spatial-genomics workflow, alongside specialized Dockerized solutions (like addImg2AnnData) integrated directly into commercial production pipelines.
  • Advanced Analytics: Experienced in 24-plex multiplex immunofluorescence analysis, cell segmentation, and spatial cell identification.
  • High-Impact Research: Primary author of spatial transcriptomics methods published in peer-reviewed journals like Genome Biology.

📊 Genomics Analysis

  • Translational Cancer Genomics: Developed comprehensive workflows for whole-genome (WGS), whole-exome (WES), and targeted sequencing data (UCSF500) across diverse sequencing platforms.
  • Variant & Mutation Workflows: Engineered robust frameworks for quality control, variant calling (including DeepVariant), and functional annotation utilizing large-scale files (FASTQ, BAM, VCF, GTF).
  • Proven Scientific Output: Co-authored genomic discovery papers in elite journals including Cell, Nature Cell Biology, and Science Advances.

💻 Software & Pipeline Development and Maintenance

  • Reproducible Engineering: 6+ years building automated, configuration-driven data pipelines using Python, Bash, Nextflow, and Linux across AWS cloud and HPC/Slurm environments.
  • Production-Grade Quality: Expert in modern software best practices including version control (Git/GitHub), containerization (Docker, Singularity), and automated CI/CD testing.
  • Rigorous Validation: Design and execute end-to-end testing strategies—including unit, integration, regression, and performance testing—to ensure absolute workflow reproducibility and reliability.

🤝 Collaboration with Other Teams & Scientists

  • Cross-Functional Bridge: Regularly collaborate across R&D, application scientists, software engineers, and product managers to translate complex scientific needs into concrete product capabilities.
  • Customer-Facing Support: Successfully supported 50+ external projects, directly troubleshooting data workflows, evaluating software performance, and gathering feedback to drive software iteration.
  • Multidisciplinary Alignment: Experienced in partnering directly with clinicians and experimental biologists to translate raw biological hypotheses into robust, objective computational solutions.

🤖 Scientific AI & Generative AI

  • Agentic Workflows: Design and optimize LLM- and agentic-AI workflows specifically built for automated biological interpretation, literature-guided evidence synthesis, and cell-type annotation.
  • AI-Genomic Integration: Built a custom agentic AI application that integrates LLM reasoning capacities directly with active spatial transcriptomics pipelines.
  • Rigorous AI Evaluation: Established benchmarking frameworks to systematically evaluate and verify the scientific accuracy, reproducibility, and trustworthiness of AI-generated biological insights.