About Me

I am Chenyu Wang, a fourth-year PhD Candidate in ECE at Boston University, co-advised by Prof. Wenchao Li and Prof. Kayhan Batmanghelich. My research is at the intersection of multimodal LLMs and healthcare. I've been working on uncertainty quantification, hallucination detection, and more recently agentic verification with tool use.

I study these problems in radiology report generation from 2D X-ray and 3D CT — a domain that demands both fine-grained visual grounding and factual correctness.

I'm always open to collaborations and discussions. Feel free to send me an email if you are interested!

News

• Oct 2026 I will be joining Amazon as an Applied Scientist Intern in Santa Clara, CA.
• Jun 2026 One collaborative paper has been accepted to ECCV 2026.
• Apr 2026 One paper has been accepted to the CVPR 2026 TRUE-V Workshop.
• Apr 2026 One paper in collaboration with Siemens Healthineers is now available as a preprint.
• Feb 2026 One collaborative paper has been accepted by Nature Biomedical Engineering.
• Jul 2025 One collaborative paper has been accepted to Findings of ACL 2025.
• Apr 2025 One paper has been accepted to Findings of NAACL 2025.
• Sep 2023 Joined Boston University as a PhD student in ECE.

Publications

Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance

Chenyu Wang, Weicheng Dai, Han Liu, Wenchao Li, and Kayhan Batmanghelich
Preprint, 2026 | PDF Code

VLM-UQBench: A Benchmark for Modality-Specific and Cross-Modality Uncertainties in Vision Language Models

Chenyu Wang*, Tianle Chen*, HM Ahmad, Kayhan Batmanghelich, Wenchao Li
CVPR 2026 TRUE-V Workshop | PDF Code

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
Findings of ACL, 2025 | PDF Code

Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation

Chenyu Wang, Weichao Zhou, Shantanu Ghosh, Kayhan Batmanghelich, Wenchao Li
Findings of NAACL, 2025 | PDF Code

Deep Learning-Based Joint Channel Coding and Frequency Modulation for Low Power Connectivity

Boxuan Chang, Chenyu Wang, Hun-Seok Kim
ICC, 2023 | PDF

Misc