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

Self-Supervised Learning paper is in MedIA!

Dec 05, 2023

Congrats to Ke and Li for their recent paper in MedIA! This is a way to go for Self-Supervised Learning in medical imaging .

Our fast diffusion paper is accepted in NeurIPs!

Sep 27, 2023

Congrats to Yanwu for his second NeurIPs paper!   We are going to have more development in this direction soon!

Our paper about shortcut learning is accepted to TMLR!

Sep 14, 2023

Congratulations to Niahl for his first journal paper in TMLR! The pre-print is available here and the final camera-ready, code and video will be out soon!

The harmonization paper is accepted to NeuroImage Clinical!

Jul 06, 2023

Congratulation to Max Reynolds for his first journal in NeuroImage Clinical Journal! The paper revisits the COMBAT method from a fully Bayesian point of view.

Two early acceptances in MICCAI 2023!

Jun 23, 2023

Congratulations to Shantanu and Matthew for their first papers in MICCAI, both early accept! I am very proud of them. Links to the paper and code are coming out soon.

Imaging-Transcriptomics paper is accept to the COPD journal!

Jun 20, 2023

We use DL techniques to define new COPD axes using CT imaging and gene expression data. Congratulation to Junxiang for his paper in the COPD Journal! The pre-print is here!

One paper is accepted to ICML 23!

Apr 24, 2023

Congratulations to Shantanu and Ke! Their first paper is accepted to ICML 2023. Find the paper, code, and more on the project page.

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

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