Fangming Liu | Computer Science | Best Researcher Award

Published on by Innovation Technologist

Mr. Fangming Liu | Computer Science | Best Researcher Award

Professor at Huazhong University of Science & Technology | China

Fangming Liu is a Full Professor at the School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China, and is also affiliated with Peng Cheng Laboratory, Shenzhen. He is widely recognized for his pioneering contributions to cloud and edge computing, data centers, green computing, software-defined networking, and applied artificial intelligence, establishing himself as a leading figure in computer science and engineering research.

Publication Profile 

Scopus

Google Scholar

Educational Background 

He completed his undergraduate studies in Computer Science and Technology at Tsinghua University and later pursued his doctoral degree in Computer Science and Engineering at the Hong Kong University of Science and Technology, where he developed strong expertise in distributed systems and large-scale computing.

Professional Experience 

Fangming Liu began his career as a software engineer in Beijing, gaining practical industry exposure before moving into academic and research roles. He worked as a research assistant at the Chinese University of Hong Kong and further expanded his international research experience as a visiting scholar at the University of Toronto. He has been with Huazhong University of Science and Technology for over a decade, where he currently serves as a Full Professor, leading research and academic initiatives in computer science and technology.

Research Interests 

His research interests span across cloud computing, edge computing, green and sustainable data centers, networking and distributed systems, mobile cloud computing, peer-to-peer systems, and the integration of machine learning and artificial intelligence with modern network and computing infrastructures.

Awards and Honors 

Fangming Liu has been recognized with numerous prestigious awards and honors. He is a recipient of the National Natural Science Fund for Excellent Young Scholars and the National Program Special Support for Top-Notch Young Professionals. He has received the First Class Prize of Natural Science from the Ministry of Education in China and the Second Class Prize of the National Natural Science Award in China. Internationally, his research has earned Best Paper Awards at major conferences including IEEE/ACM IWQoS, ACM e-Energy, and IEEE GLOBECOM.

Research Skills 

He demonstrates advanced skills in distributed computing, intelligent system design, network optimization, and sustainable computing solutions. His ability to merge theoretical models with practical system implementations has contributed significantly to advancements in next-generation computing and communication infrastructures.

Publications 

Gearing Resource-Poor Mobile Devices With Powerful Clouds: Architectures, Challenges, And Applications

Citation: 469

Year: 2013

Managing performance overhead of virtual machines in cloud computing: A survey, state of the art, and future directions

Citation: 350

Year: 2013

A Framework for Truthful Online Auctions in Cloud Computing with Heterogeneous User Demands

Citation: 307

Year: 2013

iAware: Making live migration of virtual machines interference-aware in the cloud

Citation: 263

Year: 2013

Building a network highway for big data: architecture and challenges

Citation: 258

Year: 2014

Conclusion 

In conclusion, Fangming Liu is a highly accomplished academic and researcher whose career reflects excellence in both scholarship and innovation. His impactful work in cloud and edge computing, data centers, networking, and applied AI has not only advanced the field of computer science but also provided solutions with real-world applications, positioning him as a leader in his discipline with ongoing influence in global research and technology development.

 

 

 

Manas Ranjan Sethi | ECE | Best Researcher Award

Published on by Innovation Technologist

Mr. Manas Ranjan Sethi | ECE | Best Researcher Award

Research Scholar at NIT Silchar, India

Manas Ranjan Sethi is a dedicated academic professional currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar, with a strong focus on machine learning applications in fault diagnosis and energy systems. He holds an M.Tech in Electronics & Telecommunication from BPUT, Odisha and has over 12 years of teaching experience, having worked as an Assistant Professor at Gandhi Institute for Technology (GIFT) and as a Lecturer at Koustuv Institute of Self Domain, Bhubaneswar. His research interests include machine learning, signal processing, and sustainable energy systems, particularly in wind turbine diagnostics and emotion recognition using EEG signals. Manas has contributed to numerous journals, conferences, and book chapters, and he has earned distinctions such as qualifying CBSE-UGC NET and GATE. He is also skilled in technical tools, enjoys singing, and has a passion for reading.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Manas Ranjan Sethi is currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar (since March 2020). He holds an M.Tech in Electronics & Telecommunication (Specialization in Communication Engineering) from BPUT, Odisha (2012), with a CGPA of 8.20. He completed his B.E. in Electronics & Telecommunication from BPUT, Odisha, in 2006, securing a 62.19%. Additionally, he completed his +2 Science and Matriculation from MP Board, Madhya Pradesh.

💼 Professional Experience :

Manas has a rich academic career spanning over 12 years. He served as an Assistant Professor in the Electronics & Communication Engineering Department at Gandhi Institute for Technology (GIFT), Bhubaneswar, from November 2013 to March 2020. Prior to that, he worked as a Lecturer in the Electronics & Telecommunication Engineering Department at Koustuv Institute of Self Domain, Bhubaneswar, from July 2007 to November 2013. He has a strong foundation in teaching and mentoring students in the field of Electronics and Communication Engineering.

📚 Research Interests : 

Manas’s research interests lie in the domains of Machine Learning, Signal Processing, and Fault Diagnosis. His work focuses on vibration signal-based diagnostics and energy extraction using wind turbines. He is passionate about leveraging machine learning techniques for predictive maintenance and condition monitoring. His recent research includes the application of meta-classifiers for diagnosing wind turbine blade faults and exploring emotion recognition through EEG signals.

🏆Achievements & Certifications :

Manas has earned several academic distinctions, including qualifying the CBSE-UGC NET (Electronic Science) in July 2018, and securing GATE scores of 260 (2016) and 218 (2011) in Electronics and Communication. He has also attended and contributed to various seminars, workshops, and short-term courses in fields such as VLSI Design, Microwave Filters, and Adaptive Signal Processing.

📝 Publication Top Notes :

  • Sethi, M. R., Subba, A. B., Faisal, M., Sahoo, S., & Koteswara Raju, D. (2024). Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal. Engineering Applications of Artificial Intelligence, 138, 109372.
  • Sethi, M. R., Sahoo, S., Dhanraj, J. A., & Sugumaran, V. (2023). Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions for Improving Energy Extraction Using Machine Learning Approach. Smart and Sustainable Manufacturing Systems, 7(1), 14–40.
  • Chatterjee, S., Sethi, M. R., & Asad, M. W. A. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. European Journal of Operational Research, 248(2), 658–667.
  • Sethi, M. R., Parhi, S. S., Sahoo, S., Sugumaran, V., & Mohanty, S. R. (2023). Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study. Proceedings of the 2023 IEEE Region 10 Symposium, TENSYMP 2023.
  • Kar, P., Hazarika, J., & Sethi, M. R. (2023). A Comparative Study between Supervised and Unsupervised Techniques for Two Class Emotion Recognition using EEG. Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023.
  • Banala, H. S., Sahoo, S., Sethi, M. R., & Sharma, A. K. (2023). Fault Diagnosis in Wind Turbine Blades Using Machine Learning Techniques. In R. Doriya, B. Soni, A. Shukla, & X. Z. Gao (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences (Lecture Notes in Electrical Engineering, Vol. 946), 401–411. Springer, Singapore.
  • Sethi, M. R., Sahoo, S., Kanoongo, S., & Hemasudheer, B. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Rule Classifier. Proceedings of the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET 2022), 1–6. doi: 10.1109/ICEFEET51821.2022.9848401.
  • Sethi, M. R., Hemasudheer, B., Sahoo, S., & Kanoongo, S. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Vibration Data through META Classifiers. Proceedings of the 2022 4th International Conference on Energy, Power, and Environment (ICEPE 2022), 1–5. doi: 10.1109/ICEPE55035.2022.9798026.