Fang, S., Yu, X., Li, S., Wang, Z., Kirby R., & Zhe, S. Streaming Factor Trajectory Learning for Temporal Tensor Decomposition. Advances in Neural Information Processing Systems (NeurIPS 2023)
Xin Yu
I am a final-year Ph.D. student in Kahlert School of Computing at the University of Utah. I’m now doing research on machine learning and computer vision, under the supervision of Prof. Shandian Zhe and Prof. Srikumar Ramalingam. In 2022, I joined the AI for Science group focusing on physical simulation, under the mentorship of Prof. Shandian Zhe. Prior to this, during my master’s studies, I was part of the data group, advised by Prof. Feifei Li.
My research interests lie in the intersection of Deep Learning and Optimization and focus on reliable and economical AI techniques for large-scale AI models in real-world scenarios. Specifically, I’m exploring efficient computation, economic data abstraction and collection, and bias reduction and robustness enhancement in AI systems. Drawing upon my expertise in combinatorial optimization, Bayesian machine learning, and deep neural network expressiveness, I aim to address these multifaceted challenges in computer vision and physical simulation applications. This interdisciplinary approach allows me to forge new pathways in creating more efficient, equitable, and powerful AI technologies.
For more information, see my full resume.
News
[01/2024]: Our paper Multi-Resolution Active Learning of Fourier Neural Operators is accepted by AISTATS 2024 (oral).
[01/2024]: Our paper Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data is accepted by ICLR 2024.
[12/2023]: Our paper Streaming Factor Trajectory Learning for Temporal Tensor Decomposition will be presented on NeurIPS 2023.
[05/2023]: Our paper Getting away with more network pruning: From sparsity to geometry and linear regions will be presented on CPAIOR 2023.
[11/2022]: Our papers Batch Multi-Fidelity Active Learning with Budget Constraints and Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm are presented on NeurIPS 2022.
[07/2022]: Our paper The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks wil be presented on ICML 2022.
[12/2021]: Our paper Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer Graph wil be presented on 3DV 2021.
[12/2021]: Our paper Scaling Up Exact Neural Network Compression by ReLU Stability wil be presented to NeurIPS 2021.
[11/2020]: Our paper Mapping of Sparse 3D Data using Alternating Projection wil be presented on ACCV 2020 (oral).
[10/2018]: Our paper VLASE: Vehicle localization by aggregating semantic edges wil be presented on IROS 2018.
[06/2018]: Our paper Learning strict identity mappings in deep residual networks wil be presented on CVPR 2018.
Education
- Ph.D. in Computer Science, University of Utah, 2018 - Now
- Master in Computer Science, University of Utah, 2016 - 2018
Publications
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Cai J., Nguyen K., Shrestha N., Good A., Tu R., Yu X., & Serra T. Getting away with more network pruning: From sparsity to geometry and linear regions. International Confer- ence on Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2023)
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Li, S.*, Phillips, J.*, Yu, X., Kirby, R., & Zhe, S. Batch Multi-Fidelity Active Learning with Budget Constraints. Advances in Neural Information Processing Systems (NeurIPS 2022).
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Good A.*, Lin J.*, Yu X.*, Sieg H., Ferguson M., Zhe S., Wieczore J., & Serra T. Recall Ditortion in Neural Network Pruning and the Undecayed Pruning Algorithm. Advances in Neural Information Processing Systems (NeurIPS 2022)
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Yu X.*, Serra T.*, Ramalingam S., Zhe S. The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks. In International Conference on Machine Learning (ICML 2022).
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Yu X., & Baar J, Chen S. Joint 3D Human Shape Recovery and Pose Estimation from A Single Image with Bilayer-Graph. in International Conference on 3D Vision (3DV 2021)
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Serra, T., Yu, X., Kumar, A. and Ramalingam, S., 2021. Scaling Up Exact Neural Network Compression by ReLU Stability. arXiv preprint arXiv:2102.07804. (NeurIPS 2021)
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Ranade, S.*, ,Yu, X.*, Kakkar, S., Miraldo, P. and Ramalingam, S., 2020. Mapping of Sparse 3D Data using Alternating Projection. In Proceedings of the Asian Conference on Computer Vision.(ACCV 2020 Oral)
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Yu, X., Chaturvedi, S., Feng, C., Taguchi, Y., Lee, T.Y., Fernandes, C. and Ramalingam, S., 2018, October. Vlase: Vehicle localization by aggregating semantic edges. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3196-3203). IEEE. (IROS 2018)
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Yu, X., Yu, Z. and Ramalingam, S., 2018. Learning strict identity mappings in deep residual networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4432-4440) (CVPR 2018)
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Paul, D., Li, F., Teja, M.K., Yu, X. and Frost, R., 2017, August. " Compass: Spatio temporal sentiment analysis of US election what twitter says!." In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1585-1594). (KDD 2017)
Teaching
Service and leadership
- Conference REviewer: ICLR 2024, ICML 2023, NeurIPS 2023, NeurIPS 2022, IROS 2021, ICVGIP 2021
- Program Committee: ICDM Workshop 2023