Omar Lab Members

Mohamed Omar, M.D
Principal Investigator
Mohamed.Omar@csmc.edu

0000-0002-8068-1920
mohamed-omar-md
Cedars-Sinai Profile
Mohamed's Citations
@MohamedOmarMD
mohamedomarmd.bsky.social
TheOmarLab

Mohamed recieved his MD degree from the School of Medicine at Mansoura University, Egypt. He then conducted postdoctoral training in Computational Genomics at Johns Hopkins University School of Medicine and in Computational Pathology from Weill Medical College of Cornell University (Weill Cornell Medicine) in the Lab of Dr. Luigi Marchionni.

In 2022, Mohamed became Assistant Professor of Research in Pathology and Laboratory Medicine, where he worked on developing computational approaches for cancer phenotype prediction using histopathology and multi-omics data.

He moved to Los Angeles, California in November 2024 to start his independent research lab in the Department of Computational Biomedicine at Cedars-Sinai.

Priyanka Vasanthakumari, Ph.D.
Research Data Scientist
Priyanka.Vasanthakumari@cshs.org

priyanka-vasanthakumari-phd-68492789
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Priyanka received her Ph.D. in Biomedical Engineering from Texas A&M University in 2022. Her thesis focused on deep learning-based detection of oral and skin cancer using multi-spectral Autofluorescence Lifetime Imaging (maFLIM). Subsequently, she joined Argonne National Laboratory as Postdoctoral Fellow, where she focused on developing machine learning models for predicting anti-cancer drug responses.

In the Omar lab, Priyanka is working on developing multimodal risk assessment tools for patients with prostate cancer using multiparametric MRI imaging (mpMRI) and digitized histopathology slides of biopsy specimens.

Maryam Ranjpour, Ph.D.
Postdoctoral Scientist
Maryam.RanjpourAghmiouni@cshs.org

0000-0002-4828-9099
maryam-ranjpour-63884152
Maryam's Citations

Maryam received her Ph.D. in Medical Biotechnology in 2018 before starting her postdoctoral research in immunology and molecular oncology at UT Tyler and Cedars‑Sinai, respectively. With over 13 years of research experience, her work spans cancer targeted therapy, biomarker discovery, and translational studies in prostate and bladder cancer.

In the Omar Lab, Maryam applies her expertise in proteomics, molecular assays, and advanced 3D co‑culture models for prostate cancer bone metastases to study the molecular and spatial interactions within the microenvironment of both primary tumors and metastatic sites

Itzel Valencia
PhD Student
Itzel.Valencia@cshs.org

Itzel first worked as a Research Specialist in the Multi-parametric In Situ Imaging (MISI) lab, then as a Staff Scientist in the Division of Computational Pathology at Weill Medical College of Cornell University (Weill Cornell Medicine).

Currently, she is a PhD candidate in the PhD in Health Artificial Intelligence program at Cedars-Sinai Medical Center, where she develops robust computational methods and pipelines for disease phenotype prediction and patient prognostication using multiomics and histopathology imaging data.

Bryan Magana
U-GROW Summer Intern
Subsequently: Masters Student @ Johns Hopkins Bloomberg School of Public Health

Bryan received his B.S. in Health Science with minors in Chemistry and Biology from CSU Channel Islands. A first-generation college student, he gained clinical experience through the COPE Health Scholars Program and conducted oncology research at Cedars-Sinai Medical Center.

Bryan is now pursuing a Master of Health Science in Epidemiology at Johns Hopkins Bloomberg School of Public Health, with plans to earn an M.D./Ph.D. and build a career combining clinical oncology with cancer research.

Anh Le
she/her
U-GROW Summer Intern
Anh.Le2@cshs.org

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Anh received her B.S. in Computer Science from California State University, Long Beach, where she also conducted NSF-funded research on magnetic materials using Magneto-Optic Kerr Effect (MOKE) techniques.

In the Omar Lab, Anh is developing deep learning-based segmentation frameworks for automated prostate cancer grading from whole-slide images (WSIs), leveraging AI and advanced imaging analysis to improve tumor region identification and support precision oncology research.