Jinpeng Chen | recommendation systems | Best Researcher Award

Assoc Prof Dr. Jinpeng Chen | recommendation systems | Best Researcher Award

Beijing University of Posts & Telecommunications | China

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Biography of Assoc Prof Dr. Jinpeng Chen πŸ‘¨β€πŸ’»

πŸŽ“ Education & Global Experience

Assoc. Prof. Dr. Jinpeng Chen began his academic journey with a Ph.D. in Computer Science from Beihang University (2011–2016). During his doctoral studies, he expanded his research horizons internationallyβ€”spending a year as a Visiting Ph.D. scholar at Aalborg University in Denmark (2014–2015), and also working as a Research Assistant at the University of Sydney in Australia (2013–2014). These global experiences laid the foundation for his collaborative and cross-cultural approach to research. πŸŒπŸ“˜

πŸ‘¨β€πŸ« Academic Positions

Dr. Chen currently serves as an Associate Professor at the Beijing University of Posts and Telecommunications (BUPT), a position he has held since December 2018. Prior to that, he was an Assistant Professor at BUPT from 2016 to 2018. Over the years, he has been actively involved in teaching, mentoring graduate students, and leading innovative research projects. πŸ«πŸ”¬

πŸ”¬ Research Interests & Projects

His research primarily focuses on Data Mining, Social Network Analysis, Crowdsourcing-based Data Processing, Machine Learning, and Artificial Intelligence. Dr. Chen has led and contributed to numerous high-impact research projects, including the Beijing Natural Science Foundation (2023–2026) and multiple grants from the National Natural Science Foundation of China. He also participated in the National Key R&D Program of China, contributing to the development of algorithms for user profiling, recommendations, traffic analysis, and intelligent marketing. πŸ“ŠπŸ€–

🧠 Academic Service & Community Contribution

Dr. Chen is a dedicated member of the global research community. He has served as a reviewer for prestigious journals such as ACM TOIS, TKDD, IEEE TNNLS, IEEE TFS, and TCC, among others. He is also a frequent external reviewer and program committee member for leading conferences like KDD, WWW, DASFAA, IJCAI, and BigData. His expert insights and evaluations have helped maintain the high standards of academic publishing. πŸ“πŸŒ

🎀 Talks & Presentations

As a recognized thought leader in his field, Dr. Chen has been invited to speak at major academic events. Notable presentations include his talk at the KDD China 2023 Summer School on “Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding”, and his keynote at ICAIBD 2023 on “Sequential Intention-aware Recommender based on User Interaction Graph”. He has also shared his research at CGCM 2014 and during a special invitation to the School of Information Technologies at the University of Sydney. πŸ—£οΈπŸ“’

πŸš€ Impact & Vision

Assoc. Prof. Dr. Jinpeng Chen continues to drive innovation at the intersection of data science and artificial intelligence. His research not only contributes to academic advancement but also influences real-world applications in technology, business, and social platforms. With a passion for AI and a strong foundation in collaborative research, he is shaping the future of intelligent systems, one algorithm at a time. πŸŒŸπŸ“ˆ

πŸ“š Top Notes Publications

Title: Automatic tagging by leveraging visual and annotated features in social media

Authors: J. Chen, P. Ying, X. Fu, X. Luo, H. Guan, K. Wei
Journal: IEEE Transactions on Multimedia
Year: 2021

Title: Inferring tag co-occurrence relationship across heterogeneous social networks

Authors: J. Chen, Y. Liu, G. Yang, M. Zou
Journal: Applied Soft Computing
Year: 2018

Title: Sequential intention-aware recommender based on user interaction graph

Authors: J. Chen, Y. Cao, F. Zhang, P. Sun, K. Wei
Journal: Proceedings of the 2022 International Conference on Multimedia Retrieval
Year: 2022

Title: From tie strength to function: Home location estimation in social network

Authors: J. Chen, Y. Liu, M. Zou
Journal: 2014 IEEE Computers, Communications and IT Applications Conference
Year: 2014

Title: FePN: A robust feature purification network to defend against adversarial examples

Authors: D. Cao, K. Wei, Y. Wu, J. Zhang, B. Feng, J. Chen
Journal: Computers & Security
Year: 2023

Yongzhi Qu | Scientific Machine Learning | Excellence in Innovation

Dr. Yongzhi Qu | Scientific Machine Learning | Excellence in Innovation

University of Utah | United States

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Biography of Dr. Yongzhi Qu

πŸ… Assistant Professor at University of Utah | Ph.D. in Industrial Engineering & Operations Research

Dr. Yongzhi Qu is an accomplished assistant professor at the University of Utah in the Department of Mechanical Engineering, specializing in AI-powered systems, data-driven dynamics, autonomous manufacturing, and digital twins. He earned his Ph.D. from the University of Illinois at Chicago in 2014, with a focus on Industrial Engineering & Operations Research. His research spans several fields, including machine learning, system modeling, and control for mechanical and structural systems.

πŸ“š Education

  • Ph.D. in Industrial Engineering & Operations Research – University of Illinois at Chicago (2014)
  • M.S. in Measurement & Testing Technology – Wuhan University of Technology (2011)
  • B.Sc. in Measurement & Control Instrumentation and Technology – Wuhan University of Technology (2008)

πŸ’Ό Professional Experience

  • Assistant Professor (07/2023 – Present)
    Department of Mechanical Engineering, University of Utah
  • Assistant Professor (08/2019 – 06/2023)
    Department of Mechanical & Industrial Engineering, University of Minnesota Duluth
  • Assistant/Associate Professor (01/2015 – 07/2019)
    Department of Mechanical Engineering, Wuhan University of Technology
  • Application Engineer (12/2013 – 12/2014)
    The DEI Group, Millersville, Maryland, US

🧠 Research Interests ON Scientific Machine Learning

Dr. Qu’s research focuses on scientific machine learning, AI-powered system modeling, estimation, and control for dynamic systems, with applications in autonomous manufacturing and digital twins. His recent work explores the intersection of machine learning, physics, and mathematics to model and control complex systems.

πŸ† Research Grants

  • A Neural Differential Machine Learning Framework with Nonlinear Physics
    National Institute of Standards and Technology (NIST), $121,015 (2023-2026)
  • Real-time System Identification for Machining Spindles
    NIST, $159,950 (2020-2022)
  • Learning Real-time Dynamics of a Rotor System
    University of Minnesota, $44,501 (2020-2021)

πŸ… Academic Awards

  • Best Academic Paper Award (IEEE International Conference on Prognostics and Health Management, 2013)
  • Best Student Paper Award (Society for Machinery Failure Prevention Technology Conference, 2014)
  • Best Paper Award (Prognostics and System Health Monitoring Conference, 2018)

πŸ“’ Invited Talks

  • Machine Learning for Dynamic System Modeling, Seagate (2022)
  • Keynote on Deep Learning in PHM, Annual Conference of PHM Society (2019)
  • FBG Sensing for Machinery Health Monitoring, Northeastern University, China (2016)

πŸŽ“ Teaching

  • Machine Learning for System Dynamics and Control, University of Minnesota Duluth
  • Six Sigma and Quality Control, University of Minnesota Duluth
  • Control Engineering, Wuhan University of Technology

🀝 Professional Service

  • Organizing Chair, Data Challenge, 15th Annual Conference of PHM Society (2023)
  • Panelist, Doctoral Symposium, 14th Annual Conference of PHM Society (2022)
  • Symposium Chair, ASME Manufacturing Science and Engineering Conference (2022, 2023)

πŸ“š TOP NOTES PUBLICATIONSΒ 

State space neural network with nonlinear physics for mechanical system modeling
    • Authors: Reese Eischens, Tao Li, Gregory W. Vogl, Yi Cai, Yongzhi Qu
    • Journal: Reliability Engineering & System Safety
    • Year: 2025
    • DOI: 10.1016/j.ress.2025.110946
Graph neural network architecture search for rotating machinery fault diagnosis based on reinforcement learning
    • Authors: Jialin Li, Xuan Cao, Renxiang Chen, Xia Zhang, Xianzhen Huang, Yongzhi Qu
    • Journal: Mechanical Systems and Signal Processing
    • Year: 2023
    • DOI: 10.1016/j.ymssp.2023.110701
Development of Deep Residual Neural Networks for Gear Pitting Fault Diagnosis Using Bayesian Optimization
    • Authors: Jialin Li, Renxiang Chen, Xianzhen Huang, Yongzhi Qu
    • Journal: IEEE Transactions on Instrumentation and Measurement
    • Year: 2022
    • DOI: 10.1109/TIM.2022.3219476
A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network
    • Authors: Jialin Li, Xueyi Li, David He, Yongzhi Qu
    • Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
    • Year: 2020
    • DOI: 10.1177/1748006X19867776
Gear pitting fault diagnosis using disentangled features from unsupervised deep learning
    • Authors: Yongzhi Qu, Yue Zhang, Miao He, David He, Chen Jiao, Zude Zhou
    • Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
    • Year: 2019
    • DOI: 10.1177/1748006X18822447