Tong Shu

Previous Scheduled Teaching

Published Intellectual Contributions

Conference Proceeding

• Vadnerkar, K.M., Eze, E.A., Gautam, R., Han, D., Liang, X., Shu, T. (2024). A Deep Learning Approach to Maximizing Electrostatic Sieve Efficiency in Regolith Beneficiation. 4248-4256. Washington DC, USA, IEEE International Conference on Big Data (BigData): 5th International Workshop on Big Data & AI Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD). https://ieeexplore.ieee.org/abstract/document/10826123?casa_token=bCxPwuhTg-sAAAAA:q4GUqAnmKyC85VseglEJ2uzgmIAlOihmEVJOifMKTmbVa1biuIYcKtipXV8oBJXAJnRmQuAP0os
• Nazeri, A., Godwin, D.W., Panteleaki, A.M., Anagnostopoulos, I., Edidem, M.I., Li, R., Shu, T. (2024). Exploration of TPU Architectures for the Optimized Transformer in Drainage Crossing Detection. 4178-4187. Washington DC, USA, IEEE International Conference on Big Data (BigData): 5th International Workshop on Big Data & AI Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD). https://ieeexplore.ieee.org/abstract/document/10826077?casa_token=E2Aii8QUz-4AAAAA:PeZNox8BcohCQI7ZlSNhdsjjC4hEC4lIQ9aIKdqZjOxQ0W4hJsCizkffQpthrxvPxAC0Sfr7Seo
• Zendehdel, N., Mosharrof, A., Delgado, K., Han, D., Liang, X., Shu, T. (2024). Modeling Lunar Surface Charging Using Physics-Informed Neural Networks. 4269-4274. Washington DC, USA, IEEE International Conference on Big Data (BigData): 5th International Workshop on Big Data & AI Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD).
• Pandey, D., Ghebremichael, J., Qi, Z., Shu, T. (2024). A Comparative Survey: Reusing Small Pre-Trained Models for Efficient Large Model Training. 56-63. Atlanta, GA, USA, Workshops of IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W): 5th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S). https://ieeexplore.ieee.org/abstract/document/10820576
• Pandey, D., Shu, T. (2024). AM-DGCNN: Leveraging Graph Attention Networks and Edge Attributes for Link Classification in Knowledge Graphs. Atlanta, GA, USA, Workshops of IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W): Workshop on Machine Learning with Graphs in High-Performance Computing Environments. https://ieeexplore.ieee.org/abstract/document/10820575
• Zhang, Y., Pandey, D., Wu, D., Kundu, T., Li, R., Shu, T. (2023). Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations. 1780-1788. Denver, CO, USA, Workshops of ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W). https://dl.acm.org/doi/10.1145/3624062.3624260
• Li, Y., Baik, J., Rahman, M., Anagnostopoulos, I., Li, R., Shu, T. (2023). Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices. 1767-1775. Denver, CO, USA, Workshops of ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W). https://dl.acm.org/doi/10.1145/3624062.3624258
• Kundu, T., Shu, T. (2023). HIOS: Hierarchical Inter-Operator Scheduler for Real-Time Inference of DAG-Structured Deep Learning Models on Multiple GPUs. 95-106. Santa Fe, NM, USA, IEEE International Conference on Cluster Computing (Cluster). https://ieeexplore.ieee.org/document/10319967
• Caldwell, J., Feng, W., Byun, M., Albert, M.V., Shu, T., Ji, Y. (2023). Exploring Power and Thermal Dynamics in the Summit Supercomputer: A Data Visualization Study. 7th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC).
• Haleem, Y., Wagenvoord, I., Wei, Q., Xiao, T., Shu, T., Ji, Y. (2023). Understanding Nationwide Power Outage and Restoration for Future Prediction. 7th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC).
• Shu, T., Guo, Y., Wozniak, J., Ding, X., Foster, I., Kurc, T. (2021). Bootstrapping In-Situ Workflow Auto-Tuning via Combining Performance Models of Component Applications. 28: 1-15. St. Louis, MO, USA, ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC). https://dl.acm.org/doi/10.1145/3458817.3476197

Journal Article

• Foster, I., Ainsworth, M., Bessac, J., Cappello, F., Choi, J., Di, S., Di, Z., Gok, A., Guo, H., Huck, K., Kelly, C., Klasky, S., Kleese Van Dam, K., Liang, X., Mehta, K., Parashar, M., Peterka, T., Pouchard, L., Shu, T., Tugluk, O., Van Dam, H., Wan, L., Wolf, M., Wozniak, J., Xu, W., Yakushin, I., Yoo, S., Munson, T. (2021). Online Data Analysis and Reduction: An Important Co-design Motif for Extreme-scale Computers. International Journal of Higher Performance Computing Applications. 35 (6) 617-635. SAGE.
• Shu, T., Wu, C. (2020). Energy-efficient Mapping of Large-scale Workflows under Deadline Constraints in Big Data Computing Systems. Future Generation Computer Systems. 110 515-530. Elsevier. https://www.sciencedirect.com/science/article/abs/pii/S0167739X17300468

Poster

• Shu, T., Guo, Y., Wozniak, J., Ding, X., Foster, I., Kurc, T. (2021). POSTER: In-situ Workflow Auto-tuning through Combining Component Models. 467–468. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP) 2021. https://dl.acm.org/doi/10.1145/3437801.3441615

Contracts, Grants and Sponsored Research

Grant - Research

• Shu, T. (Principal), "CAREER: A Time-Sensitive Large Model Training Platform for Dynamic Data Analytics," sponsored by National Science Foundation, Federal, $549999 Funded. (2025 - 2030).
• Shu, T. (Principal), "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure," sponsored by National Science Foundation, Federal, $201262 Funded. (2022 - 2025).