ABOUT ME

Shankaracharya, Ph.D. 

Senior Bioinformatics Consultant | Computational Biology | Multi-Omics | AI/ML for Life Sciences

I am a Senior Bioinformatics Scientist and computational biology consultant with a Ph.D. in Bioinformatics and over 12 years of post-doctoral experience working at the intersection of genomics, AI/ML, and therapeutic development across biotechnology, clinical diagnostics, and academic research.

My work spans the full spectrum of modern bioinformatics — from designing production-grade NGS pipelines and multi-omics integration frameworks to building machine learning models for biomarker discovery, CRISPR gene editing analysis, and precision medicine applications. I have delivered computational solutions for organizations ranging from early-stage gene editing biotechs to major academic medical centers and cancer research institutions.

Over the course of my career, I have:

• Designed and deployed production-level bioinformatics pipelines for whole genome sequencing, whole exome sequencing, RNA-Seq, single-cell RNA-Seq, amplicon sequencing, and CRISPR-based assays — including Digenome-seq, ONE-seq, and GUIDE-seq — at organizations including SeQure Dx, Prevention Genetics, UMASS Medical School and UT MD Anderson Cancer Center.

• Led multi-omics data integration projects spanning genomics, transcriptomics, proteomics, and metabolomics to identify biomarkers and characterize disease mechanisms in neurodegenerative diseases, oncology, and rare genetic disorders.

• Built AI and machine learning pipelines — including Logistic Regression, SVM, Random Forest, XGBoost, and Bayesian/probabilistic models — for disease risk prediction, therapeutic guide RNA selection, variant pathogenicity classification, and personalized biomarker discovery.

• Developed interactive analytical tools and dashboards (R Shiny, Python Streamlit) that make complex genomic data accessible and actionable for scientific and clinical stakeholders.

• Managed cloud computing infrastructure on AWS and Google Cloud Platform, including automated data ingestion, scalable storage, job orchestration, and containerized (Docker, Nextflow) workflow deployment.

• Contributed to 35 peer-reviewed publications in journals including Nature Genetics, Nature Biotechnology, Neuron, Cell Reports, and Cancer Discovery, reflecting a commitment to scientifically rigorous, reproducible work.

I hold an AWS Certified Cloud Practitioner certification and bring deep hands-on expertise in Python, R, Bash/Unix scripting, SQL, and the full modern bioinformatics toolstack.

My journey in bioinformatics began with my doctoral research at the Birla Institute of Technology, where I built one of the earliest machine learning systems for diabetes risk prediction in Indian populations — long before AI in healthcare became a buzzword. That project instilled in me a principle I have carried through every role since: that computational tools only matter if they solve real problems for real people.

That conviction led me through a postdoctoral fellowship at MD Anderson Cancer Center, where I developed ML-based cancer risk prediction software and contributed to the discovery of cancer susceptibility genes in Lynch Syndrome families. It carried me to UMass Medical School, where I worked on some of the largest neurodegenerative disease genomic cohorts in the world, helping uncover the genetic underpinnings of ALS and Parkinson's disease. And it shaped my work at SeQure Dx, where I built the bioinformatics infrastructure that powered a novel CRISPR gene editing diagnostics platform from the ground up.

Across every environment — startup, clinical lab, academic medical center, cancer research institution — I have operated as someone who builds things that work, communicates clearly across technical and scientific audiences, and takes ownership of outcomes from first line of code to final deliverable.

I partner with biotech and pharmaceutical companies, academic research groups, clinical genomics laboratories, and digital health organizations that need senior-level bioinformatics expertise without the overhead of a full-time hire. Whether you need a complete analytical pipeline built from scratch, an existing workflow optimized and containerized for production, a machine learning model developed and validated for a specific biological problem, or a scientific collaborator to help make sense of a complex dataset — I can help.

I work remotely and am comfortable engaging on projects of all sizes, from focused short-term analyses to long-term embedded partnerships.

If you are working on a problem at the intersection of biological data and computational science, I would love to hear about it.

📧 shankaracharya2222@gmail.com📞 713-497-6813 🔗 LinkedIn🔬 Google Scholar — 35 Publications💻 GitHub

A few things worth knowing about how I work: I write clean, documented, version-controlled code. I communicate in plain language, not jargon. I deliver what I commit to. And I care deeply about the science behind the data — because I know that behind every dataset is a research question, and behind every research question is ultimately a patient.