I am a Ph.D. candidate at the University of North Carolina at Chapel Hill, where I work on computer vision with Roni Sengupta.
I previously worked with Bardia Nezami during my time as a software engineer at ValtedSeq.
Prior to that, I earned my M.S.E. in Biomedical Engineering and Data Science from Johns Hopkins University, where I was mentored by Brian Caffo.
Earlier in my academic journey, I completed my B.S.E. in Biomedical Engineering at Northeastern University under the guidance of Ming Qian at the Shenzhen Institute of Advanced Technology.
Opinions are my own and do not represent any affiliated organizations.
Iām interested in controlling generative models + multimodalities (e.g. 3D, video, etc.). My previous research focused on personalized face generation and editing. More recently, my work has explored video generation and editing. My future research interests include combining implicit and explicit representations for generative modeling, for example, integrating data-driven approaches with inductive biases.
Click here if you'd like to work with me!
If you would like to collaborate, the best way to reach out is through email!
Depending on the project, please indicate how exactly I can help you with your research in the email. For example,
providing your surveyed literature list or tested baselines and need my help formulating the high-level problem.
providing your current results and need my help with low-level experiment design for next steps.
Due to the large number of emails, I may not be able to respond to all of them :( But I will do my best to reply to your message!
We perform face editing in diffusion models (FLUX) via attention manipulation in a training-free manner, exploring the inversionāediting trade-off in the context of facial aging.
Inspired by The Irishman, MyTimeMachine performs personalized de-aging and aging with high fidelity and identity preservation from ~50 selfies, extendable to temporally consistent video.
We introduce a continual learning problem of updating personalized 2D and 3D generative face models without forgetting past representations as new photos are regularly captured.
We personalize a pretrained GAN-based model (EG3D) using a few (~50) selfies of an individual without full finetuning, enabling scalable personalization in a real-world scenario.
Non-Contact High-Frequency Ultrasound Microbeam Stimulation: A Novel Finding and Potential Causes of Cell Responses
Luchao Qi, Qi Zhang , Yan Tan, Kwok Ho Lam, Hairong Zheng , Ming Qian
IEEE Transactions on Biomedical Engineering 2019 pdf
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google scholar
We investigate how ultrasound microbeam stimulation affects the intracellular calcium response of human breast cancer cells.