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Ph.D. in Genomics & Computational Biology

Pediatric acute myeloid leukemia (AML) affects about 25% of all children with leukemia. Half relapse even after extensive treatment and chemotherapy, which is damaging for young developing bodies. Genomic screening has opened the door for personalized approaches to targeted cancer immunotherapy. However, current treatment strategies for pediatric AML are based on genomic and clinical disease presentations in adult patients.


Given the importance of the spliceosome complex for cancer cell survival, pharmacologic spliceosome modulators have tremendous potential for development in cancer, especially AML. Further, factors such as treatment history and population-specific variants, along with other environmental factors, might alter transcriptomic markers linked to variable prognosis. To this end, I focus on the development of reproducible and scalable computational tools to interrogate the context-specific disease presentation of pediatric AML. I strive to leverage existing and novel splicing paradigms with integrated multi-comic analysis for the identification of targetable therapies on a personalized scale.

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Featured Projects

  • Transcriptome-aware ancestry inference from RNA-seq data

  • Unraveling context-specific genomic & transcriptomic landscapes in pediatric AML 

  • Adaptation and development of scalable cloud computing environments for large-scale alternative splicing analysis

M.A. in Statistics & Data Science

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