Chaoli Zhang | Computer Science | Best Researcher Award

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Assist. Prof. Dr ChaoliZhang | Computer Science | Best Researcher Award

Lecturer at Zhejiang Normal University, China

Dr. Chaoli Zhang is a Lecturer at the College of Computer Science and Technology, Zhejiang Normal University . He received his Ph.D. in Computer Science and Technology from Shanghai Jiao Tong University  and has previously worked at Alibaba DAMO Academy as a Senior Engineer . With deep expertise in time series anomaly detection, intelligent systems, and wireless data center networks , he has authored several influential papers in top-tier conferences and journals like IEEE ToN, KDD, and CIKM . He holds multiple patents in AI-driven fault detection and data analysis . Known for blending academic excellence with industrial innovation , he actively contributes to national and provincial-level research projects. His work has earned him prestigious recognitions, including a championship in a global 5G fault localization challenge . Dr. Zhang continues to push the boundaries of AI applications in realworld intelligent systems .

🔹Professional Profile

GOOGLE SCHOLAR

🎓 Education & Experience

Dr. Zhang obtained his bachelor’s degree in Information Security and Law from Nankai University (2011–2015)  and earned his Ph.D. from Shanghai Jiao Tong University (2015–2020) in Computer Science and Technology . After completing his doctorate, he worked from 2020 to 2023 at the Machine Intelligence Lab of Alibaba DAMO Academy , where he led advanced AI projects related to anomaly detection and intelligent monitoring . Since January 2024, he has served as a Lecturer at Zhejiang Normal University, where he continues research in AI and teaches advanced computing topics . His education blends theoretical depth with multidisciplinary training, while his work experience bridges top-tier academia and cutting-edge industry R&D . This combination allows him to explore highly applied, intelligent systems with real-world impact .

📈 Professional Development

Dr. Zhang has demonstrated rapid professional growth through impactful roles in both academia and industry . At Alibaba DAMO Academy, he focused on intelligent systems for real-time anomaly detection in large-scale infrastructure . He has since transitioned into academia, taking a faculty role at Zhejiang Normal University where he now leads funded research projects on smart healthcare analytics and IoT anomaly diagnostics . His professional development is characterized by an emphasis on translational research—converting algorithms into deployable solutions for real-world systems . As a project leader, he has secured competitive funding from the Zhejiang Natural Science Foundation and municipal science programs . Dr. Zhang regularly presents at global conferences (e.g., KDD, CIKM), reflecting his active engagement with the international research community . With a strong portfolio of publications, patents, and leadership, his professional path exemplifies AI-driven innovation and academic-industrial synergy .

🧠 Research Focus

Dr. Chaoli Zhang’s research interests lie at the intersection of time series anomaly detection, intelligent computing, and wireless data center networks . He develops novel algorithms for fault root cause analysis, time-frequency decomposition, and multivariate data analysis . His work on models like TFAD and DCdetector introduces advanced methods combining attention mechanisms, contrastive learning, and decomposition techniques for real-time monitoring . His recent projects also explore heterogeneous IoT anomaly detection and healthcare time series analysis, contributing to the development of robust, interpretable, and scalable AI systems . These innovations support applications in smart cities, cloud platforms, and industrial diagnostics ⚙️. With a foundation in graph modeling and deep learning, Dr. Zhang’s research aims to enhance system resilience, operational intelligence, and automation reliability across complex environments .

🏅 Awards & Honors

Dr. Zhang has earned several notable awards that reflect the excellence and impact of his research work . He was the champion of the 2022 SP Grand Challenge on 5G network fault root cause localization, prevailing over 338 global teams . His practical AI deployment solutions earned him the AAAI/IAAI’23 Deployed Application Innovation Award, one of only 10 globally recognized projects that year . He holds multiple Chinese patents related to time series analysis and cloud-based diagnostic methods 🔬, underscoring his ability to translate theory into tangible technological advances. His papers have been featured in leading journals and conferences, where he served as first or co-first author (IEEE ToN, CIKM, KDD, TCS) . These accolades highlight his cross-domain innovation, commitment to real-world impact, and leadership in the intelligent systems community .

🔹Publication of Top Notes

1.Transformers in Time Series: A Survey

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2023
Citations: 1328

2.DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Authors: Y. Yang, C. Zhang, T. Zhou, Q. Wen, L. Sun
Year: 2023
Citations: 225

3.Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Authors: K. Zhang, Q. Wen, C. Zhang, R. Cai, M. Jin, Y. Liu, J.Y. Zhang, Y. Liang, …
Year: 2024
Citations: 222

4.Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

Authors: M. Jin, Q. Wen, Y. Liang, C. Zhang, S. Xue, X. Wang, J. Zhang, Y. Wang, …
Year: 2023
Citations: 166

5. Large Language Models for Education: A Survey and Outlook

Authors: S. Wang, T. Xu, H. Li, C. Zhang, J. Liang, J. Tang, P.S. Yu, Q. Wen
Year: 2024
Citations: 146

6.TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

Authors: C. Zhang, T. Zhou, Q. Wen, L. Sun
Year: 2022
Citations: 106

7.A Survey on Diffusion Models for Time Series and Spatio-Temporal Data

Authors: Y. Yang, M. Jin, H. Wen, C. Zhang, Y. Liang, L. Ma, Y. Wang, C. Liu, B. Yang, …
Year: 2024
Citations: 76

8.LogiCoT: Logical Chain-of-Thought Instruction-Tuning

Authors: H. Liu, Z. Teng, L. Cui, C. Zhang, Q. Zhou, Y. Zhang
Year: 2023
Citations: 51

9. Transformers in Time Series: A Survey (arXiv version)

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2022
Citations: 45

10. Bringing Generative AI to Adaptive Learning in Education

Authors: H. Li, T. Xu, C. Zhang, E. Chen, J. Liang, X. Fan, H. Li, J. Tang, Q. Wen
Year: 2024
Citations: 43

11.Pricing and Allocation Algorithm Designs in Dynamic Ridesharing System

Authors: C. Zhang, J. Xie, F. Wu, X. Gao, G. Chen
Year: 2020
Citations: 35

12.Transformers in Time Series: A Survey (repeat entry, possibly updated citation)

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2023
Citations: 23

13.AHPA: Adaptive Horizontal Pod Autoscaling on Alibaba Cloud Kubernetes

Authors: Z. Zhou, C. Zhang, L. Ma, J. Gu, H. Qian, Q. Wen, L. Sun, P. Li, Z. Tang
Year: 2023
Citations: 22

14.Free Talk in the Air: A Hierarchical Topology for 60 GHz Wireless Data Center Networks

Authors: C. Zhang, F. Wu, X. Gao, G. Chen
Year: 2017
Citations: 19

15.Logical Reasoning in Large Language Models: A Survey

Authors: H. Liu, Z. Fu, M. Ding, R. Ning, C. Zhang, X. Liu, Y. Zhang
Year: 2025
Citations: 14

16.Online Auctions with Dynamic Costs for Ridesharing

Authors:C. Zhang, F. Wu, X. Gao, G. Chen
Year:2017
Citations:14

17.NetRCA: An Effective Network Fault Cause Localization Algorithm

Authors: C. Zhang, Z. Zhou, Y. Zhang, L. Yang, K. He, Q. Wen, L. Sun
Year: 2022
Citations: 13

📌 Conclusion 

Dr. Chaoli Zhang exemplifies the ideal recipient of the Best Researcher Award due to his proven research excellence, industry-validated innovations, and impactful contributions across multiple disciplines. His work seamlessly bridges the gap between theoretical advancements and real-world applications, particularly in artificial intelligence, anomaly detection, and time series analysis. With a strong publication record in top-tier journals and conferences, and recognized achievements such as the SP Grand Challenge 2022 and the AAAI/IAAI Innovation Award, Dr. Zhang has demonstrated both academic depth and practical relevance. His leadership in developing AI-driven solutions for complex, large-scale systems solidifies his standing as one of the top emerging voices in the field. These accomplishments collectively make him exceptionally worthy of recognition as a Best Researcher Award.

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

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Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof. Len Gelman , University of Huddersfield , United Kingdom

Professor Len Gelman 🇬🇧 is a globally recognized expert in signal processing and condition monitoring 🔍. He currently serves as Chair Professor and Director at the University of Huddersfield 🏫. With over two decades of academic leadership, he has significantly contributed to vibro-acoustics and non-destructive testing 🔧. A Fellow of multiple prestigious organizations 🌐, Prof. Gelman’s international collaborations span across Europe, Asia, and the USA 🌏. His innovations have advanced aerospace and medical diagnostics ✈️🧬. He continues to lead global initiatives and research committees, shaping the future of engineering diagnostics and reliability technologies 🔬🛠️.

Professional Profile

SCOPUS

Education and Experience 

Prof. Len Gelman holds a PhD and Doctor of Science (Habilitation) 🎓, with BSc (Hons) and MSc (Hons) degrees in engineering 📘. He is a British citizen 🇬🇧. Since 2017, he has been a Professor and Chair at the University of Huddersfield 🏛️. Prior to that, he served at Cranfield University (2002–2017) as Chair in Vibro-Acoustical Monitoring 🔊. His distinguished academic journey includes visiting professorships in China 🇨🇳, Denmark 🇩🇰, Poland 🇵🇱, Spain 🇪🇸, Italy 🇮🇹, and the USA 🇺🇸. Prof. Gelman combines deep technical expertise with global educational outreach 🌍👨‍🏫.

Professional Development 

Prof. Gelman has held key international leadership roles including Chair of the International Scientific Committee of the Condition Monitoring Society 🌐. He is a Fellow of BINDT, IAENG, IDE, and HEA 🎖️, and an Academician of the Academy of Sciences of Applied Radio Electronics 🧠. He chairs award and honors committees for top acoustics and vibration institutions 🏅. As Visiting Professor at Tsinghua, Jiao Tong, and Aalborg Universities, among others 🎓, he mentors emerging researchers globally 🌎. Prof. Gelman’s commitment to professional excellence shapes the advancement of diagnostic technologies and engineering education 📈🔧.

Research Focus 

Prof. Gelman’s research focuses on signal processing, vibro-acoustics, and condition monitoring of engineering systems 🔍🔊. His work spans non-destructive testing (NDT), fault diagnostics, and performance optimization in sectors such as aerospace, healthcare, and manufacturing ✈️🏥🏭. He develops advanced algorithms for fault detection and predictive maintenance using machine learning and big data 🧠📊. His interdisciplinary approach benefits both industry and academia 🌐🔬. Prof. Gelman also pioneers applications in medical diagnostics and intelligent systems for real-time monitoring 🧬⚙️. His innovations contribute to safer, more efficient engineering systems across global platforms 🌍🚀.

Awards and Honors 

Prof. Gelman has received numerous prestigious awards for innovation and research excellence 🏅. These include the Rolls-Royce Innovation Award (2012, 2019) ✈️, William Sweet Smith Prize by IMechE 🛠️, and COMADIT Prize by BINDT for impactful contributions to condition monitoring 🧲. He also received Best Paper Awards at CM/MFPT conferences 📄 and recognition from the USA Navy and Acoustical Society of America 🇺🇸🔊. His European and UK fellowships support cutting-edge human capital projects 🧠🇪🇺. He has chaired international committees in NDT and acoustics, continuing to shape future technologies through global leadership and innovation 🌐👨‍🔬.

Publication Top Notes

1. Vibration Analysis of Rotating Porous Functionally Graded Material Beams Using Exact Formulation

  • Citation: Amoozgar, M.R., & Gelman, L.M. (2022). Vibration analysis of rotating porous functionally graded material beams using exact formulation. Journal of Vibration and Control, 28(21–22), 3195–3206. https://doi.org/10.1177/10775463211027883Nottingham Repository+1SAGE Journals+1

  • Summary: This study investigates the free vibration behavior of rotating functionally graded material (FGM) beams with porosity, employing geometrically exact fully intrinsic beam equations. The research considers both even and uneven porosity distributions to simulate manufacturing imperfections. Findings reveal that material gradation and porosity significantly influence natural frequencies and mode shapes, emphasizing the necessity of accounting for these factors in the design and analysis of rotating FGM structures. Huddersfield Research Portal+2SAGE Journals+2Nottingham Repository+2

2. Vibration Health Monitoring of Rolling Bearings Under Variable Speed Conditions by Novel Demodulation Technique

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2021). Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique. Structural Control and Health Monitoring, 28(2), e2672. https://doi.org/10.1002/stc.2672Wiley Online Library

  • Summary: Addressing the challenges of diagnosing rolling bearing faults under variable speed conditions, this paper introduces an optimization-based demodulation transform method. The technique effectively estimates fault characteristic frequencies with weak amplitudes and adapts to time-varying rotational speeds. Validation through simulations and experimental data demonstrates the method’s superior diagnostic capabilities compared to existing approaches. Huddersfield Research Portal+1Wiley Online Library+1

3. Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2020). Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions. Sensors, 20(18), 5201. https://doi.org/10.3390/s20185201MDPI

  • Summary: This research presents a novel approach for estimating instantaneous defect frequencies in rolling bearings operating under non-stationary conditions. Utilizing vibration sensor data, the method enhances the accuracy of defect frequency estimation, facilitating improved fault diagnosis in dynamic operational environments. MDPI

4. Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

  • Citation: Ciszewski, T., Gelman, L.M., & Ball, A.D. (2020). Novel fault identification for electromechanical systems via spectral technique and electrical data processing. Electronics, 9(10), 1560. https://doi.org/10.3390/electronics9101560MDPI

  • Summary: This paper introduces an innovative method for fault identification in electromechanical systems by integrating spectral analysis with electrical data processing. The approach enhances the detection and diagnosis of faults, contributing to the reliability and efficiency of electromechanical system operations. MDPI

5. Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

  • Citation: Kolbe, S., Gelman, L.M., & Ball, A.D. (2021). Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions. Sensors, 21(20), 6913. https://doi.org/10.3390/s21206913PMC

  • Summary: This study proposes two novel consistency vectors combined with machine learning algorithms to adapt spectral kurtosis technology for optimal gearbox damage diagnosis under varying operating conditions. The approach enables computationally efficient online condition monitoring by predicting diagnosis effectiveness, thereby improving maintenance strategies.

Conclusion

Professor Len Gelman exemplifies the ideal candidate for the Best Researcher Award due to his groundbreaking contributions to condition monitoring, signal processing, and diagnostic technologies. His work not only advances academic knowledge but also addresses critical industry challenges in aerospace, healthcare, and manufacturing. With a sustained record of high-impact research, international leadership, and technological innovation, he stands out as a world-class researcher whose work continues to benefit both academia and society.