My research focuses on the development of machine learning techniques and AI-based models for the innovation of medical data analysis. I am an Assistant Professor at New York City College of Technology (City Tech) and teach Biomedical Data Analytics. Also, I serve as Deputy Editor of the Journal of Magnetic Resonance Imaging (JMRI).
Ph.D., Bioinformatics, 2014
Assistant Professor (2022-present)
Department of Biological Sciences, New York City College of Technology, CUNY, NY, USA.
Deputy Editor (2020-present)
Journal of Magnetic Resonance Imaging (JMRI): New Developments and Future Direction, NY, USA.
Sr II Computational Biologist (2020-2022)
Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NY, USA.
Postdoctoral Associate (2017-2020)
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medical College, NY, USA.
Postdoctoral Research Fellow (2014-2017)
School of Biological Sciences of Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Visiting Researcher (2012-2013)
Donnelly Center for Cellular and Biomolecular Research, Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
Khosravi P., Lysandrou, M., Eljalby, M., Brendel, M., Li, Q., Kazemi, E., Zisimopoulos, P., Sigaras, A., Barnes, J., Ricketts, C., Meleshko, D., Yat, A., McClure, T. D., Robinson, B. D., Sboner, A., Elemento, O., Chughtai, B., Hajirasouliha, I., A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion, Journal of Magnetic Resonance Imaging, 54 (2021).
Boehm K. M., Khosravi P., Vanguri R., Gao J., Shah P. S., Harnessing multimodal data integration to advance precision oncology, Nature Reviews Cancer (2021), 22: 114-126.
Xu, Z., Verma, A., Naveed, U., Bakhoum, S., Khosravi P., Elemento, O., Using Histopathology Images to Predict Chromosomal Instability in Breast Cancer: A Deep Learning Approach, Iscience (2021), 3;24(5).
Asgari, Y., Khosravi P., Flux variability analysis reveals a tragedy of commons in cancer cells, SN Applied Sciences (2020), 2:1966.
Khosravi, P., Kazemi, Zhan, Q., Toschi, M., Malmsten, J., Cooper, L., Hickman, C., Meseguer, M., Rosenwaks, Z., Elemento, O., Hajirasouliha I., Deep Learning Enables Robust Assessment and Selection of Human Blastocysts after In-vitro Fertilization, npj digital medicine-Nature (2019), 4;2:21.
Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., Hajirasouliha I., Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images, EBioMedicine (2018), 27: 317-328.
Habibi, M., Khosravi, P., Disruption of the Protein Complexes from Weighted Complex Networks, IEEE/ACM transactions on computational biology and bioinformatics (2018).
Asgari, Y., Khosravi, P., Zabihinpour, Z., Habibi, M., Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis, Integrative Biology (2018), 10:113-120.
Aghdam, R., Baghfalaki, T., Khosravi, P., Ansari, E. S., The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer, Genomics, Proteomics & Bioinformatics (2017), 15: 396-404.
Emamjomeh A., Robat E. S., Zahiri J., Solouki M., Khosravi P., Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data, Plant Biotechnology Reports (2017), 1:6.
Aghdam, R., Khosravi, P., Ansari, E. S., Comparative Analysis of Gene Regulatory Networks Concepts in Normal and Cancer Groups, Bioinformatics and Biocomputational Research (2016), 1: 42-45.
Khosravi P., Gazestani V.H., Pirhaji L., Law B., Sadeghi M., Bader G., Goliaei B., Inferring interaction type in gene regulatory networks using co-expression data, Algorithm for molecular Biology (2015), 10:23.
Khosravi P., Gazestani V.H., Asgari Y., Law B., Sadeghi M., Goliaei B., Network-based approach reveals Y chromosome influences prostate cancer susceptibility, Computers in Biology and Medicine (2014), 54:24-31.
Hosseinpour B., Bakhtiarizadeh M.R., Khosravi P., Ebrahimie E., Predicting distinct organization of transcription factor binding sites on the promoter regions; a new genome-based approach to expand human embryonic stem cell regulatory network. Gene (2013), 531:212-9.
https://scholar.google.com/citations?user=lHM6ZCwAAAAJ&hl=en&oi=ao
The Society for Brain Mapping and Therapeutics (SBMT) was founded in 2004 to break boundaries in healthcare. The society promotes policies that support rapid, safe, and cost-effective translation of new technology into medicine.
