Graduate Students: Multiple positions for graduate students are available for the study of machine learning, computer vision, NLP, trustworthy AI, or AI for healthcare. We do not make decision before seeing your application in the application system.
Intern Students and Visiting Students/Scholars: All formats of local and remote collaboration are welcomed.
Funded 2022 Summer Internship: Four positions are available in our group with the Mitacs Globalink Research Internship Program. Interns will be funded by Mitas or its international partner. Please click the link for more details. Apply by 9/22/2021!
If you want to work with me, please email me (click here) including your CV, transcript, and one of your research papers if applicable. Due to the large amount of emails I receive, I may not be able to respond to each one individually. To help me notice you email, please put "[ILOLLEH]", written backwards, in your email subject. Please strictly follow the instruction :)
[10/2021] One paper is accepted by BMVC 2021.
[09/2021] One paper is accepted by NeurIPS 2021 (Spotlight). Congrats, Monica!
[09/2021] One paper is accepted by Medical Image Analysis.
[08/2021] One paper is accepted by DCL workshop.
[08/2021] We receive NVIDIA Academic Hardware Grant (1 NVIDIA A100). Thank Nvidia for the generous sponsorship.
[06/2021] One paper is accepted at MICCAI 2021.
[05/2021] One paper is accepted at ICML 2021.
[01/2021] Proposal about drug discovery funded by MSR & Mila Research ($65,000 CAD).
[01/2021] Two papers are accepted at ICLR 2021.
[10/2020] Best Paper Award in DART 2020.
[08/2020] Awarded MICCAI Student Participation Award.
[07/2020] One paper is accepted at Medical Image Analysis (IF 11.15).
[06/2020] One paper is accepted at ICML 2020.
[05/2020] Two papers is accepted at MICCAI 2020.
[03/2020] Passed Thesis Defense at Yale.
[10/2019] Best Paper Award in MLMI 2019.
[06/2019] Two papers are accepted at MICCAI 2019.
For the complete list, please check my google scholar. Email me if you have any questions about my papers or code, or if you would like to collaborate with me.
Subgraph Federated Learning with Missing Neighbor Generation.
Zhang, K., Yang, C., Li, X., Sun, L., & Yiu, SM.
NeurIPS 2021 (Spotlight).
EMA: Auditing Data Removal from Trained Models.
Huang, Y., Li, X.*, & Li, K.
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis.
Huang, B., Li, X., Song, Z., & Yang, X.
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization.
, Jiang, M., Zhang, X., Kamp, M., & Dou, Q.
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization.
Chen, S., Li, X., Song, Z., & Zhuo, D.
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results.
Li, X., Gu, Y., Dvornek, N., Staib, L., Ventola, P., & Duncan, J. S.
Medical Image Analysis.
Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty.
Li, X., Zhou, Y., Dvornek, N., Gu, Y., Ventola, P., & Duncan, J. S.
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis.
Li, X., Zhou, Y., Dvornek, N., Zhang, M., Zhuang, J, Ventola, P., & Duncan, J. S.
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE.
Zhuang, J., Dvornek, N.C., Li, X., Tatikonda, S., Papademetris, S.,and Duncan, J.S.
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery.
Li, X., Dvornek, N.C., Zhou, Y., Zhuang, J., Ventola, P. and Duncan, J.S.
Interpretable Multimodality Embedding of Cerebral Cortex Using Attention Graph Network for Identifying Bipolar Disorder.
Yang, H.*, Li, X.*, , Wu, Y., Li, S., Lu, S., Duncan, J.S., Gee, J.C. and Gu, S.
Graph Neural Network for Interpreting Task-fMRI Biomarkers.
Li, X., Dvornek, N.C., Zhou, Y., Zhuang, J., Ventola, P. and Duncan, J.S.
Here are my recent exciting projects. For the full list of my projects, please refer to my publications.
Federated Learning on Non-iid Features via Local Batch Normalization
Most of the previous federated learning work has focused on a difference in the distribution of labels. Unlike those settings, we address an important problem of FL, e.g., different scanner/sensors in medical imaging, where local clients may store examples with different marginal or conditional feature distributions compared to other nodes, which we denote as feature shift non-iid. In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models.
Transfer Pruning for Inversion Attack Defense
We propose to prune a target neural network with a public datasetand transfers it to the distributed learning setting to collaboratively train the pruned network with distributed private datasets via sharing a hidden layer of representations, without sharing the data. Our experiments show that the proposed approach achieves a much better trade-off between accuracy and privacy preservation.
Privacy-preserving Multi-site Medical Images Analysis
We present a privacy-preserving federated learning framework for multi-site fMRI analysis. To overcome the domain shift issue, we have proposed two strategies: MoE and adversarial domain alignment to boost federated learning model performance. We have shown federated learning performance can potentially be boosted by adding domain adaptation and discussed the condition of benefits. Our approach brings new hope for accelerating deep learning applications in the field of medical imaging, where data isolation and the emphasis on data privacy have become challenges.
BrainGNN: Explainable Graph Neural Network for Neuroimaging Analysis
BrainGNN takes graphs built from neuroimages as inputs, and then outputs prediction results together with interpretation results.With the built-in interpretability, BrainGNN not only performs better on prediction than alternative methods, but also detects salient brain regions associated with predictions and discovers brain community patterns.
DistDeepSHAP: Efficient Shapley Explanation for Feature Importance Estimation Under Uncertainty
We propose DistDeepSHAP, a post-hoc feature importance estimation method under uncertainty evaluation for deep learning models.First, it can obtain uncertainty estimates for the provided feature importance scores. Second, it can better utilize the empirical distribution and has the potential for better feature importance scores estimation from the generated percentile saliency maps. Last but not least, it can compare with arbitrary subgroup references and interpret subgroup salient features, which is crucial for medical image study.
[10/2021] Invited talk about "Trusted AI for Healthcare: from Theory to Practice" at Chinese University of Hong Kong.
[02/2021] Invited talk about Trustworthy AI for Healthcare at Emory University.
[01/2021] Invited talk about Trustworthy AI for Healthcare at Istanbul Technical University.
[11/2020] Invited talk about BrainGNN at Tulane University.
[11/2020] Invited talk about BrainGNN at Lehigh University.
[09/2020] Invited talk about Multi-site Federated Learning for NeuroImaging Analysis at Chinese Academy of Sciences.
[2017, 2018] ENAS 194: Ordinary and Partial Differential Equation (Yale University)
[2017, 2019] BENG 352: Biosignal Processing (Yale University)
 COS 598D: Systems and Machine Learning (Princeton University) [Course website]
 ELEC 400M: Machine Learning Fundamentals for Engineers (UBC) [Syllabus]
Chun-Yin Huang (PhD of 2021, from Carnegie Mellon University)
Sana Ayromlou (MASc of 2021, from Sharif University of Technology, co-advised with Prof. Purang Abolmaesumi)
Nan Wang (Visting PhD student, from East China Normal University)
Organizer of NeurIPS 2021 workshop on Medical Imaging Meets NeurIPS.
Organizer of ICML 2021 workshop on Interpretable ML in Healthcare.
Organizer of MICCAI 2021 workshop on Distributed and Collaborative Learning
President of Women in MICCAI (2021-2023)
Reviewer of ICML, ICLR, NeurIPS, ICCV, AAAI, MICCAI, IPMI, TPAMI, Nature Machine Intelligence, MedIA, TMI, Neural Networks, etc.