Xinhui Li

I am a third-year Ph.D. student in Electrical and Computer Engineering at the Georgia Institute of Technology and the Center for Translational Research in Neuroimaging and Data Science (TReNDS), advised by Dr. Vince Calhoun and Dr. Rogers Silva. Previously, I worked as a research engineer at the Child Mind Institute, where I developed a software pipeline C-PAC for MRI preprocessing and analysis.

I used to be an engineer dreaming to connect brains and computers, now I am growing as a scientist attempting to solve fundamental questions in neuroscience and computer science. My current research interests include representation learning and multimodal neuroimaging analysis. To develop a comprehensive understanding of the brain, I am developing a deep multidataset subspace analysis framework including linear and nonlinear latent variable models (ICA/IVA/ISA) to learn linked and identifiable representations from multimodal neuroimaging datasets. Apart from my job, I am curious about biological and artificial intelligent algorithms and systems. I believe that understanding the principles of neural computation will help us to develop more flexible artificial intelligence algorithms and build more energy-efficient machines.

Please feel free to reach out if you want to discuss any questions or collaboration opportunities. I would also love to hear your feedback so I can learn and grow.


Apr 15, 2024 Our paper, DeepSeg: A transfer-learning segmentation tool for limited sample training of nonhuman primate MRI, is accepted at EMBC 2024.
Apr 5, 2024 I am excited to start my first industrial internship as a data scientist at Amazon this summer!
Jan 19, 2024 At OHBM 2024, I will present:
  • Educational Course: Communicating neuroscience across peoples, languages, and cultures
  • Poster Session: Deep independent vector analysis learns linked and identifiable sources from multimodal data
  • BrainArt Exhibition: Schizosymphony
Dec 1, 2023 I am honored to receive the Distinguished Scholar Award from the TReNDS Center and the D-MAP Center. I am grateful to my advisors and collaborators for their guidance and support.
Oct 26, 2023 I am selected to attend WE23. Thanks Georgia Tech ECE for travel support!
Aug 24, 2023 Two abstracts on multimodal subspace independent vector analysis (MSIVA) and deep independent vector analysis (DeepIVA) are accepted at RSBC 2023. Thanks RSBC organizers for travel support!
Jun 19, 2023 Two papers are accepted at ICML 2023 workshops:
Jun 7, 2023 I have the pleasure to interview Dr. Hongkui Zeng, the OHBM 2023 Talairach speaker. Check out our blog post to learn about Hongkui’s career trajectory, exciting research, suggestions to junior researchers, and more!
Mar 13, 2023 Three abstracts are accepted at OHBM 2023.
Jan 22, 2023 Two papers are accepted at ISBI 2023:
Dec 1, 2022 I am honored and humbled to receive the Cadence Diversity in Technology Scholarship. It means a lot to me that my experience could inspire the next generation of women in techology.
Oct 22, 2022 Our paper Pipeline-Invariant Representation Learning for Neuroimaging is accepted at ML4H Symposium 2022. Looking forward to the trip to NOLA!
Jul 16, 2022 I start working as the website and communications manager in the OHBM BrainArt Special Interest Group! I hope to promote the communications between neuroscientists and artists, and ultimately promote the interaction between neuroscience and art.
Jul 11, 2022 Our paper Mind the gap: functional network connectivity interpolation using variational autoencoder is accepted as an oral presentation at EMBC 2022.
Jun 19, 2022 I present 3 posters at OHBM 2022:
  • Moving Beyond Processing and Analysis-Related Variation in Neuroscience
  • Mind the gap: functional network connectivity interpolation using variational autoencoder
  • Direct linkage detection with Multimodal IVA fusion: uncovering joint biomarkers in large studies
Mar 5, 2022 I give a talk about inter-pipeline agreement at OpenTalks.
Feb 15, 2022 I am honored to be a scholar in the Georgia Tech/Emory Computational Neural Engineering Training Program.
Dec 3, 2021 Our new preprint Moving Beyond Processing and Analysis-Related Variation in Neuroscience is available on bioRxiv!
Oct 6, 2021 I give a talk about the Configurable Pipeline for the Analysis of Connectomes (C-PAC) at OpenTutorials.