headshot of Kayhan Batmanghelich

Kayhan Batmanghelich

Assistant Professor
kayhan@pitt.edu
(412) 648-9037
Electrical and Computer Engineering

overview

My research is at the intersection of medical vision (medical image analysis), machine learning, and bioinformatics. I develop algorithms to analyze and understand medical image along with genetic data and other electrical health records such as the clinical report. For example, we are developing a probabilistic model to extract information from brain images (Magnetic Resonance Images) of patients with Alzheimer's disease and relate them the underlying genetic markers involved in the disease. We are interested in method development as well as translational clinical problems because after all, exciting research directions are coming from real applications.

about

BSc Biomedical Engineering, Amirkabir University of Technology, 1998 - 2002

MSC Electrical and Computer Engineering, University of Tehran, 2002 - 2005

PhD Electrical and System Engineering, University of Pennsylvania, 2007 - 2012

Dalca, A.V., Batmanghelich, N.K., Sabuncu, M.R., & Shen, L. (2018). Introduction. xxi-xxx.Elsevier. doi: 10.1016/b978-0-12-813968-4.02001-0.

Huisman, S.M.H., Mahfouz, A., Batmanghelich, N.K., Lelieveldt, B.P.F., Reinders, M.J.T., & Alzheimer’s Disease Neuroimaging Initiative. (2018). A structural equation model for imaging genetics using spatial transcriptomics. Brain Inform, 5(2), 13.Springer Nature. doi: 10.1186/s40708-018-0091-0.

Batmanghelich, N.K., Saeedi, A., Estepar, R.S.J., Cho, M., & Wells, W.M. (2017). Inferring Disease Status by Non-parametric Probabilistic Embedding. Lecture Notes in Computer Science, 10081, 49-57.Springer Nature. doi: 10.1007/978-3-319-61188-4_5.

Dalca, A.V., Batmanghelich, N.K., Sabuncu, M.R., & Shen, L. (2017). Preface micgen 2017. 10551 LNCS.

Dalca, A.V., Batmanghelich, N.K., Sabuncu, M.R., & Shen, L. (2017). Imaging genetics. 1-169.

Schabdach, J., Wells, W.M., Cho, M., & Batmanghelich, K.N. (2017). A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies. Inf Process Med Imaging, 10265, 170-183.Springer Nature. doi: 10.1007/978-3-319-59050-9_14.

Batmanghelich, N.K., Dalca, A., Quon, G., Sabuncu, M., & Golland, P. (2016). Probabilistic Modeling of Imaging, Genetics and Diagnosis. IEEE Trans Med Imaging, 35(7), 1765-1779.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TMI.2016.2527784.

Batmanghelich, N.K., Saeedi, A., Cho, M., Estepar, R.S.J., & Golland, P. (2015). Generative Method to Discover Genetically Driven Image Biomarkers. Inf Process Med Imaging, 24, 30-42.Springer International Publishing. doi: 10.1007/978-3-319-19992-4_3.

Freifeld, O., Hauberg, S., & Fisher III, J. (2015). Highly Expressive Spaces of Well-Behaved Transformations: Keeping It SImple. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2911-2919.

Batmanghelich, N., Cho, M., Estepar, R., & Golland, P. (2014). Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies. Workshop on Bayesian and Graphical Models for Biomedical Imaging, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 107-117.

Batmanghelich, N.K., Quon, G., Kulesza, A., Kellis, M., Golland, P., & Bornn, L. (2014). Diversifying Sparsity Using Variational Determinantal Point Processes.

Batmanghelich, N.K., Taskar, B., & Davatzikos, C. (2012). Generative-discriminative basis learning for medical imaging. IEEE Trans Med Imaging, 31(1), 51-69.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TMI.2011.2162961.

Bloy, L., Ingalhalikar, M., Batmanghelich, N.K., Schultz, R.T., Roberts, T.P.L., & Verma, R. (2012). An integrated framework for high angular resolution diffusion imaging-based investigation of structural connectivity. Brain Connect, 2(2), 69-79.Mary Ann Liebert. doi: 10.1089/brain.2011.0070.

Batmanghelich, N., Ye, D., Taskar, B., & Davatzikos, C. (2011). Disease Classification and Prediction via semi-supervised Dimensionality Reduction. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1086-1090.

Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., & Trojanowski, J.Q. (2011). Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of aging, 32, 2322-e19.Elsevier.

Fan, Y., Batmanghelich, N., Clark, C.M., Davatzikos, C., & Alzheimer's Disease Neuroimaging Initiative. (2008). Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage, 39(4), 1731-1743.Elsevier. doi: 10.1016/j.neuroimage.2007.10.031.

Ghanbari, Y., Bloy, L., & Verma, R. Dominant Component Analysis of Electrophysiological Connectivity Network. International Conference on Medical Image Computing and Computer Assisted Intervention.

Batmanghelich, K., Saeedi, A., Narasimhan, K., & Gershman, S. (2016). Nonparametric Spherical Topic Modeling with Word Embeddings. In Proc Conf Assoc Comput Linguist Meet, 2016(2016), (pp. 537-542).Association for Computational Linguistics (ACL).United States. doi: 10.18653/v1/P16-2087.

Binder, P., Batmanghelich, N.K., Estepar, R.S.J., & Golland, P. (2016). Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort. In Mach Learn Med Imaging, 10019, (pp. 180-187).Springer Nature.Germany. doi: 10.1007/978-3-319-47157-0_22.

Batmanghelich, N.K., Saeedi, A., Cho, M., Estepar, R.S.J., & Golland, P. (2015). Generative Method to Discover Genetically Driven Image Biomarkers. In Lecture Notes in Computer Science, 24, (pp. 30-42).Springer Nature. doi: 10.1007/978-3-319-19992-4_3.

Batmanghelich, N.K., Dalca, A.V., Sabuncu, M.R., Polina, G., & ADNI. (2013). Joint modeling of imaging and genetics. In Inf Process Med Imaging, 23, (pp. 766-777).Springer Nature.Germany. doi: 10.1007/978-3-642-38868-2_64.

Batmanghelich, N., Dong, A., Taskar, B., & Davatzikos, C. (2011). Regularized tensor factorization for multi-modality medical image classification. In Med Image Comput Comput Assist Interv, 14(Pt 3), (pp. 17-24).Springer Nature.Germany. doi: 10.1007/978-3-642-23626-6_3.

Batmanghelich, N., Gooya, A., Kanterakis, S., Taskar, B., & Davatzikos, C. (2010). Application of Trace-Norm and Low-Rank Matrix Decomposition for Computational Anatomy. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, (pp. 146-153).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/cvprw.2010.5543596.

Batmanghelich, N., Taskar, B., & Davatzikos, C. (2009). A general and unifying framework for feature construction, in image-based pattern classification. In Inf Process Med Imaging, 21, (pp. 423-434).Springer Nature.Germany. doi: 10.1007/978-3-642-02498-6_35.

Batmanghelich, N., & Verma, R. (2008). On non-linear characterization of tissue abnormality by constructing disease manifolds. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (pp. 1-8).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/cvprw.2008.4563027.

Shariatpanahi, H.F., Batmanghelich, N., Kermani, A.R.M., Ahmadabadi, M.N., & Soltanian-Zadeh, H. (2006). Distributed Behavior-Based Multi-Agent System for Automatic Segmentation of Brain MR Images. In The 2006 IEEE International Joint Conference on Neural Network Proceedings, (pp. 4535-4542).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/ijcnn.2006.247079.

Batmanghelich, N., Soltanian-Zadeh, H., & Araabi, B.N. (2005). Knowledge-based segmentation: Using simultaneous shape priori and histogram information to segment brain structures. In Proceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005, (pp. 414-419).

Karimi, M.M., Batmanghelich, N., Soltanian-Zadeh, H., & Lucas, C. (2004). A 3-D deformable surface method for automatic hippocampus-amygdala complex segmentation. In IEEE Nuclear Science Symposium Conference Record, 6, (pp. 3725-3729).

Karimi, M.M., Batmanghelich, N., Soltanian-Zadeh, H., & Lucas, C. (2004). Improvement of simplex meshes model for 3D hippocampus segmentation. In Proceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing, (pp. 631-635).