Zhendong Liu is a candidate for a bachelor of engineering (B.Eng.) degree at Shenzhen Technology University (expected to graduate in July 2026). His major research interests include improving model deployment and enhancing robustness of deep learning models across diverse data distributions. He focuses on developing efficient algorithms to improve both accuracy and computational efficiency of machine learning models in real-world applications.
Jiarong Liao is a fourth-year undergraduate majoring in Data Science and Big Data Technology at Shenzhen Technology University. His research focuses on optimizing the underlying architecture of software systems. Specifically, he works on developing innovative methods to enhance the efficiency, scalability, and reliability of software frameworks, ensuring robust performance in handling large-scale data processing and complex computational tasks. By improving the foundational structure, his work aims to streamline software development and deployment processes while maximizing system resilience and adaptability in dynamic environments.
Chuyang Ye is currently a senior undergraduate student in the Data Science and Big Data Technology program at Shenzhen Technology University. His research centers on enhancing model inference and generalization, with a particular emphasis on creating optimized algorithms that expedite the deployment of deep learning models while ensuring their robustness across varied real-world data scenarios.
Dongyan Wei will obtain a B.E. degree in Data Science and Big Data Technology from Shenzhen Technology University, Shenzhen, China, in 2025. His academic journey has been marked by a deep interest in model inference optimization, a pivotal aspect of ensuring that machine learning models are not only accurate but also efficient in their application.
Tingting Zhang received her Ph.D. degree from the University of Alberta, Alberta, Canada, in 2024. She is currently a postdoctoral fellow at McGill University, Quebec, Canada. Her research interests include new computing architectures, approximate computing, Ising computing, combinatorial optimization, and nanoelectronic circuits and systems. She was a recipient of the Best Paper Award Candidate at the Design, Automation and Test in Europe Conference (DATE) 2022. She served as the session chair for the IEEE International Conference on Nanotechnology (IEEENANO) 2024 and a Technical Program Committee Member for the International Conference on Computer-Aided Design (ICCAD) 2025.
Xianghua Fu received a Ph.D. degree in Computer Science and Technology from Xi’an Jiaotong University, Shanxi, China. He is currently a professor of the College of Big Data and Internet, and vice dean at the College of Big Data and Internet. His research interests include natural language processing, information retrieval, machine learning, and data mining. He has presided over one item of the National Natural Science Foundation, several projects of Shenzhen Basic Research and the Natural Science Foundation of Guangdong Province, and participated in many projects, such as the National Natural Science Foundation Project and the National Support Program. He has published more than 80 articles in many important journals and international conferences at home and abroad. He has also completed two provincial-level quality courses and received the first prize for provincial teaching achievements.
Jingyan Jiang received her Ph.D. degree in 2020 from Jilin University, China, specializing in Computer Science. After completing her doctoral studies, she served as a postdoctoral research fellow at the Tsinghua University Shenzhen Graduate School from 2020 to 2022.
She is currently an assistant professor at the Shenzhen Technology University, Shenzhen, China. Her research interests mainly lie in the areas of edge intelligence and federated learning.

*Xianghua Fu (fuxianghua@sztu.edu.cn)
*Jingyan Jiang (jiangjingyan@sztu.edu.cn)