Jirong Yi

Welcome to Jirong Yi's webpage.

Short bibliography

I received a PhD degree in computer engineering from University of Iowa under supervision of Prof. Weiyu Xu and Xiaodong Wu. My research has been centered around computational medical imaging-based precision health. My work at University of Iowa focused on (1) compressed sensing and deep learning methods and theory for medical image reconstruction and quanlity enhancement, and (2) large-scale convex optimization for CT-guided cancer radiation therapy treatment planning optimization. See my thsis.

During my time with the Computer-aided Diagnosis (CAD) Science team at Hologic Inc, I worked on (1) deep learning for tomosynthesis-based image classification and image object detection, and (2) their applications in breast cancer diagnostic product development and deployment.

Since 2023, I have been with Prof. Piotr Slomka's team at Cedars-Sinai Medical Center, and working on (1) deep learning for cardiac perfusion SPECT/CT and PET/CT multimodality image analysis; (2) their applications in cardiovascular disease prognosis, diagnosis, and treatment; (3) construction of the world largest nuclear multimodality medical imaging spanning (PET, SPECT, CT, eletronical medical records, clinical data) databases of over 46K patients. I have long-standing and close collaboration with clinicians and industrial partners including cardiologists, radiologists, medical physicists, radiation oncologists, and medical imaging AI experts.

My research has been producing publications in leading journals (e.g., Lancet Digital Health), FDA-approved products (e.g., Genius AI Detection for tomosynthesis-based breast cancer diagnosis), and patents (e.g., CT-guided cancer radiation therapy treatment planning). See ORCID, Google Scholar, and Linkedin for more.

Education

Ph.D., Computer Engineering, University of Iowa, Iowa City, Iowa, USA, 2021
M.S., Computer Engineering, University of Iowa, Iowa City, Iowa, USA, 2020
B.S., Information and Computing Science, Harbin Engineering University, Harbin, Heilongjiang, PRC, 2015

Experience

Postdoctoral Scientist, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA, 10/2023-Now
Machine Learning Research Scientist, Computer-Aided Diagnosis Science, Hologic Inc, Santa Clara, California, USA, 09/2021-08/2023
Engineer, Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China, 07/2015-07/2016

Interests

My interest lies in the intersection of AI/ML, medical image analysis and informatics, and their translation into clinical practice and precision medicine, specifically in the following highly aligned topics:

Medical image understanding and informatics

including compressed sensing, computer vision, and deep learning methods for CT-, SPECT-, PET-, MRI-, and US-based image classification, image object detection, image segmentation, image registration, generative imaging AI, multi-modality image fusion and alignment, vision-language foundation models, agentic imaging AI systems.

Translation into clinical practice and precision medicine

including machine learning, computer vision, and deep learning techniques for CT-, SPECT-, PET-, MRI-, and US–based biomarker discovery and quantification, and disease prognosis, diagnosis, and treatment in cardiovascular disease, breast cancer, cervical cancer, prostate cancer, and so on.

Artificial intelligence and computer vision methodologies

including imbalanced learning, fine-grained learning, small-sample learning, explainable AI, domain adaptation, supervised learning, self-supervised learning, semi-supervised learning, noisy supervised learning, and model calibration.

Expertise

My expertise includes artificial intelligence/machine learning (AI/ML), medical image analysis, and their clinical translation for improving human health outcomes.

Vision

My long-term goal is to accelerate innovation and clinical translation in biomedical imaging AI by strategically aligning cutting-edge technology development, advanced medical image analysis, real-world clinical value propositions, and scalable solutions.