Boyu Wang | Cybersecurity | Best Researcher Award

Dr. Boyu Wang | Cybersecurity | Best Researcher Award

Assistant professor at Beijing University of Civil Engineering and Architecture, China

Dr. Boyu Wang is a Principal Data Scientist at Tacoma Public Utilities in Washington, USA, where he leads energy and peak forecasting, financial modeling, and power operations. He holds a Ph.D. in Electrical Engineering from Louisiana State University, as well as Master’s degrees in Electrical Engineering and Computer Science. Dr. Wang has extensive experience in applying advanced data science techniques, including deep learning and blockchain, to optimize energy management systems. His research focuses on power flow prediction, decentralized micro-grids, and grid stability, and he has contributed to several publications and patents. He is committed to enhancing energy stability and resiliency through innovative data-driven solutions.

 

Education

Dr. Boyu Wang holds a Ph.D. in Electrical Engineering from Louisiana State University, Baton Rouge, LA, USA, which he completed from August 2014 to August 2018. Prior to his doctoral studies, he earned a Master of Science in Electrical Engineering from the same institution, Louisiana State University, during the period from August 2012 to August 2014. In addition to his background in electrical engineering, Dr. Wang expanded his expertise by obtaining a Master of Science in Computer Science from the Georgia Institute of Technology, where he studied from January 2020 to May 2022. His multidisciplinary academic training provides a solid foundation for his research and contributions in energy systems, machine learning, and data science.

 Experience

Dr. Boyu Wang currently serves as a Principal Data Scientist at Tacoma Public Utilities in Washington, USA, where he leads the annual energy and peak forecasting for resource planning, financial modeling, and power operations. His responsibilities include supporting various teams with data collection, cleaning, and manipulation, as well as developing risk models and automation tools to improve reporting efficiency. Dr. Wang has played a key role in assisting with project management strategies and energy stability efforts. Prior to this, he worked as a Power Engineer Intern at Entergy, where he conducted analysis on renewable energy integration into distribution systems. Throughout his career, Dr. Wang has leveraged his expertise in deep learning, blockchain, and data science to contribute to various innovative research projects, particularly in energy management and grid stability.

Research Interests

Dr. Boyu Wang’s research interests lie at the intersection of energy systems, machine learning, and advanced technologies. His work primarily focuses on applying deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM) models, to predict power flow and optimize grid stability in real-time. Additionally, Dr. Wang has explored blockchain-based energy management for decentralized micro-grids, developing dynamic pricing strategies and decision-making algorithms to enhance energy distribution and trading. He is also passionate about developing novel methods for power grid stability, including multi-layer constrained spectral clustering for post-contingency problems and dynamic programming-based control systems for micro-grids. His research aims to advance the resilience and efficiency of energy systems through the integration of cutting-edge computational techniques.

Skills

Dr. Boyu Wang possesses a robust skill set in programming, data analysis, and energy systems optimization. He is proficient in Python, R, and SQL, which he utilizes for data manipulation, analysis, and model development. Dr. Wang is also experienced with platforms and software such as Tableau, DBeaver, Snowflake, and Databricks, enabling him to work efficiently with large datasets and develop impactful visualizations and analytics solutions. His technical expertise extends to deep learning, where he has applied convolutional neural networks (CNN) and long short-term memory (LSTM) models for power flow prediction, as well as blockchain technology for decentralized energy management in micro-grids. These skills, combined with his background in electrical and computer engineering, allow him to tackle complex challenges in energy systems and grid stability.

 

Publication

Cybersecurity Enhancement of Power Trading within the Networked Microgrids Based on Blockchain and Directed Acyclic Graph Approach

  • Authors: B. Wang, M. Dabbaghjamanesh, A. Kavousi-Fard, S. Mehraeen
  • Journal: IEEE Transactions on Industry Applications
  • Volume: 55, Issue 6, Pages 7300-7309
  • Publication Year: 2019
  • Cited by: 188

A Novel Two-Stage Multi-Layer Constrained Spectral Clustering Strategy for Intentional Islanding of Power Grids

  • Authors: M. Dabbaghjamanesh, B. Wang, A. Kavousi-Fard, S. Mehraeen, …
  • Journal: IEEE Transactions on Power Delivery
  • Volume: 35, Issue 2, Pages 560-570
  • Publication Year: 2019
  • Cited by: 70

Blockchain-Based Stochastic Energy Management of Interconnected Microgrids Considering Incentive Price

  • Authors: M. Dabbaghjamanesh, B. Wang, A. Kavousi-Fard, N.D. Hatziargyriou, …
  • Journal: IEEE Transactions on Control of Network Systems
  • Volume: 8, Issue 3, Pages 1201-1211
  • Publication Year: 2021
  • Cited by: 43

Networked Microgrid Security and Privacy Enhancement by the Blockchain-Enabled Internet of Things Approach

  • Authors: M. Dabbaghjamanesh, B. Wang, S. Mehraeen, J. Zhang, A. Kavousi-Fard
  • Conference: 2019 IEEE Green Technologies Conference (GreenTech)
  • Pages: 1-5
  • Publication Year: 2019
  • Cited by: 37

Superconducting Fault Current Limiter Allocation in Reconfigurable Smart Grids

  • Authors: A.S. Abdollah Kavousi-Fard, Boyu Wang, Omid Avatefipour, Morteza …
  • Conference: IEEE, Berkley University Conference on Smart City and Smart Grid
  • Publication Year: 2019
  • Cited by: 28

Stability Improvement of Microgrids Using a Novel Reduced UPFC Structure via Nonlinear Optimal Control

  • Authors: H. Saberi, S. Mehraeen, B. Wang
  • Conference: 2018 IEEE Applied Power Electronics Conference and Exposition (APEC)
  • Pages: 3294-3300
  • Publication Year: 2018
  • Cited by: 17

Conclusion

Dr. Boyu Wang’s extensive work in energy systems, innovative applications of deep learning and blockchain technologies, and his leadership in power grid optimization make him an excellent candidate for the Research for Best Researcher Award. His research not only advances theoretical knowledge but also provides practical solutions for improving energy efficiency, grid stability, and resilience, aligning with the award’s recognition of impactful, cutting-edge research.

Yuxing Yang | Detection and Prevention | Best Researcher Award

Dr. Yuxing Yang | Detection and Prevention | Best Researcher Award

Postdoctoral at Xi’an Jiaotong-Liverpool University, China

Dr. Yuxing Yang is a dedicated researcher specializing in abnormal event detection, human action recognition, and multi-feature detection frameworks. He is currently pursuing his PhD in Engineering at Newcastle University, UK, focusing on developing novel frameworks for detecting abnormal events in video. Dr. Yang holds a Master’s degree in Electrical and Electronics Engineering from Newcastle University and a Bachelor’s degree in the same field from the University of Liverpool. His research experience spans signal processing, machine learning, and deep learning, with numerous publications in top-tier journals and conferences. Dr. Yang has also served as a Teaching Assistant and Research Assistant, mentoring students and contributing to key projects in multimodal security and video surveillance. His technical expertise, coupled with his leadership in academic and social initiatives, has earned him several accolades, including recognition for outstanding research presentations.

Education:

Dr. Yuxing Yang has an extensive academic background in electrical and electronics engineering. He is currently pursuing a PhD in Engineering at Newcastle University, UK, where his research focuses on developing novel frameworks for multi-feature abnormal event detection in video. His PhD work addresses complex challenges in video anomaly detection, employing advanced signal processing and information fusion techniques under the supervision of Dr. Syed Mohsen Naqvi. Dr. Yang also holds a Master of Electrical and Electronics Engineering degree from Newcastle University, where he worked on a dissertation titled “PHD Filter for Multiple Human Tracking.” During his Master’s program, he studied modules such as Signal Processing, Modulation and Coding, Internet of Things, and Wireless Network Technologies. Dr. Yang earned his Bachelor’s degree in Electrical and Electronics Engineering from the University of Liverpool, where his dissertation explored the use of fluorescence for monitoring water quality. His academic journey has equipped him with deep expertise in embedded systems, digital and wireless communications, and engineering management.

Professional Experience:

Dr. Yuxing Yang has gained valuable professional experience through his research and teaching roles at Newcastle University, UK. From 2018 to 2023, he served as an MSc Research Assistant with the Intelligent Sensing and Communications (ISC) research group. In this role, he guided MSc students on projects related to signal processing, machine learning, and video anomaly detection. He played a crucial role in advising students, refining their research findings, and supporting the presentation of their work. Additionally, Dr. Yang worked as a Research Assistant on the 2021 EPSRC IAA project titled “Multimodal Human Security,” where he contributed to enhancing detection accuracy in security surveillance using multimodal data. His work on this project led to a publication at the IEEE International Conference on Information Fusion. Furthermore, Dr. Yang held a position as a Teaching Assistant and Lab Demonstrator at Newcastle University from 2018 to 2022, where he taught and mentored undergraduate and master’s students, providing instruction on theoretical concepts, lab equipment, and experiment conduction, while also assisting with grading and course evaluations.

Research Interests:

Dr. Yuxing Yang’s research interests lie at the intersection of video anomaly detection and human behavior analysis, with a particular focus on advanced techniques in machine learning and signal processing. His work includes multiple human tracking, image segmentation, human action recognition, and multi-feature abnormal event detection in video. Dr. Yang is passionate about developing novel frameworks that enhance the accuracy and efficiency of video surveillance systems, especially in detecting abnormal events in complex environments. His research contributes to advancements in security systems, where multi-modal data fusion and real-time anomaly detection are critical for improving public safety and monitoring.

Skills:

Dr. Yuxing Yang possesses a diverse skill set, with expertise in programming languages and tools critical to his research in video anomaly detection and signal processing. He is proficient in Python, particularly in machine learning frameworks such as Keras, TensorFlow, and PyTorch. Additionally, Dr. Yang is skilled in MATLAB, which he uses extensively for algorithm development and data analysis. His experience with Latex allows him to efficiently prepare academic papers, while his knowledge of Linux Basics ensures a strong foundation in system operations and scripting. These technical skills enable him to develop and implement advanced algorithms for human-related abnormal event detection and security surveillance.

Conclusion:

Dr. Yuxing Yang’s solid academic background, innovative research in abnormal event detection, and extensive teaching experience, coupled with his leadership and contributions to community awareness, make him a suitable and competitive candidate for the Best Researcher Award. His dedication to advancing knowledge in video surveillance and anomaly detection is evident through his numerous publications, research contributions, and awards.

Publication Top Noted:

Abnormal event detection for video surveillance using an enhanced two-stream fusion method

  • Authors: Y. Yang, Z. Fu, S. M. Naqvi
  • Journal: Neurocomputing
  • Year: 2023
  • Volume: 553, Article 126561
  • Cited by: 15
  • DOI: 10.1016/j.neucom.2023.126561

Enhanced adversarial learning-based video anomaly detection with object confidence and position

  • Authors: Y. Yang, Z. Fu, S. M. Naqvi
  • Conference: 2019 13th International Conference on Signal Processing and Communication
  • Year: 2019
  • Cited by: 14
  • DOI: 10.1109/icspcc46631.2019.8962857

A two-stream information fusion approach to abnormal event detection in video

  • Authors: Y. Yang, Z. Fu, S. M. Naqvi
  • Conference: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year: 2022
  • Cited by: 13
  • DOI: 10.1109/icassp43922.2022.9746515

Pose-driven human activity anomaly detection in a CCTV-like environment

  • Authors: Y. Yang, F. Angelini, S. M. Naqvi
  • Journal: IET Image Processing
  • Year: 2023
  • Volume: 17, Issue 3, Pages 674-686
  • Cited by: 10
  • DOI: 10.1049/ipr2.12876

Video anomaly detection for surveillance based on effective frame area

  • Authors: Y. Yang, Y. Xian, Z. Fu, S. M. Naqvi
  • Conference: 2021 IEEE 24th International Conference on Information Fusion (FUSION)
  • Year: 2021
  • Cited by: 8
  • DOI: 10.23919/fusion49465.2021.9626892

Skeleton-based fall events classification with data fusion

  • Authors: L. Xie, Y. Yang, F. Zeyu, S. M. Naqvi
  • Conference: 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
  • Year: 2021
  • Cited by: 5
  • DOI: 10.1109/MFI52462.2021.9604344

One-shot medical action recognition with a cross-attention mechanism and dynamic time warping

  • Authors: L. Xie, Y. Yang, Z. Fu, S. M. Naqvi
  • Conference: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year: 2023
  • Cited by: 4
  • DOI: 10.1109/icassp49357.2023.10095933

Action-based ADHD diagnosis in video

  • Authors: Y. Li, Y. Yang, S. M. Naqvi
  • Platform: arXiv preprint
  • Year: 2024
  • Cited by: 2
  • DOI: arXiv:2409.02261

Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data

  • Authors: L. Xie, Y. Yang, Z. Fu, S. M. Naqvi
  • Platform: arXiv preprint
  • Year: 2023
  • Cited by: 1
  • DOI: arXiv:2309.15635