Computational Genomics

Our research interest is the development of computational genomic tools for the study of human diseases and cellular development. We focus on detailed analyses of genomic and epigenomic data generated by high-throughput sequencing to address specific questions related to disease progression, treatment response, stem cell differentiation and neurological processes.

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News

  • [December 2024] Two new preprints are out from our lab. The first is a deep-learning model for cross species scRNA-seq integration termed scVital from our student Jonathan Rub and close collaboration with Tuomas Tammela. The second is identification of plasma proteins biomarkers of Parkinson's disease using large public biomedical databases led by Fayzan Chaudhry

  • June 2024] New Cell publication in collaboration with Lorenz Studer describing how the TNF-NF-κB-p53 pathways control in-vivo survival of engrafted dopamine neurons.

  • [October 2023] In our recent collaboration with the Studer lab we developed a cellular aging score that was used to identify new approaches for in vitro age modulation. Part of our ongoing collaboration with Lorenz Studer and his group developing new approaches of stem cell models of neurodegenerative diseases.

    • Full list of publications

Open Positions

We are looking for students in the area of computational genomics. Trainees will be working on:

  • Developing new machine learning algorithms for the analysis of single cell data and spatial genomics.
  • Participating and leading multi-desciplinary projects with collaborators in immunology (autoimmune, tumor immunology), stem cell and cancer genetics.

Interested graduate students should apply through Weill Cornell graduate school to the CBM or PBSB programs.