Kayhan Batmanghelich headshot

Kayhan Batmanghelich

Assistant Professor

Department of Electrical and Computer Engineering

Boston University

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

batman@bu.edu

Phone: 617-358-0538

Brief Bio

I am an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University. My research focuses on medical image analysis and the broader application of artificial intelligence in healthcare. Previously, I was a faculty member in the Department of Biomedical Informatics at the University of Pittsburgh. I received my PhD from the University of Pennsylvania. I am a recipient of the NSF CAREER Award and a Google Academic Research Award.

Research

My lab studies the fundamental challenges of AI in healthcare: (1) explainability, (2) data efficiency, (3) multimodal data fusion, and (4) causality. We develop machine learning methods for complex clinical problems, with applications in Breast Cancer, Chronic Obstructive Pulmonary Disease (COPD), and Alzheimer's disease. Our research is supported by funding from the NIH, NSF, and industry partners. I am also a co-founder of MLxMed, a multi-campus online seminar series focused on machine learning methods for healthcare, and READE.ai, a startup developing real-time evaluation of adverse events during surgery.

Students and Postdocs

Batman Lab Alumni

Yanwu Xu (PhD, Pitt-ISP)
Li Sun (PhD, Pitt-ISP)
Max Raynold (PhD, Pitt-DBMI)
Junxiang Chen (Postdoc)
Mingming Gong (Postdoc)
Rohit Kumar Jena (MS, CMU-RI)
Sead Nikšić (Undergrad, Pitt-ECE)
Brian Pollack (Postdoc)
Sumedha Singla (PhD, Pitt-CS)
Payman Yadollahpour (Postdoc)
Ke Yu (PhD, Pitt-ISP)

News

2025

  • [Jul 2025]I am honored to have received the NSF CAREER award!
  • [May 2025]We are excited that NIH awarded us $3.1M to continue developing AI technology to study lung COPD.
  • [May 2025]Congratulations to Shantanu! His paper, Ladder, was accepted at ACL 2025.
  • [Jan 2025]Congratulations to Chenyu and Wenchao! Our paper has been accepted to NAACL!

Publications

2025

LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers thumbnail
LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers
Shantanu Ghosh, Rayan Syed, Chenyu Wang, Vaibhav Choudhary, Binxu Li, Clare B. Poynton, Shyam Visweswaran, Kayhan Batmanghelich
Venue: Findings of the Association for Computational Linguistics: ACL 2025
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation thumbnail
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation
Chenyu Wang, Weichao Zhou, Shantanu Ghosh, Kayhan Batmanghelich, Wenchao Li
Venue: Findings of NAACL, 2025
A Human-Centered Approach to Identifying Promises, Risks, \& Challenges of Text-to-Image Generative AI in Radiology thumbnail
A Human-Centered Approach to Identifying Promises, Risks, \& Challenges of Text-to-Image Generative AI in Radiology
Katelyn Morrison, Arpit Mathur, Aidan Bradshaw, Tom Wartmann, Steven Lundi, Afrooz Zandifar, Weichang Dai, Kayhan Batmanghelich, Motahhare Eslami, Adam Perer
Venue: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application thumbnail
High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application
Hung-Ching Chang, Yusi Fang, Michael T Gorczyca, Kayhan Batmanghelich, George C Tseng
Venue: Bioinformatics
Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis thumbnail
Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis
Atta Taseh, Souri Sasanfar, Michelle Chan, Evan Sirls, Ara Nazarian, Kayhan Batmanghelich, Jonathan F Bean, Soheil Ashkani-Esfahani
Venue: JMIR Medical Informatics
Multi-Modal Large Language Models are Effective Vision Learners thumbnail
Multi-Modal Large Language Models are Effective Vision Learners
Li Sun, Chaitanya Ahuja, Peng Chen, Matt D'Zmura, Kayhan Batmanghelich, Philip Bontrager
Venue: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

2024

MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images thumbnail
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
Yanwu Xu, Li Sun, Wei Peng, Shuyue Jia, Katelyn Morrison, Adam Perer, Afrooz Zandifar, Shyam Visweswaran, Motahhare Eslami, Kayhan Batmanghelich
Venue: IEEE Transactions on Medical Imaging
Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography thumbnail
Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography
Shantanu Ghosh, Clare B Poynton, Shyam Visweswaran, Kayhan Batmanghelich
Venue: International conference on medical image computing and computer-assisted intervention
Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning thumbnail
Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning
Ke Yu, Shantanu Ghosh, Zhexiong Liu, Christopher Deible, Clare B Poynton, Kayhan Batmanghelich
Venue: Radiology: Artificial Intelligence

Positions Available

Please check back for available positions.

Teaching

Deep Learning (EC 523) - Fall 2023
Instructor
Boston University
Medical Imaging With AI (EC 500) - Spring 2024
Instructor
Boston University
Deep Learning (EC 523) - Fall 2024
Instructor
Boston University
Machine Learning (EC 414) - Spring 2025
Instructor
Boston University
Medical Imaging With AI (EC 500) - Fall 2025
Instructor
Boston University