Email: shaogang-ren@utc.edu | renshaogang@gmail.com
I am a tenure-track Assistant Professor of the Computer Science and Engineering Department at the University of Tennessee at Chattanooga. Before joining UTC, I spent four years as a researcher in the industry.
My research interests include deep generative models, foundation models, causality, optimization, and their applications in different areas, e.g., healthcare, computer vision, NLP, etc.
I'm actively looking for self-motivated Ph.D students and research assistants (master and undergraduate students) who are interested in large language models (LLMs), deep generative models, multimodality data processing, and their applications in healthcare and other areas. If you are interested in working with me and pursuing a degree at UTC, please email me at shaogang-ren@utc.edu.
Shaogang Ren, and Xiaoning Qian. Causal Bayesian Optimization via Exogenous Distribution Learning. arXiv:2402.02277, 2024. PDF
Shaogang Ren, and Xiaoning Qian. Dynamic Incremental Optimization for Best Subset Selection. arXiv:2402.02322, 2024. PDF
Ziyi Zhang, Shaogang Ren, Xiaoning Qian, and Nick Duffield. Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data. KDD, 2024. PDF
Ziyi Zhang, Shaogang Ren, Xiaoning Qian, and Nick Duffield. Toward Invariant Time Series Forecasting in Smart Cities. 10th International Smart City Workshop-The Web and Smart Cities, 2024. PDF
Shaogang Ren, Dingcheng Li, and Ping Li. Word Embedding with Neural Probabilistic Prior. Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), 2024. PDF Code
Shaogang Ren, Hongliang Fei, Dingcheng Li, and Ping Li. Learning Latent Structural Relations with Message Passing Prior. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023. PDF Supp
Shaogang Ren, Dingcheng Li, and Ping Li. Causal Effect Prediction with Flow-based Inference. The IEEE International Conference on Data Mining (ICDM), 2022. Short paper. PDF
Shaogang Ren, Belhal Karimi, Dingcheng Li, and Ping Li. Variational Flow Graphical Model. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. PDF
Shaogang Ren and Ping Li. Flow-based Perturbation for Cause-effect Inference. The Conference on Information and Knowledge Management (CIKM), 2022. PDF Code
Peng Yang, Shaogang Ren, Yang Zhao, Ping Li. Calibrating CNNs for Few-Shot Meta Learning. Winter Conference on Applications of Computer Vision (WACV), 2022. PDF
Shaogang Ren, Haiyan Yin, Mingming Sun, and Ping Li. Causal Discovery with Flow-based Conditional Density Estimation. The IEEE International Conference on Data Mining (ICDM), 2021. Short paper. PDF Code
Dingcheng Li, Hongliang Fei, Shaogang Ren, Ping Li. A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation. Findings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. PDF
Shaogang Ren, Weijie Zhao, and Ping Li. Thunder: a Fast Coordinate Selection Solver for Sparse Learning. Advances in Neural Information Processing Systems (NeurIPS), 2020. PDF Code
Shaogang Ren, Dingcheng Li, Zhixin Zhou, and Ping Li. Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection. Proceedings of The Web Conference (WWW), 2020. PDF Code
Shaogang Ren, Shuai Huang, Jieping Ye, and Xiaoning Qian. Safe Feature Screening for Generalized LASSO. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Issue: 99, 2018. PDF Code
Shaogang Ren, Shuai Huang, John Onofrey, Xenophon Papademetris, and Xiaoning Qian. A Scalable Algorithm for Structured Kernel Feature Selection. 18th International Conference on Artificial Intelligence and Statistics (AIStats), 2015. PDF Code
Shaogang Ren, Bo Zeng, and Xiaoning Qian. Adaptive Bi-level Programming for Optimal Gene Knockouts for Targeted Overproduction under Phenotypic Constraints. 11th Asia Pacific Bioinformatics Conference, 2013; Journal of BMC Bioinformatics. PDF Code
Shaogang Ren and Xiaoning Qian. Structured Sparse PCA to Identify MiRNA Co-regulatory Modules. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. PDF Code
Shaogang Ren and Yu Sun. Human-Object-Object-Interaction Affordance. Winter Vision Meeting (WVM), 2013. PDF
Yu Sun, Shaogang Ren, and Yun Lin. Object-Object Interaction Affordance Learning. Journal of Robotics and Autonomous Systems, 2013. PDF
Yun Lin, Shaogang Ren, and Yu Sun. Learning Grasping Force from Demonstration. IEEE International Conference on Robotics and Automation (ICRA), 2012. PDF
Meltem Apaydin, Bo Zeng, Shaogang Ren, and Xiaoning Qian. A Computationally Efficient Solution Strategy for Optimal Gene Knockouts for Targeted Overproduction. The 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, 2015. PDF
Dissertation
Shaogang Ren. Scalable Algorithms for High Dimensional Structured Data. Texas A&M Unversity, 2017. PDF
Fall-24 UTC, Course title: Design and Analysis of Computer Algorithms; Course number: CPSC 5210
Spring-23 TAMU, Course title: Computational Data Science; Course number: ECEN 360 / STAT 315
Reviewer or committee member for ICLR 2025, NeurIPS 2024, AIStats 2024, ICLR 2024, NeurIPS 2023, ICML 2023, ICLR 2023, NeurIPS 2022, KDD 2022, ICML 2022, AIStats 2022, NeurIPS 2021, ICML 2020, IJCAI 2020, IJCAI 2019, IEEE Robotics and Automation Letters 2018.
Shaogang Ren is a tenure-track Assistant Professor of the Computer Science and Engineering Department at the University of Tennessee at Chattanooga. His research focuses on deep generative models, foundation models, causality, optimization, and their applications in different areas. Shaogang received his Ph.D degree in Computer Engineering from Texas A&M University in 2017. He holds a Master's degree in Computer Science from the University of South Florida, a Master's degree in Computer Architecture from Huazhong University of Science & Technology, and a Bachelor's degree in Computer Science from Central South University. He has been a reviewer or committee member for different machine learning venues, e.g., NeurIPS, ICLR, ICML, etc.