Jonathan L. Golob, MD, PhD

I lead an engineering team within the Biomedical AI group in R&D at GSK, applying cutting-edge AI/ML to accelerate drug and vaccine development for infectious diseases.

• Engineer — build robust models for prediction of neutralization, antigen engineering, and viral evolution.
• Engineering manager — built and now leads a multi-continental engineering team; recruited and onboarded engineers, bringing them to full productivity through structured mentorship.
• Responsible AI — piloted a project for GSK’s Responsible AI programme and authored a perspective on the work.
• Subject-matter expert — clinical-trial design and implementation, EHR data, vaccines, antimicrobials, and antimicrobial resistance.
• Fleming Initiative leadership — the lead Data & AI/ML engineer across all six Grand Challenges; scientific co-lead for the antibiotic and antifungal Grand Challenge; GSK organizing-committee member.

I led a research group focused on the practical clinical translation of microbiome science to patients within precision medicine — pairing novel computational techniques with cutting-edge in vitro and in vivo models — and responded to the COVID-19 pandemic, caring for hundreds of critically ill patients while developing and running human observational and interventional trials.

• Computational biologist — multi-omics integration, massive multi-study datasets, and novel approaches and workflows; technical team leader with hands-on software depth.
• Human clinical trialist — observational trial development, novel therapeutic and target development, protocol writing, safety monitoring, and implementation.
• Clinical care — transplant infectious disease; cancer, solid-organ and hematopoietic stem cell transplant.
• Translational research — inflammatory bowel disease, stem cell transplant, immunotherapy, and prematurity / birth outcomes.
• Basic research — microbe–microbe and host–microbe interaction modeling; stem-cell biology and organoids.

Postdoctoral fellow studying how the gut microbiome relates to and mechanistically contributes to outcomes in patients undergoing hematopoietic stem cell transplant. Mentor: David Fredricks.

• Combined human observational study data with novel analytic and computational techniques.
• Attending physician specializing in cancer-related infections.

My doctoral work used the (then-new) pluripotent stem-cell model to establish the basic epigenetic mechanisms behind the earliest stages of human development. I employed some of the earliest “next-generation” high-throughput sequencing and built a novel computational pipeline integrating transcriptional micro-array and tiling-array data. Mentor: Charles “Chuck” Murry.

Develops deep learning models to support biodefense and detect anomalous or potentially concerning uses of AI in health and biological contexts.

Applies responsible AI practice to clinical AI safety, model evaluation, and governance for tools deployed in high-stakes scientific and healthcare settings.

Wrote Delayed Critical, a researched stage play about nuclear safety, institutional accountability, and the failure of technical organizations to act on known risk. The work parallels contemporary concerns in AI governance, oversight, and sociotechnical risk.

Building on MaLiAmPi, an atlas of the human microbiome in health and disease — harmonizing new raw data into existing feature sets, a key step toward clinical translation of microbiome-based predictive models like those from the preterm-birth DREAM challenge.

Led a crowdsourced machine-learning challenge to identify pregnancies at high risk of early preterm birth from vaginal-microbiome data, validated on independent datasets harmonized post-hoc. With Marina Sirota and Tomiko Oskotsky.

Identified the clinical relevance of microbiome butyrate production inhibiting gut recovery after injury during hematopoietic stem cell transplant — combining human observational data with organoid-based in vitro modeling — and latent HHV-7 infection of epithelium as a modulator of host–microbiome interactions.

Decoding the meaning of genetic variation — especially “variants of unknown significance” — for a general audience, with Ranjani Ramamurthy.

Core method behind MaLiAmPi — generalizable, taxonomy-independent microbiome features via phylogenetic placement.

Answered readers' science questions; extensive coverage of the 2008 financial crisis and the 2011 Fukushima disaster.

Long-form science & technology features for a general audience.

Public-facing analysis from a physician-scientist.

Decoding the genetic jigsaw: interpreting variants of unknown significance and cancer screening.

The lead Data & AI/ML engineer across all six Grand Challenges; scientific co-lead for the antibiotic and antifungal Grand Challenge; GSK organizing-committee member.

Provided medical expert testimony on protecting incarcerated people from COVID-19.

Protocol development, safety monitoring, and implementation.