Ju Sun (孙举)

4-192 Keller Hall

200 Union Street SE

Minneapolis, MN 55455

Assistant Professor
Department of Computer Science & Engineering
Department of Neurosurgery
Data Science Graduate Faculty
Institute for Health Informatics (Affiliate Faculty)
Institute for Engineering in Medicine (Affiliate Faculty)
University of Minnesota, Twin Cities

Research Interests: Intersection and/or Union of machine learning, data sciences, and numerical optimization, computer vision, signal/image processing, and computational imaging, healthcare. See my [CV].

Contact

• Email: jusun@umn.edu \ \ sunjunus@gmail.com
• Office: 5-225E, KHKH
• Phone: 612-301-2729
• Webpage: http://sunju.org
• Office Hour: 5 – 6pm Tue/Thur (2021 Spring); By appointment (2021 Summer)

Seminars

• Co-organizing UMN Machine Learning Seminar Series
• UMN Data Science Seminars
• MnRI Seminar

What’s new

Upcoming

• Talk: in the session Mathematical Foundation of Data Science in Scientific Computing at AMS Spring Central Sectional Meeting (Purdue University, West Lafayette, Mar 26–27, 2022)
• Talk: in the session Theory and algorithms for nonlinear inverse problems at Asilomar 2021 (Virtual, Oct 31–Nov 03, 2021)
• Talk: In the session The Interplay Between Optimization and Statistics at 2021 INFORMS Annual Meeting (Anaheim, Oct 24–27, 2021)
• Organization: Co-organizing the 2nd Workshop on Knowledge Guided Machine Learning (KGML) (Virtual, Aug 9–13, 2021)

Past

• Service: To be an Area chair for AISTATS 2022 (Jul 5, 2021)
• Paper release: Proud to release our extended abstract Phase Retrieval using Single-Instance Deep Generative Prior that demonstrates single-instance deep generative priors (e.g., deep image priors) could be a powerful tool solving the most difficult cases of Fourier phase retrieval, a cental problem in computational imaging. (Jun 9, 2021)
• Paper release: Proud to release our paper Rethink Transfer Learning in Medical Image Classification showing that transfer learning for medical image classification should probably be performed on truncated deep models, rather than full deep models conventionally used. (Jun 9, 2021)
• Paper release: Proud to release our paper A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals that describes our year-long project on X-ray-based COVID-19 diagnostic system that has been 1) trained on extensive internal and public data, 2) carefully validated externally, and 3) deployed in real hospitals. See also the common pitfalls of the majority of AI models for COVID-19 here and here. AI is to complement and assist but not replace doctors, and close collaboration between AI experts and medical doctors is essential to building trustworthy AI models for healthcare. (Jun 05, 2021)
• Grant approval: We are funded by a U of M Informatics Institute (UMII) seed grant to revamp the GRANSO package into a user-friendly, scalable numerical optimization package. (PI, May 25, 2021)
• Grant approval: We are funded by CISCO to develop x-ray/CT-based AI systems for fracture detection in trauma care, in collaboration with the Medical School and M Health Fairview, and a number of pilot federated learning partners. (PI, May, 2021)
• Media: Gaoxiang wrote an article for the Minnesota Undergraduate Research & Academic Journal (MURAJ) featuring Le and our COVID-19 project. (Mar, 2021)
• Honors: 2021 AAAI New Faculty Highlights (Feb, 2021)
• Media: An AI-based diagnostic system for COVID-19 based on chest x-rays developed by our group has been deployed and tested in M Health Fairview, and goes live in their decision-support system today! See this UMN, M Health Fairview, and Epic joint press release, CSE news article and this coverage by StarTribune. (Oct 01, 2020)
• Talk: “运筹千里”纵横论坛 organized by Operation Research Society of China (Sep 27, 2020) [Slides]
• Talk: IMA Data Science Seminar at UMN (Sep 08, 2020)
• Talk: AIME2020: International Conference on Artificial Intelligence in Medicine (Hosted virtually by UMN, Aug 25–28, 2020) [Slides]
• Grant approval: DOE: FAIR Framework for Physics‐Inspired Artificial Intelligence in High Energy Physics (Co-PI, Aug 10, 2020)
• Grant approval: NSF 2038403: CPS: Medium: Smart Tracking Systems for Safe and Smooth Interactions Between Scooters and Road Vehicles, as covered by UMN CSE news (Co-PI, Aug 04, 2020)
• Service: To be an Area chair for AISTATS 2021 (Jul 18, 2020)
• Talk: SIAM Conference on Imaging Science Virtual Conference (Jul 6–17, 2020) [Slides]
• Grant approval: OVPR COVID19 Rapid Response Grant # 10: Rapid detection and severity trajectory of COVID19 using X-ray images and artificial intelligence (May 01, 2020)
• Talk: SIAM Conference on Mathematics of Data Science at Cincinnati, Ohio (postponed for the BEERvirus, 2020)
• Paper release: our new paper Inverse Problems, Deep Learning, and Symmetry Breaking is on arXiv. We show how symmetries in nonlinear inverse problems can cause a significant difficulty for the end-to-end deep learning approach, and how to address it by breaking the symmetry (Mar 20, 2020).
• Talk: Workshop on Numerical Algebra in High-Dimensional Data Analysis at Tianyuan Mathematical Center in Southeast China (TMSE), Xiamen, China (Dec 27–29, 2019)
• Talk: INFORMS Annual Meeting 2019 at Seattle (Oct 20–23, 2019)
• Join CS&E @ UMN as an assistant professor! (Jul 01, 2019)

Archive

• Stanford years (2016 – 2019)
• Columbia years (2011 – 2016)