BioMedsAI Inc, is a public benefit corporation for biomedical research and discovery.

Md Babu Mia founded BioMedsAI, Inc. and leads it. He is a stem cell scientist and computational biologist who has worked on both sides of the bench, running the experiments and then writing the software to analyze the data. He was Assistant Professor of Medicine in Hematology and Medical Oncology at the Icahn School of Medicine at Mount Sinai, where he led single-cell genomics for NCI-funded grants and for industry-sponsored studies, working from lab experiments to library preparation, sequencing through to the analysis and interpretation. That work identified new genetic drivers and therapeutic vulnerabilities in myeloid malignancies and built patient-derived models, with papers in Blood, Leukemia and others. Earlier he worked at Indiana University. Alongside the biology he has published numerous open-source repositories, from Bayesian cell-type annotation and image-based DNA damage classification to a large library of evidence-linked AI skills for genomics, clinical workflows and drug discovery with a runtime that runs them across commercial and local language models, and his work has been adopted by leading LLM companies' AI models. He builds these systems with human review checkpoints, source-reliability metadata and audit trails.

Minhaz Ud-Dean leads the computational biology platform at BioMedsAI, Inc. Dr. Uddean is an applied physicist, engineer, and computational biologist who specializes in mathematically modeling complex biological systems. For the past four years, he worked as a bioinformatician at a leading medical center, where he built pipelines for detecting variants, duplications, copy number changes, and fusions in clinical samples. Previously, he worked at the bioinformatics core facility of the Helmholtz Center Munich. Beyond his contributions to computational biology and bioinformatics, Dr. Uddean brings strong academic credentials to BiomedsAI, Inc. He earned his doctorate from ETH Zurich, one of Europe's top universities and a globally recognized center of scientific excellence, where he developed methods and frameworks for inferring biological networks from expression data. He then completed postdoctoral training at Columbia University, further deepening his expertise in computational and quantitative biology. His research interests span biophysics, computational biology, and mathematical modeling, and his work has been published in leading journals including Bioinformatics, Nature Communications, Science Immunology, and Proceedings of the National Academy of Sciences.