Kayhan Batmanghelich

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Kayhan Batmanghelich

Assistant Professor
Department of Electrical and Computer Engineering
Boston University

Phone: 617-358-0538
My CV (please contact me)


8 St Mary’s St, Office 421
Boston, MA 02215

I am an Assistant Professor at the Department of Electrical and Computer Engineering at Boston University. My research is at the intersection of medical vision (medical image analysis), machine learning, and bioinformatics. I develop algorithms to analyze and understand medical images, genetic data, and other electrical health records, such as clinical reports. The main themes of research in my lab are about the main challenges of AI in healthcare: (1) Explainability, (2) Data Efficiency, (3) Multimodal Data Fusion, and Causality. My lab works on Alzheimer’s Disease, Chronic Obstructive Pulmonary Disease (COPD), and Non-Alcoholic Fatty Liver Disease (NAFLD) projects. Our research is supported by funding from NIH, NSF, and industry awards.  Read More

Recent News

One paper is accepted to ICML 23!

Apr 24, 2023

Congratulations to Shantanu and Ke! Their first paper is accepted to ICML 2023. This is the preprint, the final version, and the code will be released soon!

One paper is accepted to WACV!

Oct 11, 2022

Congrats to Sumedha and Nihal for their paper in WACV! They showed how our previous work on Counterfactual Explainer could be used to fix an overconfident BlackBox classifier!

Yingci’s manuscript is accepted to the oral oncology!

Sep 07, 2022

Happy for Yingci! Her manuscript has been accepted to Oral Oncology!

Counterfactual blackbox explanation paper is accepted to MedIA!

Sep 02, 2022

Congratulation to Sumedha and her team! Her manuscript on counterfactual model explanation paper is finally accepted for a special issue in MedIA about XAI! The very initial pre-print is here; the full version is coming out soon! I am thankful to Motahare for her amazing contribution to the paper.

An efficient 3D GANs is finally out!

Apr 26, 2022

Congratulations to Li and his team for their big paper in IEEE JBHI that makes volumetric GANs possible for high-resolution medical images. They made a huge effort, and I am proud of them!

Yanwu’s Paper about Adversarial Spatial Perturbation is accepted to CVPR!

Mar 02, 2022

Congratulations to Yanwu for his CVPR paper! His method uses the Maximal Spatial Perturbation idea that significantly enhances image-to-image translation!

Selected Publications

Probabilistic Modeling of Imaging, Genetics and the Diagnosis

K.N. Batmanghelich, A. Dalca, G. Quon, M. Sabuncu, P. Golland
IEEE Trans Med Imaging

Explanation by Progressive Exaggeration

S. Singla, B. Pollack, J. Chen, K. Batmanghelich

Eighth International Conference on Learning Representations (ICLR)


Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector

S. Singla, M. Gong, S. Ravanbakhsh, F. Sciurba, B. Poczos, K.  Batmanghelich
Medical Image Computing & Computer-Assisted Intervention (MICCAI)

Twin Auxiliary Classifiers GAN

M.*, Y. Xu*, Ch. Li, Kun Zhang, K. Batmanghelich (*: equal contribution)
NeurIPS 2019 [Spotlight 2.4%]

Lab Members

Junxiang Chen

Post Doctoral Researcher

Ke Yu

PhD Student (Pitt-ISP)
Research Assistant

Li Sun

PhD Student (Pitt-ISP)
Research Assistant

Yanwu Xu

PhD Student (Pitt-ISP)
Research Assistant

Rick Chang

PhD Student (Pitt-Biostat)
Research Assistant

Shantanu Ghosh

PhD Student (Pitt-ISP)
Research Assistant

Nihal Murali

PhD Student (Pitt-ISP)
Research Assistant

Matthew Ragoza

PhD Student (Pitt-ISP)
Research Assistant

Maxwell Reynolds

PhD Student (Pitt-DBMI)
Research Assistant


Jiaming Bai

Lisa Hou

Fan Qian

Keyi Yu