I am currently a postdoctoral scholar at Medical-image Analysis and Statistical Interpretation lab (MASI) of Vanderbilt University. My PI is Dr. Bennett Landman. I earned my Ph.D. from Image Analysis and Communications Laboratory (IACL) of Johns Hopkins University in 2024. My advisor is Dr. Jerry Prince. I was also a predoctoral research fellow at Laboratory of Behavioral Neuroscience of the National Institute on Aging from 2019 to 2023, under the supervision of Dr. Susan Resnick.

RESEARCH INTERESTS

• Medical image analysis
• Image synthesis and harmonization
• Magnetic resonance imaging
• Machine learning and deep learning

NEWS

2024

• [11/2024] I filed two provisional patents based on my recent work “Extending the Field of View of Chest CT Images Using Latent Diffusion” and “Robust Body Composition Analysis by Generating 3D CT Volumes from Limited 2D Slices”.
• [11/2024] Nine papers (two as first author, one as last author) accepted at SPIE: Medical Imaging 2025. See you in San Diego!
• [09/2024] I will teach ES1400: Intro to Electrical and Computer Engineering as a co-instructor.
• [08/2024] I gave an invited talk titled “Decoding AI’s creative mind: An introduction to generative models.” at VALIANT Summer School 2024.
• [03/2024] I started my new position as a postdoctoral scholar at Vanderbilt University!
• [02/2024] My co-authored paper “Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis” was presented at the deep dive session of SPIE Medical Imaging 2024.
• [02/2024] I presented “Inconsistent MR acquisition in longitudinal volumetric analysis: Impacts and solutions” at the 2024 Department of Medicine and Whiting School of Engineering Research Retreat, Johns Hopkins University.
• [01/2024] I successfully defended my Ph.D. dissertation titled “Unsupervised structural MRI harmonization by learning disentangled representations”. Heartfelt thanks to my committee members: Dr. Jerry Prince, Dr. Trac Tran, and Dr. Mathias Unberath.
• [01/2024] “Deep learning for medical image analysis (2nd edition)” is now available! I am the lead author of “Chapter 5: An overview of disentangled representation learning for MR images”. I also co-authored Chapter 16: OCTA Segmentation with limited training data using disentangled representation learning”.

2023

• [12/2023] I gave a seminar talk titled “From voxels to patients: A data-centric journey towards consistent translational AI” at Vanderbilt University. Thanks for the invitation from Dr. Bennett Landman.
• [11/2023] I won the Best Poster Award at Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS) Young Scientist Summit, 2023.
• [10/2023] My co-authored paper “Self-supervised super-resolution for anisotropic MR images with and without slice gap” won the Best Paper Award at the 8th Workshop in Simulation and Synthesis in Medical Imaging (SASHIMI) in conjunction with MICCAI 2023.
• [09/2023] My new harmonization work “HACA3: A unified approach for multi-site MR image harmonization” is published in Computerized Medical Imaging and Graphics. Code is available at GitHub.
• [09/2023] I was a selected as a finalist for the 2024 Siebel Scholarship.
• [08/2023] I was honored to receive the Young Scientist Summit Training Award from the Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS), 2023.
• [06/2023] I was interviewed by the Video Journal of Neurology, discussing the how to fix inconsistent MRI data using image harmonization and the future of MS image standardization. The interview is available online.
• [06/2023] I was interviewed by NeurologyLive on novel harmonization approaches for multiple scelrosis care The interview is available online.