Tianru Zhang
Postdoctoral position at Department of Information Technology; Division of Systems and Control
- Telephone:
- +46 18 471 61 68
- E-mail:
- tianru.zhang@it.uu.se
- Visiting address:
- Hus 10, Regementsvägen 10
- Postal address:
- Box 524
751 20 UPPSALA
- ORCID:
- 0000-0001-9983-3755
Short presentation
Tianru Zhang is a Postdoc in Department of Information Technology at Uppsala University, working on Multimodel LLM, AI empowered Data Management, and Probabilistic Machine Learning.
Keywords
- Data management
- Multimodel AI
- Federated Learning
Biography
Dr. Tianru Zhang is a Postdoctoral Researcher in Department of Information Technology at Uppsala University. He holds a PhD in scientific computing from Uppsala University. Previously, he received his MSc degree in Statistics and Data Science from ENSAI (National school of statistics and information analysis of France), and his BSc degree in Mathematics from University of Science and Technology of China (USTC). Before joining Uppsala University, he also worked as an assistant researcher in Fujitsu R&D center, Beijing. Zhang is interested in research field including Data Management, Federated Machine Learning, and Multimodel LLMs.

Publications
Selection of publications
-
Intelligent Data Management via Machine Learning: From Storage Hierarchy to Information Hierarchy
2025
-
Accelerating Fair Federated Learning: Adaptive Federated Adam
Part of IEEE Transactions on Machine Learning in Communications and Networking, p. 1017-1032, 2024
- DOI for Accelerating Fair Federated Learning: Adaptive Federated Adam
- Download full text (pdf) of Accelerating Fair Federated Learning: Adaptive Federated Adam
-
Autonomous Hierarchical Storage Management via Reinforcement Learning
2024
-
Efficient Hierarchical Storage Management Empowered by Reinforcement Learning
Part of IEEE Transactions on Knowledge and Data Engineering, p. 5780-5793, 2023
Recent publications
-
Intelligent Data Management via Machine Learning: From Storage Hierarchy to Information Hierarchy
2025
-
Accelerating Fair Federated Learning: Adaptive Federated Adam
Part of IEEE Transactions on Machine Learning in Communications and Networking, p. 1017-1032, 2024
- DOI for Accelerating Fair Federated Learning: Adaptive Federated Adam
- Download full text (pdf) of Accelerating Fair Federated Learning: Adaptive Federated Adam
-
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Part of 2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI), p. 158-169, 2024
-
Autonomous Hierarchical Storage Management via Reinforcement Learning
2024
-
Part of Expert systems with applications, 2024
- DOI for Data management of scientific applications in a reinforcement learning-based hierarchical storage system
- Download full text (pdf) of Data management of scientific applications in a reinforcement learning-based hierarchical storage system
All publications
Articles in journal
-
Accelerating Fair Federated Learning: Adaptive Federated Adam
Part of IEEE Transactions on Machine Learning in Communications and Networking, p. 1017-1032, 2024
- DOI for Accelerating Fair Federated Learning: Adaptive Federated Adam
- Download full text (pdf) of Accelerating Fair Federated Learning: Adaptive Federated Adam
-
Part of Expert systems with applications, 2024
- DOI for Data management of scientific applications in a reinforcement learning-based hierarchical storage system
- Download full text (pdf) of Data management of scientific applications in a reinforcement learning-based hierarchical storage system
-
Efficient Hierarchical Storage Management Empowered by Reinforcement Learning
Part of IEEE Transactions on Knowledge and Data Engineering, p. 5780-5793, 2023
Comprehensive doctoral thesis
Conference papers
-
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Part of 2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI), p. 158-169, 2024
-
Autonomous Hierarchical Storage Management via Reinforcement Learning
2024
-
Demo Abstract: Blades: A Unified Benchmark Suite for Byzantine-Resilient in Federated Learning
Part of 9TH ACM/IEEE CONFERENCE ON INTERNET OF THINGS DESIGN AND IMPLEMENTATION, IOTDI 2024, p. 229-230, 2024
-
Efficient Hierarchical Storage Management Empowered by Reinforcement Learning Extended Abstract
p. 3869-3870, 2023