Greg Holste

Greg Holste

Ph.D. Candidate
ECE Department, UT Austin

About Me

I am a Ph.D. candidate in Electrical Engineering at The University of Texas at Austin advised by Atlas Wang. As part of the Visual Informatics @ UT Austin (VITA) group, I develop deep learning and computer vision techniques to solve clinical decision-making and medical image analysis problems. My research is funded by the NSF Graduate Research Fellowship Program (GRFP). [CV]

Previously, I have worked with the Medical Imaging and Data Integration (MIDI) Lab at Michigan State University under Adam Alessio on multimodal learning for breast cancer prediction and pediatric rib fracture detection.


  • Medical image analysis
  • Computer-aided diagnosis
  • Multimodal learning
  • Self-supervised learning
  • Long-tailed learning


  • Ph.D. in Electrical Engineering, 2021-present

    The University of Texas at Austin

  • M.S.E. in Electrical Engineering, 2021-2024

    The University of Texas at Austin

  • B.A. in Mathematics, 2016-2020

    Kenyon College


  • [Jun. 2024] Paper accepted to Radiology: Artificial Intelligence on contrastive learning for improved fairness in chest X-ray classification
  • [May 2024] Paper accepted to Communications Medicine on contrastive learning for echocardiography
  • [May 2024] Paper accepted to Medical Image Analysis summarizing the CXR-LT challenge
  • [Apr. 2024] Paper accepted to Scientific Reports on pediatric rib fracture detection
  • [Mar. 2024] Our challenge “CXR-LT: Long-tailed, multi-label, and zero-shot classification on chest X-rays” was accepted to MICCAI 2024
  • [Feb. 2024] Paper accepted to JAMA Cardiology on detecting aortic stenosis progression with deep learning [Preprint]
  • [Jan. 2024] Paper accepted to JAMIA on self-supervised learning for electrocardiographic (ECG) images
  • [Aug. 2023] Paper accepted to Nature Communications on fairness in chest X-ray classification
  • [Jun. 2023] Paper accepted to European Heart Journal on severe aortic stenosis detection!
  • [May 2023] Paper early accepted (top 14%) to MICCAI 2023 on pruning chest X-ray classifiers!
  • [Mar. 2023] Our workshop, Computer Vision for Automated Medical Diagnosis (CVAMD), was accepted to ICCV 2023
  • [Mar. 2023] 🎉 I was awarded the NSF Graduate Research Fellowship (GRFP)!! 🎉
  • [Jan. 2023] Paper accepted to ISBI 2023 on multimodal fusion of histopathology imaging and clinical data from my internship with Artera!
  • [Oct. 2022] Paper accepted to IEEE Transactions on Medical Imaging on weakly supervised localization in chest X-rays! [Link] [arXiv]
  • [Aug. 2022] Preprint out on automated detection of severe aortic stenosis [Paper]
  • [Jul. 2022] Paper accepted to DALI 2022 (MICCAI 2022 workshop) and abstract accepted to RSNA 2022 on long-tailed learning for chest X-rays
  • [Jun. 2022] Paper accepted to IMLH 2022 (ICML 2022 workshop) on self-supervised learning of echocardiogram videos [Paper] [Code] [Poster]
  • [May 2022] Started my internship with the AI team at Artera
  • [Oct. 2021] One paper accepted to SPIE Medical Imaging 2022 on pediatric rib fracture detection
  • [Aug. 2021] Started my Ph.D. at UT Austin with Atlas Wang!
  • [Aug. 2021] One paper accepted to CVAMD 2021 (ICCV 2021 workshop) on multimodal learning

Recent Publications

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