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Kourosh Zarringhalam

Kourosh Zarringhalam

Position: Principle Investigator,
Professor, Department of Mathematics,
Center for Personalized Cancer Therapy (CPCT),
Computational Sciences Graduate Program (CSci), Director

Education:

  • Ph.D. in Mathematics, University of New Hampshire 2006

Research Interests:

  • Computational Biology
  • Computational Parasitology
  • Computational Cancer Research
  • Applied Mathematics
  • Machine Learning

Email: [email protected]
Office:

  • Department of Mathematics, Wheathley, 3rd floor
  • Center for Personalized Cancer Therapy (CPCT), ISC, 4th floor Room 4740

Phone: 617-287-7486

Links: Personal Website | Google Scholar |

Biography

Dr. Kourosh Zarringhalam is a Professor in the Department of Mathematics and a member of the Center for Personalized Cancer Therapy (CPCT) at the University of Massachusetts Boston. He leads the Mathematical Biology Lab (MathBioLab).

Research Focus

The MathBioLab develops innovative computational approaches to solve complex biological problems. Current research areas include:

  • Computational approaches for parasitology research
  • Deep learning applications in cancer imaging and diagnostics
  • Transcriptional gene regulation
  • Single cell analysis tools
  • Mathematical modeling of biological systems
  • Network-based analysis of biological data

Selected Publications

  1. The essential genome of Plasmodium knowlesi reveals determinants of antimalarial susceptibility (2025), Science
  2. ingle cell expression and chromatin access of the Toxoplasma gondii lytic cycle identifies AP2XII-8 as an essential pivotal controller of a ribosome regulon (2024), Nature Communications
  3. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images (2020), Nature Communications
  4. Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer (2021), Modern Pathology
  5. Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators (2019), Nucleic Acids Research
  6. A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data (2023), NAR Genomics & Bioinformatics