Memorial Hospital Research Laboratories

The Harini Veeraraghavan Lab

Research

Harini Veeraraghavan
Harini Veeraraghavan, PhD
Attending Computer Scientist

Professor

Attending Computer Scientist Harini Veeraraghavan’s lab develops and translates new AI and machine learning tools diagnosing and personalizing cancer treatments through automated segmentation of normal tissues and tumors applied to radiation treatment automation, early predicting treatment response and toxicity prediction, and longitudinal tumor treatment response monitoring from medical images.

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Research Projects

Our AI models identify features that best signal contrast between tumor and background for highly accurate tumor segmentation.
Our AI models can track voxel-level changes in tumors and organs to provide explainable biomarkers of treatment outcomes.
Our AI image foundation models segment organs from brain to pelvis and are capable of segmenting tumors from multiple imaging modalities.

Publications Highlights

Aneesh Rangnekar and Harini Veeraraghavan, “Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation”, in Transactions Machine Learning Research. 2026: 2026:7268. https://pubmed.ncbi.nlm.nih.gov/42370064/

Sudarshan Madhavan, Chengcheng Gui, Lando Bosma, Josiah Simeth, Jue Jiang, Nicolas Cote, Nima Hassan Rezaeian, Himanshu Nagar, Victoria Brennan, Neelam Tyagi, Harini Veeraraghavan. “Segmentation regularized training for multi-domain deep learning registration applied to magnetic resonance-guided prostate cancer radiotherapy”, Physics Imaging Radiation Oncology. 2026. 39:100989. DOI: 10.1016/j.phro.2026.100989. https://pubmed.ncbi.nlm.nih.gov/42232230/

Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan. “Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images”, Proceedings of the 8th International Conference on Medical Imaging in Deep Learning, Proc Machine Learning Research 301: 679-694, 2026. https://pubmed.ncbi.nlm.nih.gov/42180651/

Chloe Min Seo Choi, Jue Jiang, Nikhil P Mankuzhy, Nishant Nadkarni, Sudarshan Madhavan, Abraham J Wu, Joseph O Deasy, Maria Thor, Andreas Rimner, Harini Veeraraghavan. “Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients”, Physics Imaging Radiation Oncology. 2026. 37:100907. DOI: 10.1016/j.phro.2026.100907. https://pubmed.ncbi.nlm.nih.gov/41631006/

Jorge Tapias Gomez, Nishant Nadkarni, Lando S Bosma, Jue Jiang, Ergys D Subashi, William P Segars, James M Balter, Mert R Sabuncu, Neelam Tyagi, Harini Veeraraghavan. “Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion”, Physics Medicine Biology. 71(1):015029. DOI: 10.1088/1361-6560/ae2b46. https://pubmed.ncbi.nlm.nih.gov/41370930/

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People

Harini Veeraraghavan

Harini Veeraraghavan, PhD

Attending Computer Scientist

Professor

  • Attending Computer Scientist Harini Veeraraghavan's lab develops and translates new AI and machine learning tools diagnosing and personalizing cancer treatments through automated segmentation of normal tissues and tumors applied to radiation treatment automation, early predicting treatment response and toxicity prediction, and longitudinal tumor treatment response monitoring from medical images.
  • PhD, University of Minnesota

Members

Min Seo Chloe Choi
Deonte Hall
Josiah Simeth
Jorge Tapias-Gomez
Shehtab Zaman
Sulov Chalise
Senior Analyst and Researcher
Duc Fehr
Goodyear Inc.
Jue Jiang
Machine Learning Scientist, MSK
Jue Jiang
Machine Learning Scientist
Peter Klages
Medical Physicist, Princess Margaret Cancer Center
Sudarshan Madhavan
Aneesh Rangnekar
Florent Tixier
Nationwide Children’s Hospital
Hyemin Um
Case Western Reserve University, pursuing PhD
Wei Zhao

Achievements

  • Dr. Veeraraghavan was awarded an NIBIB R01 grant as M-PI (with PI Dr. Neelam Tyagi) for developing AI-based virtual digital twin MRI images for developing and validating longitudinal registration and dose accumulation methods applied to pancreatic cancer patients treated with MRI guided radiation treatments.
  • Dr. Veeraraghavan was awarded an NIH R01 grant as contact-PI (with M-PI Dr. Andreas Rimner) for improving safety of lung cancer radiotherapies using AI-based segmentation and tracking of tumor and tissue changes on weekly cone-beam CTs.
  • Dr. Veeraraghavan was awarded an Elekta research grant with M-PI Dr. Neelam Tyagi where we are working on developing and evaluating the automated segmentation and tracking of dominant index prostatic lesions from MRI.
  • The Veeraraghavan Lab has successfully implemented an AI auto-segmentation solution for radiotherapy planning for head and neck cancer (May 2020) and lung cancer (July 2021), with an MRI-based automated segmentation and registration solution for upper GI organs used for pancreatic cancer treatments under testing prior to release into clinic.

Open Positions

To learn more about available postdoctoral opportunities, please visit our Career Center

To learn more about compensation and benefits for postdoctoral researchers at MSK, please visit Resources for Postdocs

Career Opportunities

The Veeraraghavan Lab has multiple openings involving developing deep learning methods for longitudinal tumor treatment response monitoring, image registration for dose accumulation, as well as developing machine learning methods with sparse and heteromodal data sets for response prediction.

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Disclosures

Members of the MSK Community often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community. These activities outside of MSK further our mission, provide productive collaborations, and promote the practical application of scientific discoveries.

MSK requires doctors, faculty members, and leaders to report (“disclose”) the relationships and financial interests they have with external entities. As a commitment to transparency with our community, we make that information available to the public. Not all disclosed interests and relationships present conflicts of interest. MSK reviews all disclosed interests and relationships to assess whether a conflict of interest exists and whether formal COI management is needed.

Harini Veeraraghavan discloses the following relationships and financial interests:

No disclosures meeting criteria for time period


The information published here is a complement to other publicly reported data and is for a specific annual disclosure period. There may be differences between information on this and other public sites as a result of different reporting periods and/or the various ways relationships and financial interests are categorized by organizations that publish such data.

This page and data include information for a specific MSK annual disclosure period (January 1, 2025 through disclosure submission in spring 2026). This data reflects interests that may or may not still exist. This data is updated annually.

Learn more about MSK’s COI policies here. For questions regarding MSK’s COI-related policies and procedures, email MSK’s Compliance Office at [email protected].


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