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

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🎓 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.

Assist Prof Dr Jaya Singh Dhas L | Data Science | Best Researcher Award

Head of the Department at Scott Christian College (Autonomous),India

Dr. L. Jaya Singh Dhas is the Head of the Department of Computer Science at Scott Christian College (Autonomous), Nagercoil, Tamil Nadu, India. With over two decades of experience in academia, Dr. Dhas is a distinguished researcher and educator, specializing in areas like Artificial Intelligence, Machine Learning, Data Mining, and Cloud Computing. His work combines theoretical research with practical applications, particularly in the fields of clustering techniques, heart disease prediction, and network security. Dr. Dhas has contributed significantly to the academic community through his research publications, conference participation, and various professional development activities.

Profile

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Education 🎓

Ph.D. in Computer Science – Bharathidasan University, Tiruchirappalli (2022), First Class
M.Phil. in Computer Science – Alagappa University, Karaikudi (1998), First Class
M.C.A. (Master of Computer Applications) – Bharathidasan University, Tiruchirappalli (1996), First Class
B.Sc. in Computer Science – Madurai Kamaraj University, Madurai (1991), First Class

Dr. Dhas’ academic qualifications reflect his deep commitment to the field of computer science and his expertise in both foundational and advanced topics within the discipline.

Professional Experience 💼

Dr. Dhas joined Scott Christian College (Autonomous) in 1998, where he has served as the Head of the Department of Computer Science since then. With more than 20 years of teaching and leadership experience, Dr. Dhas has significantly influenced the department’s curriculum and research direction. He is dedicated to fostering academic growth and promoting innovative research among students and faculty.

Research Interests 🔬

Dr. Dhas’ primary research interests lie in Artificial Intelligence, Data Science, Clustering Techniques, Big Data Analytics, and Network Security. He has worked extensively on the following areas:

Clustering Techniques: Investigating different clustering algorithms for analyzing temporal relational data.
Heart Disease Prediction: Using machine learning techniques for early-stage heart disease prediction.
Network Intrusion Detection: Optimizing deep learning approaches for network security.
Big Data: Exploring synergetic filtering and neural network techniques for handling large datasets.

Awards & Honors 🏆

Dr. Dhas has received multiple recognitions for his outstanding contributions in research and education, including:

Indian Patent (2022) for “Monitoring E-Health Care System Using Artificial Intelligence Techniques”.
Member of the Internet Society and International Association of Engineers (IAENG), further reflecting his international recognition in the field.
Reviewer for several renowned journals, including International Journal of Information Technology and Decision Making (IJITDM) and Journal of Scientific Research and Reports (JSRR).

Achievements 🌟

Successfully published numerous papers in high-impact journals such as Expert Systems With Applications (Elsevier), International Journal of Engineering and Advanced Technology (IJEAT), and Indian Journal of Natural Sciences (IJONS).
Served as a reviewer for several prestigious international journals and conferences, contributing to the academic community’s growth.
Authored multiple book chapters in edited volumes on topics like data clustering and artificial intelligence, further establishing his expertise.

Upcoming Projects 🚀

Dr. Dhas is currently engaged in projects related to AI-driven healthcare systems, particularly focusing on AI in early disease detection.
He is also exploring the use of neural networks and big data analytics to tackle contemporary challenges in network security and data privacy.

Publications 📚

“Hybrid Fast Correlation-based Feature Selection with Improved Weighed Particle Swarm Optimization to Predict and Classify Heart Disease at an Early Stage”, Indian Journal of Natural Sciences (IJONS), Vol. 15, Issue 85, August 2024, Pages 76542 – 76550.
“Network Intrusion Detection: An Optimized Deep Learning Approach Using Big Data Analytics”, Expert Systems With Applications, Elsevier, Volume 251, 1 October 2024, 123919.
“Kulczynski Similarity Index Feature Selection based Map Estimated Rocchio Classification for Brain Tumor Disease Diagnosis”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), December 2023.
“Identification of Clustering Techniques with Temporal Relational Data Points”, International Journal of Interdisciplinary Global Studies (IJIGS), Volume 14, Issue 04, Oct-Dec’ 2020.
“Efficient Synergetic Filtering in Big Dataset using Neural Network Technique”, International Journal of Recent Technology and Engineering (IJRTE), Volume 8, Issue 5, January 2020, Pages 1349 – 1360.