Nabavi, Sheida

Sheida Nabavi

Associate Professor, School of Computing

Email sheida.nabavi@uconn.edu
Phone (860) 486-0756
Mailing Address University of Connecticut 371 Fairfield Way, Unit 4155 Storrs, CT 06269-4155
Campus Storrs
Link Affiliation Website
Google Scholar Link

Brief Bio

I am an Associate Professor in department of Computer Science and Engineering at the University of Connecticut (UConn), joining Fall 2015. Before joining UConn, I was  a Research Associate in the Center for Biomedical Informatics (CBMI) at Harvard Medical School (HMS). I received my PhD from Electrical and Computer Engineering Department at Carnegie Mellon University (CMU) in December 2008 and my Master’s in Medical Science focused on Bioinformatics from HMS in May 2012.

My research interest is on developing novel computational methods for analyzing genomic data  and biomedical images by employing advanced statistical machine learning and signal/image processing techniques.

  • Integrating genomic data to identifying candidate biomarkers and building phenotypic predictive models for cancer studies.
  • Developing genomic aberration detection methods using DNA single cell  sequencing data.
  • Developing computational methods for analyzing single-cell sequencing data.
  • Detection of breast cancer using digital mammograms.
  • Weakly-Supervised Deep Learning Model for Analyzing Histopathology Images.
  • Analyzing mouse brain multiplex microscopic images: Registration, Segmentation, 3D reconstruction
  • CSE 4830   Computer Vision and Machine Learning for Image Analysis
  • CSE 4059   Machine Learning for Analyzing Biomedical Images
  • CSE 5095    High-Throughput Genomics Data Analysis
  • CSE 5830    Probabilistic Graphical Models
  • CSE 3666    Introduction to Computer Architecture
  • CSE 2301    Digital Logic Design
  • A. Jeny, S. Hamzehei, A. Jin., S.A. Baker, T. Van Rathe, J. Bai, C. Yang, S. Nabavi, “Hybrid transformer‐based model for mammogram classification by integrating prior and current images,” Medical Physics, 2025 (https://doi.org/10.1002/mp.17650).
  • Hamzehei, J. Bai, G. Raimondi, R. Tripp, L. Ostroff, S. Nabavi, “Advanced Feature Extraction and Outlier Detection for 3D Biological/Biomedical Image Registration,” IEEE Transactions on Computational Biology and Bioinformatics, 2025 (DOI: 10.1109/TCBBIO.2024.3517596).
  • Weiner, B. Li, S. Nabavi, “Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing,” Bioinformatics, 40 (8), btae506, 2024 (https://doi.org/10.1093/bioinformatics/btae506).
  • Bai, A. Jin, M. Adams, C. Yang, S. Nabavi, “Unsupervised feature correlation model to predict breast abnormal variation maps in longitudinal mammograms,” Computerized Medical Imaging and Graphics, 113, 102341, 2024 (https://doi.org/10.1016/j.compmedimag.2024.102341).
  • Li, S. Nabavi, “A Multimodal Graph Neural Network Framework for Cancer Molecular Subtype Classification,” BMC Bioinformatics 25 (1), 27, 2024 (https://doi.org/10.1186/s12859-023-05622-4).
  • M. Behzadi, M. Madani, H. Wang, J. Bai, A. Bhardwaj, A. Tarakanova, H. Yamase, G.H. Nam, S. Nabavi, “Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images,” Biomedical Signal Processing and Control, 95, 106351, 2024 (https://doi.org/10.1016/j.bspc.2024.106351).
  • Madani, M. M. Behzadi, S Nabavi, “The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review,” Cancers, 14 (21), 5334, 2022 (https://doi.org/10.3390/cancers14215334).
  • J. Bai, A. Jin, T. Wang, C. Yang, S. Nabavi, “Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms,” Medical Physics 49 (6), 3654-3669, 2022 (https://doi.org/10.1002/mp.15598).
  • T. Wang, Jun Bai, S. Nabavi, ” Single-cell Classification Using Graph Convolutional Networks,”  BMC Bioinformatics, 22, 364, 2021 (https://doi.org/10.1186/s12859-021-04278-2)..
  • J. Bai, R. Posner, T. Wang, C. Yang, S. Nabavi, “Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review,”   Medical Image Analysis,  Vol 71, 102049, 2021.
  • D. Abdelhafiz, J. Bi, R. Ammar; C. Yang, S. Nabavi, “Convolutional neural network for automated mass segmentation in mammography,” BMC Bioinformatics, 21, 192, 2020.
  • E. Przybytkowski, T. Davis, A. Hosny, J. Eissmann, U. A. Matulonis, G. M. Wulf, and S. Nabavi “An immune-centric exploration of BRCA1 and BRCA2 germline mutation related breast and ovarian cancers,” BMC Cancer, 20(1) pp 1-16, 2020.
  • Eismann, Y. J. Heng, J. Waldschmidt, I. S. Vlachos, K. Gray, U. A. Matulonis, P. A. Konstantinopoulos, C. J. Murphy, S. Nabavi, G. M. Wulf “Transcriptome analysis reveals overlap in fusion genes in a phase I clinical cohort of TNBC and HGSOC patients treated with buparlisib and olaparib,” Journal of Cancer Research and Clinical Oncology, 146(2), pp 503-514, 2020.
  • I. Sirois, et al., “A Unique Morphological Phenotype in Chemoresistant Triple-Negative Breast Cancer Reveals Metabolic Reprogramming and PLIN4 Expression as a Molecular Vulnerability,” Molecular Cancer Research, 17 (12), pp 2492-2507, 2019.
  • D. Abdelhafiz, S. Nabavi, R. Ammar, C. Yang,” Deep Convolutional Neural Networks for Mammography: Advances, Challenges and Applications,” BMC Bioinformatics, 20 (Suppl 11):281, 2019 .
  • T. Wang, B. Li, C. E. Nelson, S. Nabavi, “Comparative Analysis of Differential Gene Expression Analysis Tools for Single-Cell RNA Sequencing Data,” BMC Bioinformatics, 20 (1), 40, 2019.
  • F. Zare, S. Ansar, K. Najarian, S. Nabavi, “Preprocessing Read Count Data for Precise Detection of Copy Number Variations,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, (Early Online Access).
  • F. Zare, A Hosny, and S. Nabavi, “Noise Cancellation Using Total Variation for Copy Number Variation Detection,” BMC Bioinformatics, 19 (Suppl 11), 361, 2018.
  • T. Wang, S. Nabavi, “SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data,” Methods, Volume 145, pp 25-32, 2018.
  • N. Krieger, S Nabavi, P.D. Waterman, N.S. Achacoso, L. Acton, S.J. Schnitt, L.A. Habel, “Feasibility of analyzing DNA copy number variation in breast cancer tumor specimens from 1950 to 2010: how old is too old?” Cancer Causes & Control 29 (3), 305-314, 2018.
  • F. Zare, M. Dow, N. Monteleone, A Hosny, and S. Nabavi, “An evaluation of copy number variation detection tools for cancer using whole exome sequencing data,” BMC Bioinformatics, 18 (1), 286, 2017.
  • M. O. Taveira, S. Nabavi, Y. Wang, P. Tonellato, F. Esteva, L. C. Cantley, G. M. Wulf, 2017, ”Genomic characteristics of trastuzumab-resistant Her2-positive metastatic breast cancer,” Journal of Cancer Research and Clinical Oncology 143 (7), 1255-1262, 2017.
  • S. Nabavi, “Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data,” BMC Genomics, vol. 17, no. 1, p. 638, 2016.
  • H. Hu, M. Luo, C. Desmedt, S. Nabavi, S. Yadegarynia,A. Hong, P. A. Konstantinopoulos, E. Gabrielson, R. Hines-Boykin, C. Sotirious, D. P. Dittmer, J. D. Fingeroth and G. M. Wulf, “Epstein Barr Virus infection of mammary epithelial cells promotes malignant transformation,” EBioMedicine, 2016.
  • S. Nabavi,D. Schmolze, M. Maitituoheti, and A. H. Beck, “EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes,” Bioinformatics 2016, 32:533–541.
  • E. Przybytkowski, E. Lenkiewicz, M.T. Barrett, K. Klein, S. Nabavi, C.M. Greenwood, and M. Basik, “Chromosome-breakage genomic instability and chromothripsis in breast cancer,” BMC Genomics, 15(1): 579, 2014.
  • E. Przybytkowski, A. Aguilar-Mahecha, S. Nabavi, P.J.Tonellato, and M. Basik, “Ultradense Array CGH and Discovery of Micro-Copy Number Alterations and Gene Fusions in the Cancer Genome,” Methods in Molecular Biology, 973, Pages: 15-38, 2013.
  • Y. Ng, B. V. K. Vijaya Kumar, K. Cai, S. Nabavi, and T. C. Chong, “Picket-Shift Codes for Bit-Patterned Media Recording with Insertion/Deletion Errors,” IEEE Transactions on Magnetics, Volume 46, Issue 6, Pages: 2268-2271, 2010.
  • H. Suzuki, W. C. Messner, J. A. Bain, V. Bhagavatula, and S. Nabavi, “Simultaneous PES Generation, Timing Recovery, and Multi-track Read on Patterned Media: Concept and Performance,” IEEE Transactions on Magnetics, Volume 46, Issue 3, Pages: 825-829, 2010.
  • S. Nabavi, and B. V. K. Vijaya Kumar, “ An Analytical Approach for Performance Evaluation of Bit-Patterned Media Channels,” IEEE Journal on Selected Areas in Communications, Volume 28, Issue 2, Pages: 135-142, 2010.
  • S. Nabavi, B. V. K. Vijaya Kumar, J. A. Bain, C. Hogg, and S. Majetich, “Application of Image Processing to Characterize Media Noise in Bit-Patterned Media,” IEEE Transactions on Magnetics, Volume 45, Issue 10, Pages: 3523-3526, 2009.
  • H. Suzuki, W. C. Messner, J. A. Bain, V. Bhagavatula, and S. Nabavi, “A Method for Simultaneous Position and Timing Error Detection for Bit Patterned Media,” IEEE Transactions on Magnetics, Volume 45, Issue 10, Pages: 3749-3752, 2009.
  • S. Nabavi, B. V. K. Vijaya Kumar, and J. A. Bain, “Two-Dimensional Pulse Response and Media Noise Modeling for Bit-Patterned Media,” IEEE Transactions on Magnetics, Volume 44, Issue 11, Pages: 3789-3792, 2008.
  • S. Nabavi, and B. V. K. Vijaya Kumar, “Modifying Viterbi Algorithm to Mitigate Inter-track Interference for Bit-Patterned Media,” IEEE Transactions on Magnetics, Volume 43, Issue 6, Pages: 2274-2276, 2007.
  • S. Nabavi, and B. V. K. Vijaya Kumar, “Application of Linear and Nonlinear Equalization Methods for Holographic Data Storage,” Japanese Journal of Applied Physics, Volume 45, Issue 2B, Pages: 1079-1083, 2006.