Razi Mahmood

About Me

I am a graduate student at Rensselaer Polytechnic Institute studying multimodal machine learning for medical imaging and beyond in the BME Department. Prior to this, I was an undergraduate at UC Berkeley Data Science majoring in data science with a domain emphasis on cognition.

I am a team-oriented research-focused graduate student with data science internship experience to solve real-world problems ranging from IT Ops, Neurotech, e-Commerce to Healthcare. I have used statistical and deep-learning-based AI models in NLP, computer vision, and medical imaging. I am interested in basic exploratory data analysis, particularly to answer research questions. I am also interested in interfacing with domain experts to learn new topics, analyze their data and communicate the findings to the corresponding business stakeholders.

My M.S. Thesis carves out a new space for inference-time fact-checking of generative AI in chest radiology. I built a new fact-checking model as a supervised contrastive multi-label regression network trained on a large synthetically created dataset of real and fake pairings of chest radiology imaging with report findings. The 24 million size dataset called RadCheck has been contributed to open source on HuggingFace and is the largest to date for chest X-rays. This model is agnostic to report generators, and can auto-correct radiology reports from frozen report generator models. This has been recently accepted as Master's thesis at RPI and has spawned 4 publications appearing in MICCAI, ISBI, NeurIPS, etc. Please see my CV for details.

Education/Courses Taken

• M.S. Biomedical Engineering, Dept. of Biomedical Engineering, Rensselaer Polytechnic Institute (RPI), August 2025.

Published M.S. Thesis on Image-Driven Fact-Checking of AI Generated Chest Radiology Reports.
This thesis showed that a fact-checking discriminative neural model could be developed for use at inference time to detect and correct errors in generative AI radiology reports independent of the report generators. Led to 15-40% improvement in the quality of generative AI chest radiology reports for clinical workflows.

• B.A. Data Science, University of California, Berkeley, May 2022

Studied Data Science with a domain emphasis in Cognition at UC Berkeley.

Awards

Skills

Research/Academic Experience

Summer Internships

Projects

Over the last 7 years, I have done several projects covering data science and general CS areas. The projects done as part of work experience are proprietary and details are provided in the attached presentations. For projects done in open source or freelance, GitHub links are provided where possible for code.

Data Science Projects

CS Projects

Film Projects

Publications

1. R. Mahmood, K. C. L. Wong, D. M. Reyes, N. D’Souza, L. Shi, J. Wu, P. Kaviani, M. Kalra, G. Wang, P. Yan, and T. Syeda-Mahmood "A Phrase-grounded Fact-checking Model for Automatically Generated Radiology Reports,” in Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Daejeon S. Korea, Oct. 2025. The underlying dataset of 24 million pairs of images with correct/incorrect radiology findings called RadCheck has been contributed to open source on HuggingFace.
2. R. Mahmood, P. Yan, D. M. Reyes, G. Wang, M. K. Kalra, P. Kaviani, J. T. Wu, and T. Syeda-Mahmood, “Evaluating Automated Radiology Report Quality through Fine-Grained Phrasal Grounding of Clinical Findings,” Houston, TX, April. 2025.
3. R. Mahmood, Ge Wang, Mannudeep Kalra, Pingkun Yan, “Fact-Checking of AI-Generated Reports,” in Proc. Machine Learning for Medical Imaging (MICCAI Workshop), Vancouver, BC, Canada October 2023.
4. R. Mahmood, X. Liu, A. Xu, R. Akkiraju, “ContrastBERT: Supervised Contrastive Learning of BERT-Encoded IT logs for Anomaly Classification,” in Proc. IEEE Big Data Conference Workshop on Knowledge Discovery in Data Mining on IT Operations. Osaka, Japan, Dec. 2022.
5. N. Shrivastava, R. Mahmood, T. Syeda-Mahmood, “Spatially-preserving flattening in deep learning for location-aware classification,” in Proc. International Symposium on Biomedical Imaging, Kolkata, India, March, 2022.
6. R. Mahmood, T. Syeda-Mahmood, “Automatic detection of left ventricular aneurysms in echocardiograms,” in Proc. International Symposium on Biomedical Imaging (ISBI), New York, April 2015. See local copy.
7. R. Mahmood, T. Syeda-Mahmood, ”Automatic detection of dilated cardiomyopathy in cardiac ultrasound videos,” in Proc. American Medical Informatics Association (AMIA) Annual Conference, Washington, D.C., November, 2014. See local copy.

Contact

• Facebook
• LinkedIn
• GitHub

Elements

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