We build multimodal artificial intelligence and computational genomics methods that turn biomedical data — images, sequences, and clinical signals — into discoveries and tools that reach patients.
Our work spans multimodal AI, genomics, and precision medicine — with a throughline of building methods rigorous enough for biology and reliable enough for care.
Foundational AI trained on millions of time-lapse images for embryo selection, automated blastocyst ploidy prediction, and validation of clinical efficacy.
Leading the NIH-funded Microbiome In A Bottle (MIAB) project. Modular metagenomic analysis systems (CAMP) and novel algorithms for microbial genomics, in collaboration with the international MetaSUB Consortium.
Deep learning that fuses pathology, radiology, and imaging — building models designed for real diagnostic and decision-support settings.
Algorithms and deep-learning frameworks for structural variant discovery and assembly.
Weakly-supervised tumor-purity prediction from H&E slides, prostate-cancer foundation models, and inference of lineage plasticity in metastatic disease.
AI for the automated detection of polycystic ovary syndrome and ovarian morphology on ultrasonography, and downstream reproductive-health applications.
est. 2016 · Weill Cornell Medicine
Associate Professor of Systems and Computational Biomedicine at Weill Cornell Medicine, Cornell University, and Co-Director of the Tri-Institutional Computational Biology & Medicine Ph.D. program (Weill Cornell, Memorial Sloan Kettering, and The Rockefeller University).
His research develops multimodal AI and computational methods across genomics, clinical imaging, and precision medicine. He is a member of the Englander Institute for Precision Medicine and the Meyer Cancer Center. He earned his Ph.D. in Computing Science at Simon Fraser University, with postdoctoral research at Brown University and Stanford University, and a fellowship at the Simons Institute, UC Berkeley.
A selection of recent peer-reviewed papers.
The researchers behind the lab, across the Tri-Institutional and Cornell programs.
Dr. Camir Ricketts → Bioinformatics Scientist - AI, NVIDIA · Dr. David Danko → CTO, Biotia · Dr. Dmitrii Meleshko → Faculty, ITMO University · Dr. Matthew Brendel → Senior Consultant, ClearView Healthcare Partners · Dr. Josue Barnes → AI Success and Strategy Lead, Arya Health · Dr. Hamid Mohamadi → Senior Applied Scientist, Amazon · Dr. Pegah Khosravi → Associate Professor, University of Central Florida (UCF) Institute for Artificial Intelligence · Dr. Wei Wei → Postdoc, MSKCC.
We welcome curious researchers who want to work at the intersection of computational genomics, machine learning, and medicine — from quantitative, biological, and clinical backgrounds alike.
Already admitted to one of our affiliated programs — the Tri-Institutional CBM program, the Weill Cornell Graduate School, Cornell Ithaca, Cornell Tech, and others? Email Dr. Hajirasouliha directly. Prospective students should apply to the relevant program first and mention the lab in their application.
We're using AI to change how reproductive medicine is practiced, and looking for a postdoc to help lead the next chapter.
We are recruiting a Postdoctoral Associate in AI for Reproductive Medicine to develop the next generation of our AI models: large-scale foundation models, multimodal learning, and clinically deployable tools that can improve outcomes for patients. Our research has already produced patented technologies, and you'll have the opportunity to help translate new models into deployable products, with exposure to IP and entrepreneurship along the way.
You'd be a great fit if you have (or are about to complete) a Ph.D. in machine learning, computer science, computational biology, biomedical engineering, or a related field, with strong deep-learning and generative-AI skills. I'm especially keen to hire someone who can build product- and clinical-grade systems: robust, well-engineered, validated models and software that move beyond prototypes into real clinical and product settings. Strong software engineering and experience with model deployment/MLOps, medical imaging, foundation models, or clinical/regulatory translation (e.g., software as a medical device) are valued; a passion for translational, patient-facing impact is essential. Exceptional candidates without a Ph.D. but with equivalent research and/or industry experience will also be considered for a comparable Staff or Research Associate position.
A highly collaborative home across the Tri-Institutional community (Weill Cornell, Memorial Sloan Kettering, Rockefeller), direct access to world-class clinical collaborators and data, strong mentorship, and the chance to publish in top venues and ship tools that reach the clinic — all in the heart of New York City.
Applications are reviewed on a rolling basis, and the position is open until filled, with priority given to applications received by July 31. Interested, or know someone who is? Email Dr. Hajirasouliha with your CV and a short note on what draws you to this work.
Beyond the call above, prospective postdoctoral applicants interested in computational genomics, metagenomics and the microbiome, or general AI in medicine are welcome to email Dr. Hajirasouliha directly with a CV and a brief note of interest.
MD and MD/PhD students, and Weill Cornell residents in their dedicated research years, are encouraged to reach out about research projects in the lab.
Research in the lab is supported by federal and foundation funding.
