Yanwei Fu | Computer Science | Best Researcher Award

Yanwei Fu | Computer Science | Best Researcher Award

Dr Yanwei Fu ,Fudan University ,China

Based on the comprehensive profile provided, Prof. Dr. Yanwei Fu is indeed a highly qualified candidate for the Best Researcher Award. His achievements, research contributions, and academic standing illustrate his significant impact on the field of computer science, particularly in machine learning and computer vision.

Publication profile

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Educational Background

Prof. Fu holds a Ph.D. in Computer Science with a specialization in Computer Vision from Queen Mary University of London (2011-2014). His earlier education includes a Master’s degree from Nanjing University and a Bachelor’s degree from Nanjing University of Technology. This solid foundation in computer science has equipped him with the skills necessary for cutting-edge research.

Awards and Recognitions

Prof. Fu’s accolades demonstrate his prominence in the academic community:

  • DECRA Fellow by the Australian Research Council (2016)
  • Distinguished Professor of Eastern Scholar at Shanghai Institutions of Higher Learning (2017)
  • 1000 Young Innovative Talent Professional Fellow by NSFC (2018)
  • Winner of the Best Paper Award at the IEEE International Conference on Multimedia and Expo (ICME) (2019)
  • Recipient of the ACM China SIGAI Rising Star Award (2018) and ACM Shanghai Rising Star Award (2019)
  • Fellow of the British Computer Society (2022)

These recognitions highlight his significant contributions to research and academia, establishing him as a leader in his field.

Research Interests

Prof. Fu’s research interests span various crucial topics in machine learning and computer vision:

  1. Learning from Small Samples: He focuses on statistical sparsity and has developed methods for one-shot and few-shot learning.
  2. 3D/4D Object Reconstruction: His work includes innovative techniques for 3D model reconstruction and robotic grasping.
  3. Artificial Intelligence and Generative Models: He explores foundation models for image manipulation and advanced applications in robotic tasks.

His diverse research portfolio reflects his commitment to advancing knowledge in these areas, driving innovation through interdisciplinary approaches.

Positions Held

Prof. Fu’s career includes prestigious positions such as:

  • DECRA Fellow (2017-2020)
  • Visiting Professor at renowned institutions, including Tencent AI Lab and AItricks.com
  • Associate Professor and Professor at Fudan University since 2016

These roles not only underscore his expertise but also his ability to contribute to and lead significant research projects.

Selected Publications

Prof. Fu has authored numerous influential papers, contributing substantially to the field. Some notable publications include:

  • “Pixel2mesh: Generating 3D Mesh Models from Single RGB Images” – A pioneering work in 3D modeling.
  • “An End-to-End Architecture for Class-Incremental Object Detection with Knowledge Distillation” – Recognized with a best paper award, showcasing his innovative approach to object detection.

Publication Top Notes

Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers

Pixel2mesh: Generating 3d mesh models from single rgb images

Soft filter pruning for accelerating deep convolutional neural networks

Transductive Multi-view Zero-Shot Learning

Pose-normalized image generation for person re-identification

Chained-tracker: Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking

Multi-scale deep learning architectures for person re-identification

Multi-level semantic feature augmentation for one-shot learning

Transductive multi-view embedding for zero-shot recognition and annotation

Conclusion

Given his extensive educational background, numerous prestigious awards, impactful research interests, and significant contributions to the field of computer vision, Prof. Dr. Yanwei Fu stands out as an exemplary candidate for the Best Researcher Award. His work not only advances theoretical knowledge but also has practical implications that push the boundaries of current technology.

Dinesh Sharma | Computer Science | Best Researcher Award

Dr. Dinesh Sharma | Computer Science | Best Researcher Award

Dr. Dinesh Sharma, Manipal University Jaipur, India

Dr. Dinesh Sharma holds a Ph.D. in Computer Science and Engineering from Uttarakhand and an M.E. from C-DAC, Pune. With over 14 years of experience in technical and engineering education, he currently serves as an Associate Professor at Manipal University Jaipur. He has published multiple patents, including innovations in animal wellbeing and waste management. Dr. Sharma is a technical committee member for various international conferences and has acted as a guest editor for respected journals. He is also an AICTE High-Performance Computing Master Trainer, dedicated to advancing technology in education. šŸŒāœØ

 

Publication profile

Scopus

Qualification

Dr. Dinesh Sharma is an accomplished academic in the field of Computer Science and Engineering, holding a Ph.D. from Uttarakhand Technical University. He also earned a Masterā€™s degree in CSE from C-DAC, Pune, and a Bachelor’s degree from R.G.P.V., Bhopal. With over 14 years of experience in technical education, he currently serves as an Associate Professor at Manipal University Jaipur. Dr. Sharma has a strong research background, with multiple patents and publications focusing on innovative technologies. His contributions to academia include serving as a reviewer for numerous journals and as a technical committee member for various international conferences. šŸŒāœØ

 

Professional Achievements šŸ†

Dr. Dinesh Sharma has made significant contributions to academia and industry, serving as a Guest Editor for a special issue on “Industrial System Pioneering in Industry 4.0” in the Journal of New Materials and Electrochemical Systems. He is an AICTE High-Performance Computing Master Trainer and has been invited as a session chair at numerous international conferences, including IEEE CSNT and CICN. Dr. Sharma coordinated a five-day Faculty Development Program on IoT at Amity University and served as an Associate Editor for Pragyan Journal of Information Technology. Additionally, he reviews for various SCI, IEEE, and Scopus-indexed journals. šŸŒāœØ

 

Awards & Guided Projects šŸ…

Dr. Dinesh Sharma has successfully mentored CSE students who achieved remarkable milestones, including securing international funding of $1,000 and $250 from Latrobe University Technology Grand Challenge, where one project also won the 1st runner-up prize. Under his guidance, Mr. Ashish Kumar Mishra developed a ā€œSmart Attendance System,ā€ earning 1st position in a national challenge organized by Amazon and receiving ā‚¹35,000. Additionally, Ms. Priyanshi Gupta won ā‚¹30,000 and the runner-up prize at the “Gwalior Smart City Tech Challenge 2020.” Dr. Sharma also led the development of the web conferencing platform “Bharat Live” for online activities. šŸŒšŸŽ‰

 

Professional Experience šŸ“š

Dr. Dinesh Sharma brings over 14 years of expertise in technical and engineering education, specializing in software development with 8 years of freelance experience in C#, ASP.Net, PHP, Java, and Android app development. Currently, he serves as an Associate Professor in Data Science and Engineering at Manipal University Jaipur since August 2023, where he is also a software developer, KPI coordinator, and E-cell coordinator. Previously, he worked as an Assistant Professor at Amity University Madhya Pradesh and IMS Unison University, contributing significantly as a software developer and coordinator for various academic initiatives. His journey began as the Head of the Computer Science & Engineering Department at Amardeep College of Engineering and Management. šŸŽ“šŸ’»

 

Conclusion

Dr. Dinesh Sharmaā€™s qualifications, innovative research contributions, professional achievements, and mentorship make him an exemplary candidate for the Best Researcher Award. His commitment to advancing technology and educating future generations in the field of computer science is commendable, and he is well-deserving of this recognition.

 

Publication Top Notes

  • Neural network-based soil parameters predictive coordination algorithm for energy efficient wireless sensor networkCited by: 0 (2024) šŸ¾
  • Automatic detection and classification of plant leaf diseases using image processing: A surveyCited by: 1 (2023) šŸŒ±
  • Enhancing Feature Extraction in Plant Image Analysis through a Multilayer Hybrid DCNNCited by: 0 (2023) šŸ–¼ļø
  • Comparative Analysis of Skin Cancer Detection Using Classification AlgorithmsCited by: 1 (2023) šŸŽ—ļø
  • Face Mask Detection Analysis for Covid19 Using CNN and Deep LearningCited by: 3 (2022) šŸ˜·
  • Energy Efficient Multitier Random DEC Routing Protocols for WSN: In AgriculturalCited by: 18 (2021) šŸŒ¾
  • A new energy efficient multitier deterministic energy-efficient clustering routing protocol for wireless sensor networksCited by: 34 (2020) šŸ’”
  • Comparative energy evaluation of lEACH protocol for monitoring soil parameter in wireless sensors networkCited by: 7 (2018) šŸŒ
  • Enhance PeGASIS algorithm for increasing the life time of wireless sensor networkCited by: 6 (2018) āš”

Xiaozhou Lei | Computer Science | Best Researcher Award

Xiaozhou Lei | Computer Science | Best Researcher Award

Dr Xiaozhou Lei, shanghai university, China

Evaluation for the Best Researcher Award: Dr. Xiaozhou Lei.

Publication profile

Orcid

Research Contributions and Innovations

Dr. Xiaozhou Lei has made notable contributions to the field of image enhancement through his pioneering work on the cell vibration energy model. This model, which he first proposed, quantitatively describes the relationship between stimulus intensity and energy during cell photothermal conversion. His work has successfully applied this model to address significant challenges in low-light enhancement and image dehazing, offering a novel approach to these problems. This research represents a unique intersection of biological modeling and image processing, with potential applications across various scientific and technological domains.

Academic Achievements

Dr. Lei has demonstrated a solid academic foundation, having earned his B.S. and M.S. degrees in mechanical design and mechatronic engineering, respectively, from the Wuhan Institute of Technology. He is currently pursuing his Ph.D. in control science and engineering at Shanghai University, which underscores his commitment to advancing his expertise. Despite being early in his academic career, Dr. Lei has completed or is involved in 9 research projects, published 5 papers in SCI-indexed journals, and contributed to the field by serving as a reviewer for the Pattern Recognition Journal.

Industry and Professional Involvement

Dr. Leiā€™s involvement in 11 consultancy and industry projects highlights his ability to bridge the gap between academic research and practical applications. Although he has not yet published books or patents, his work has significant implications for the fields of image processing and photothermal conversion. His professional network is also expanding, as seen in his reviewer role, although he does not currently hold any editorial appointments or professional memberships.

Conclusion

Dr. Xiaozhou Leiā€™s innovative research on the cell vibration energy model and its application to image enhancement positions him as a strong candidate for the Best Researcher Award. His work is both original and impactful, demonstrating a deep understanding of both the theoretical and practical aspects of his field. While his academic and professional profile is still developing, his contributions thus far are promising and reflect significant potential for future advancements. Thus, he is a suitable candidate for recognition in this award category.

Publication top notes

Low-light image enhancement based on cell vibration energy model and lightness difference

Low-Light Image Enhancement Using the Cell Vibration Model

 

Huilong Fan | Computer Science | Best Researcher Award

DrĀ HuilongĀ Fan |Ā Ā Computer Science |Ā Ā Best Researcher Award

assistant researcher atĀ Ā University of Electronic Science and Technology of China

Huilong Fan is a research assistant at the University of Electronic Science and Technology of China, born in December 1992, and residing in Changsha, Hunan. He specializes in Edge Computing and Artificial Intelligence.

profile

Academic Background:

  • Ph.D. in Computer Science and Technology, Central South University (2019-2023)
    • Major: Satellite multi-intelligence collaborative computing, digital twins, swarm intelligence negotiation, multi-intelligence deep reinforcement learning, online scheduling, artificial intelligence, machine learning.
  • Master in Computer Science and Technology, Guizhou University (2015-2018)
    • Major: Medical big data, big data analysis and prediction, deep learning, multi-label data classification, natural language processing.
  • Bachelor in Network Engineering, Nanyang Institute of Technology (2010-2014)
    • Major: Computer Networks, Principles of Computer Composition, Operating Systems, Algorithm Design.

Professional Experience:

  • Data Analyst, Beijing Ark Hospital (June 2014-Sept 2015; Dec. 2023-Present)
    • Responsibilities: Data cleaning, analysis, and visualization, system development and maintenance, research on satellite networks, collaborative computing, and edge computing.
  • R&D Engineer, Hunan Lisen Data Technology Co Ltd (June 2018-Sept 2019)
    • Responsibilities: Algorithm design, multi-platform software architecture design, software development, database management, interface development and design.

Projects and Leadership:

  • Led projects on mixed integer programming for multi-process production scheduling, satellite and management software R&D, and real-time analysis methods for large-scale multi-source data based on supercomputing.
  • Participated in significant research such as intelligent analysis technology for TFDS images and resource allocation technology based on collaborative perception.

Awards and Patents:

  • Second prize in scientific and technological progress (2020)
  • First prize in the Guizhou Province Innovation and Entrepreneurship Competition (2016)
  • National third prize in the ‘Internet +’ College Students Innovation and Entrepreneurship Competition (2016)
  • Invention Patents: Multi-agent Space-based Information Network Task Scheduling Method (2021), Dynamic Reconfigurable Space-based Information Network Simulation and Computing System (2022).

Skills:

  • Proficient in software architecture design, Java, Python, C, and other programming languages.
  • Experienced in leading R&D teams and writing research project applications.

Research Focus in Computer Science:

Huilong Fan’s research in Computer Science spans several advanced and interdisciplinary areas, primarily focusing on:

  1. Satellite Multi-Intelligence Collaborative Computing:
    • Developing systems that allow multiple intelligent agents to work together effectively in satellite networks.
    • Utilizing collaborative algorithms to improve the efficiency and reliability of satellite communications and operations.
  2. Digital Twins:
    • Creating digital replicas of physical systems to simulate and analyze their real-world counterparts.
    • Applying digital twin technology to monitor, diagnose, and optimize satellite and network operations.
  3. Swarm Intelligence Negotiation:
    • Investigating algorithms that enable decentralized agents to coordinate and negotiate within a swarm.
    • Using swarm intelligence for tasks such as resource allocation and scheduling in dynamic environments.
  4. Multi-Intelligence Deep Reinforcement Learning:
    • Developing deep learning models that enable multiple intelligent agents to learn and adapt to complex environments.
    • Applying these models to solve problems in satellite networks and edge computing.
  5. Online Scheduling:
    • Researching methods for real-time scheduling of tasks and resources in dynamic and distributed systems.
    • Focusing on optimizing the allocation of contact windows in satellite communication networks.
  6. Artificial Intelligence and Machine Learning:
    • Applying AI and ML techniques to solve complex problems in big data analysis, prediction, and decision-making.
    • Emphasizing multi-label data classification and natural language processing for diverse applications.
  7. Medical Big Data:
    • Analyzing and predicting trends in medical data using big data technologies.
    • Developing models for deep learning and multi-label classification to enhance medical data interpretation and application.
  8. Graph-Driven Resource Allocation:
    • Utilizing graph theory and cooperative game theory to optimize resource allocation in Internet of Things (IoT) and satellite networks.
    • Developing adaptive scheduling algorithms for real-time and dynamic environments.

Through his extensive research, Huilong Fan aims to push the boundaries of what is possible in satellite communication, edge computing, and AI, contributing significantly to advancements in these fields.

Publication Top Notes:

  • Dynamic Network Resource Autonomy Management and Task Scheduling Method Li, X., Yang, J., Fan, H.
    Mathematics, 2023, 11(5), 1232. Citations: 6
  • A novel multi-satellite and multi-task scheduling method based on task network graph aggregation Fan, H., Yang, Z., Zhang, X., Long, J., Liu, L.
    Expert Systems with Applications, 2022, 205, 117565. Citations: 15
  • A Spatio-Temporal Graph Neural Network Approach for Traffic Flow Prediction Li, Y., Zhao, W., Fan, H.
    Mathematics, 2022, 10(10), 1754. Citations: 6
  • Quantum Digital Signature with Continuous-Variable Deng, X., Zhao, W., Shi, R., Ding, C., Fan, H.
    International Journal of Theoretical Physics, 2022, 61(5), 144. Citations: 3