Dr. Ryszard ฤ†wiertniak | Artificial Intelligence | Best Researcher Award

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Dr. Ryszard ฤ†wiertniak | Artificial Intelligence | Best Researcher Award

Dr. Ryszard ฤ†wiertniak, Krakow University of Economics, Poland

Dr. Ryszard ฤ†wiertniak is an accomplished expert in project management, specializing in agile methodologies, Design Thinking, and AI-driven innovation. He holds a PhD in Management and Quality Sciences from the University of Economics in Krakow and has a strong academic and professional background in administration, management, and electrical engineering. With extensive experience in research and teaching, he has contributed to the fields of digital transformation, e-learning, and Industry 4.0. As an IBM Design Thinking mentor and Early Warning Europe ambassador, he helps businesses implement cutting-edge solutions. His work spans academia, consulting, and applied research in AI and business process optimization.

๐ŸŒย Professional Profile:

Orcid

Google Scholar

๐Ÿ† Suitability for Best Researcher Awardย 

Dr. Ryszard ฤ†wiertniak’s pioneering research in AI-driven project management, digital transformation, and innovation management makes him an outstanding candidate for the Best Researcher Award. His involvement in Erasmus+ projects, contributions to Industry 4.0, and mentorship in agile methodologies showcase his impact on academia and industry. His expertise in AI-based decision-making, personalized education, and digital business models has transformed organizational processes. With numerous peer-reviewed publications, a book, and a grant-winning project, his research advances the future of smart business ecosystems. His leadership in AI-powered business solutions and educational innovations solidifies his reputation as a top researcher in the field.

๐ŸŽ“ Educationย 

Dr. Ryszard ฤ†wiertniak earned his PhD in Management and Quality Sciences from the University of Economics in Krakow (2019), focusing on innovation management. He also holds a Masterโ€™s degree in Administration and Management from the University of Warsaw (1994). In addition, he has a background in electrical engineering, equipping him with a multidisciplinary approach to research. His academic journey reflects a deep commitment to combining management principles with technology, particularly in AI applications, e-learning, and agile business strategies. His education has laid the foundation for his expertise in digital transformation, business innovation, and advanced project management methodologies.

๐Ÿ’ผ Professional Experienceย 

Dr. ฤ†wiertniak currently serves as an academic teacher at Krakow University of Economics, specializing in technology and product ecology. Previously, he was the Rectorโ€™s Representative for Quality of Education and E-learning at the College of Economics and Computer Science (2020โ€“2024). His role in the Early Warning Europe initiative highlights his expertise in digital business transformation. He also contributes to the Erasmus+ program, working on AI-powered educational solutions. As an IBM Design Thinking mentor, he facilitates agile project implementation. His professional engagements bridge academia and industry, driving innovation, AI adoption, and digital business strategies in various sectors.

๐Ÿ… Awards and Honorsย 

๐Ÿ”น Early Warning Europe Ambassador (2021โ€“Present) โ€“ Recognized for supporting digital business transformation.
๐Ÿ”น Erasmus+ Research Grant Recipient โ€“ Contributed to AI-driven education models.
๐Ÿ”น Ministerial Research Grant Winner (2021) โ€“ Awarded funding for advancing e-learning and digital education techniques.
๐Ÿ”น IBM Design Thinking Mentor โ€“ Certified expert in guiding agile and innovative project execution.
๐Ÿ”น Industry 4.0 & AI Innovation Contributor โ€“ Acknowledged for pioneering work in integrating AI with project management and digital marketing.
๐Ÿ”น Invited Researcher at THWS Business School (2024) โ€“ Recognized for leadership in AI-based digital transformation.

His contributions to AI, project management, and education technology have earned him national and international acclaim.

๐Ÿ”ฌ Research Focus

Dr. ฤ†wiertniak’s research spans AI-driven project management, innovation strategies, digital transformation, and e-learning technologies. He explores Industry 4.0 applications, AI-based decision-making, and agile methodologies to optimize business processes. His focus on digital business models, social media analytics, and e-commerce strategies has redefined marketing and management practices. Through Design Thinking and AI integration, he enhances project execution efficiency. His research also covers personalized education using AI, ensuring smarter, data-driven learning environments. As an expert in AI-powered business solutions, he contributes to making organizations more adaptable and efficient in an era of rapid technological advancements.

๐Ÿ“Šย Publication Top Notes:

  1. Rola potencjaล‚u innowacyjnego w modelach biznesowych nowoczesnych organizacji โ€“ prรณba oceny

    • Citations: 11
    • Year: 2015

  2. Zarzฤ…dzanie portfelem projektรณw w organizacji: Koncepcje i kierunki badaล„

    • Citations: 4
    • Year: 2018

  1. Addressing students’ perceived value with the virtual university concept

    • Citations: 3
    • Year: 2022

  2. Ksztaล‚towanie portfela projektรณw w zarzฤ…dzaniu innowacjami

    • Citations: 2
    • Year: 2018

  1. The concept of project evaluation in the implementation of innovation

    • Citations: 1
    • Year: 2020

 

 

Dr. Yingbin Wang | Artificial Intelligence | Best Researcher Award

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Dr. Yingbin Wang | Artificial Intelligence | Best Researcher Award

Dr. Yingbin Wang, Xi’an Institute of Space Radio Technolog, China

Dr. Yingbin Wang is a leading researcher in space microwave communication, detection, and AI-driven signal processing. He earned his Ph.D. in Electronic Science and Technology from Xidian University in 2022 and currently serves as a Senior Engineer at the National Key Laboratory of Science and Technology on Space Microwave at the Xiโ€™an Institute of Space Radio Technology. His research spans Integrated Sensing and Communication (ISAC), deep learning, and anti-jamming satellite systems. With over ten high-impact publications and contributions to national-level R&D projects, Dr. Wang is shaping the future of space-based communication and intelligent sensing. ๐Ÿš€๐Ÿ“ก

๐ŸŒย Professional Profile:

Google Scholar

๐Ÿ† Suitability for the Best Researcher Award

Dr. Yingbin Wang is a highly qualified candidate for the Best Researcher Award, given his significant contributions to space microwave communication and AI-powered signal processing. His expertise in satellite-terrestrial integration, space-based radar target detection, and anti-jamming satellite systems plays a crucial role in advancing global space technology. With publications in top-tier IEEE journals, participation in national R&D projects, and contributions to cutting-edge ISAC applications, Dr. Wang is at the forefront of next-generation communication research. His work in AI-driven remote sensing is revolutionizing the field, making him a distinguished and deserving nominee. ๐Ÿ†๐Ÿš€

๐ŸŽ“ Education

Dr. Yingbin Wang pursued his entire higher education at Xidian University, China, a prestigious institution in electronic engineering and space communication. He obtained his Ph.D. in Electronic Science and Technology in June 2022, focusing on advanced space microwave systems and AI-enhanced signal processing. His doctoral research contributed to improving satellite communication resilience, radar detection, and deep learning applications in space technologies. Throughout his academic journey, he combined hardware engineering with AI-driven software models, enabling breakthroughs in integrated satellite-terrestrial communication. His strong foundation in electromagnetic waves, remote sensing, and computational intelligence defines his research excellence. ๐ŸŽ“๐Ÿ“ก๐Ÿ”ฌ

๐Ÿ’ผ Experienceย 

Dr. Yingbin Wang is a Senior Engineer at the National Key Laboratory of Science and Technology on Space Microwave, Xiโ€™an Institute of Space Radio Technology. His role involves leading research in space microwave communication, detection, and AI-driven signal optimization. He has contributed to major national R&D projects, including space-based radar target detection, anti-jamming satellite communication, and integrated sensing for satellite-terrestrial networks. His work on AI-based signal processing and deep learning models has significantly enhanced real-time space communication efficiency. His expertise in high-frequency electromagnetic applications and AI-powered satellite technology is instrumental in shaping the future of space communications. ๐Ÿš€๐Ÿ“ถ

๐Ÿ… Awards & Honorsย 

Dr. Yingbin Wang has received multiple recognitions for his contributions to space communication and AI-driven signal processing. His research in anti-jamming satellite networks has been awarded national-level research funding. He has received Best Paper Awards at leading IEEE conferences on signal processing and remote sensing. Additionally, his contributions to integrated satellite-terrestrial communication have been recognized by the National Science and Technology Innovation Program. As a reviewer for top IEEE journals, he actively contributes to the scientific community. His pioneering work in AI-enhanced space sensing continues to push the boundaries of satellite communication technologies. ๐Ÿ†๐Ÿ“ก

๐Ÿ”ฌ Research Focusย 

Dr. Yingbin Wangโ€™s research spans Artificial Intelligence, communication, deep learning, and signal processing, with a strong emphasis on space microwave communication and detection. His work explores AI-driven radar target detection, anti-jamming satellite communication, and integrated sensing and communication (ISAC) systems. He develops machine learning models for real-time adaptive signal processing, enhancing satellite-terrestrial connectivity. His studies in neural network-driven space communication systems optimize data transmission efficiency in complex space environments. His research is critical for next-generation deep-space exploration, smart communication networks, and high-performance microwave technology, ensuring global connectivity and security in aerospace applications. ๐Ÿš€๐Ÿ“ก๐Ÿ›ฐ๏ธ

๐Ÿ“–ย Publication Top Notes

  1. SPB-Net: A Deep Network for SAR Imaging and Despeckling with Downsampled Data
    • Journal: IEEE Transactions on Geoscience and Remote Sensing
    • Publication Year: 2020
    • Citations: 27
  2. Lq-SPB-Net: A Real-Time Deep Network for SAR Imaging and Despeckling
    • Journal: IEEE Transactions on Geoscience and Remote Sensing
    • Publication Year: 2021
    • Citations: 8
  1. Multi-Scale and Single-Scale Fully Convolutional Networks for Sound Event Detection
    • Journal: Neurocomputing
    • Publication Year: 2021
    • Citations: 18
  2. MSFF-Net: Multi-Scale Feature Fusing Networks with Dilated Mixed Convolution and Cascaded Parallel Framework for Sound Event Detection
    • Journal: Digital Signal Processing
    • Publication Year: 2022
    • Citations: 12
  1. A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method
    • Journal: Sensors
    • Publication Year: 2019
    • Citations: 13

 

Prof. Dr. Xin Wang | Distributed AI | Best Researcher Award

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Prof. Dr. Xin Wang | Distributed AI | Best Researcher Award

Prof. Dr. Xin Wang, Qilu University of Technology, China

Prof. Dr. Xin Wang is a distinguished scholar in Distributed AIย and Federated Learning, currently serving as a Professor at Shandong Computer Science Center, Qilu University of Technology. With a Ph.D. in Control Science and Engineering from Zhejiang University, he has contributed significantly to AI Security, Privacy, and LLM Security. Dr. Wang has led multiple national research projects and received prestigious honors, including the Taishan Scholars Award and the Shandong Provincial Science and Technology Progress Award. His work integrates AI with secure computing, enhancing privacy protection and optimization in collaborative learning systems.

๐ŸŒย Professional Profile:

Google Scholar

๐Ÿ† Suitability for Awardย 

Dr. Xin Wangโ€™s outstanding contributions to Distributed AI, Federated Learning, and AI Security make him a strong candidate for the Best Researcher Award. As a leader in AI-driven security frameworks, he has spearheaded national-level projects focusing on privacy-preserving AI and secure learning models. His research bridges theory with practical applications, enhancing security in multi-agent and industrial IoT systems. Recognized for his high-impact publications and award-winning research, Dr. Wangโ€™s innovations in cryptographic function identification and UAV data collection optimization demonstrate exceptional originality and real-world relevance, solidifying his place as a leader in computational intelligence and AI security.

๐ŸŽ“ Educationย 

  • Ph.D. in Control Science and Engineering (2015-2020) โ€“ Zhejiang University, supervised by Prof. Peng Cheng & Prof. Jiming Chen, specializing in AI Security and Distributed Intelligence.
  • Visiting Scholar in Information Security (2018-2019) โ€“ Tokyo Institute of Technology, mentored by Prof. Hideaki Ishii, focusing on cryptographic vulnerabilities and federated learning security.

His multidisciplinary training across AI, security, and automation has positioned him at the forefront of cutting-edge computational research.

๐Ÿ’ผ Experienceย 

  • Professor (2024โ€“Present) โ€“ Shandong Computer Science Center, Qilu University of Technology.
  • Associate Professor (2020โ€“2024) โ€“ Shandong Computer Science Center, leading research on privacy protection in collaborative AI.
  • Project PI in National Natural Science Foundation of China (2025-2027) โ€“ Developing privacy-preserving defense mechanisms for federated learning.
  • Project PI in National Key Research and Development Program (2021-2024) โ€“ Developing AI-driven meta-services for cloud-based industrial manufacturing.
  • Visiting Scholar (2018-2019) โ€“ Tokyo Institute of Technology, conducting security research on cryptographic vulnerabilities in multi-agent IoT systems.

๐Ÿ… Awards and Honorsย 

  • Taishan Scholars Award (2024) ๐Ÿ… โ€“ Recognized for research excellence in AI security and distributed systems.
  • Leader of Youth Innovation Team (2022) ๐Ÿš€ โ€“ Acknowledged for driving innovation in Shandong Higher Education Institutions.
  • Second Prize, Shandong Provincial Science and Technology Progress Award (2022) ๐Ÿ† โ€“ Contributions to federated learning and privacy-preserving AI.
  • Best Paper Award, CCSICCโ€™21 ๐Ÿ“„ โ€“ Vulnerability Analysis for IoT Devices in Multi-Agent Systems.
  • Best Paper Award, ICAUSโ€™24 โœˆ๏ธ โ€“ Optimized Data Collection for UAVs in Industrial IoT Environments.

๐Ÿ”ฌ Research Focusย 

Dr. Wang specializes in Distributed AI, Federated Learning, and AI Security & Privacy. His research integrates cryptographic techniques, optimization algorithms, and adversarial defenses to improve the security of collaborative learning models. He has pioneered LLM security frameworks to safeguard against data leakage and adversarial attacks. His work extends into privacy-preserving AI for multi-agent IoT systems and UAV data collection efficiency. Through national projects, he has developed secure meta-services for cloud computing, advancing the field of intelligent automation and resilient AI architectures for real-world deployment in cyber-physical systems and industrial environments.

๐Ÿ“Š Publication Top notes:

  • Title: Privacy-Preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation
    • Year: 2020
    • Citations: 61
  • Title: Privacy-Preserving Collaborative Computing: Heterogeneous Privacy Guarantee and Efficient Incentive Mechanism
    • Year: 2018
    • Citations: 49
  • Title: Differentially Private Maximum Consensus: Design, Analysis and Impossibility Result
    • Year: 2018
    • Citations: 26
  • Title: Dynamic Privacy-Aware Collaborative Schemes for Average Computation: A Multi-Time Reporting Case
    • Year: 2021
    • Citations: 18
  • Title: Leveraging UAV-RIS Reflects to Improve the Security Performance of Wireless Network Systems
    • Year: 2023
    • Citations: 17

 

Dr. Vamsi Inturi | Machine Learning | Best Researcher Award

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Dr. Vamsi Inturi | Machine Learning | Best Researcher Award

Dr. Vamsi Inturi, Chaitanya Bharathi Institute of Technology, India

Dr. Vamsi Inturi is an accomplished researcher and academic specializing in Mechanical Engineering, with expertise in fault diagnosis, health monitoring, and digital twin technologies. He earned his Ph.D. from BITS Pilani, focusing on adaptive condition monitoring for wind turbine gearboxes. With experience spanning postdoctoral research at Trinity College Dublin and academic roles in India, he has made significant contributions to machine learning applications in engineering. He has received prestigious awards, including the Best Paper Award at the 43rd International JVE Conference. His research integrates AI and signal processing to enhance predictive maintenance and mechanical system reliability.

Professional Profile:

Google Scholar

Orcid

Scopus

๐Ÿ† Suitability for Awardย 

Dr. Vamsi Inturi is an outstanding candidate for the Best Researcher Award, given his pioneering work in mechanical fault diagnosis, machine learning, and predictive maintenance. His research significantly impacts renewable energy systems, particularly wind turbines, optimizing efficiency and reducing downtime. Recognized with international travel grants, research fellowships, and best paper awards, he has demonstrated academic excellence and innovation. His work in digital twins and signal processing has been published in high-impact journals, reinforcing his status as a leader in mechanical engineering research. His commitment to advancing engineering solutions makes him highly deserving of this prestigious recognition.

๐ŸŽ“ Education

Dr. Vamsi Inturi holds a Ph.D. in Mechanical Engineering from BITS Pilani (2016-2020), where he developed an adaptive condition monitoring scheme for wind turbine gearboxes under the supervision of Prof. Sabareesh G R and Prof. Pavan Kumar P. He earned his M.Tech in Machine Design from JNTU Kakinada (2012-2014), focusing on modeling process parameters in milling aluminum composites. His academic journey began with a Bachelor’s in Mechanical Engineering, followed by extensive research in fault diagnosis and mathematical modeling. His interdisciplinary expertise bridges mechanical systems, AI-driven analytics, and sustainable energy solutions, shaping advancements in mechanical diagnostics.

๐Ÿ‘จโ€๐Ÿซ Experienceย 

Dr. Vamsi Inturi has a diverse academic and research career. He is currently an Assistant Professor at CBIT(A), Hyderabad, specializing in engineering drawing, robotics, and mechanical systems. Previously, he was a Postdoctoral Researcher at Trinity College Dublin, managing the REMOTE-WIND project. He also served as a Research Scholar at BITS Hyderabad, working on mechanical vibrations and fault diagnosis. His teaching experience includes faculty positions at PACEITS and QISIT, mentoring students in mechanical design and computational modeling. With extensive research output in AI-driven diagnostics, he plays a crucial role in advancing predictive maintenance strategies.

๐Ÿ… Awards and Honors

Dr. Vamsi Inturi has received multiple accolades for his research excellence. He was awarded the Best Paper Award at the 43rd International JVE Conference (2019) and recognized for outstanding Ph.D. performance (2017-18). As a CSIR Senior Research Fellow (2019-20), he contributed to groundbreaking studies in mechanical diagnostics. He also secured a CSIR International Travel Grant (2019) to present his research globally. Additionally, he was elected a campus-level senate member for Ph.D. programs (2018-20). His expertise has made him a sought-after speaker and session co-chair at international mechanical engineering conferences.

๐Ÿ” Research Focusย 

Dr. Vamsi Inturiโ€™s research centers on health monitoring, fault diagnosis, and AI-driven mechanical analytics. His work integrates machine learning, signal processing, and digital twin technologies to enhance predictive maintenance in mechanical systems, particularly wind turbines. He specializes in mathematical modeling and deep learning applications for fault detection, helping industries reduce operational risks. His studies on adaptive condition monitoring schemes for gearboxes have led to innovative diagnostic frameworks. His interdisciplinary approach merges mechanical engineering with computational intelligence, making significant contributions to sustainable energy and industrial automation.

๐Ÿ“šย Publication Top Notes:

  • Title: Comparison of Condition Monitoring Techniques in Assessing Fault Severity for a Wind Turbine Gearbox Under Non-Stationary Loading
    • Volume: 124
    • Citations: 102
  • Title: Evaluation of Surface Roughness in Incremental Forming Using Image Processing-Based Methods
    • Year: 2020
    • Citations: 68
  • Title: Integrated Condition Monitoring Scheme for Bearing Fault Diagnosis of a Wind Turbine Gearbox
    • Year: 2019
    • Citations: 63
  • Title: Comprehensive Fault Diagnostics of Wind Turbine Gearbox Through Adaptive Condition Monitoring Scheme
    • Year: 2021
    • Citations: 45
  • Title: Optimal Sensor Placement for Identifying Multi-Component Failures in a Wind Turbine Gearbox Using Integrated Condition Monitoring Scheme
    • Year: 2022
    • Citations: 30

 

Jingcheng Ke | Diffusion Models | Excellence in Research

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Jingcheng Ke | Diffusion Models | Excellence in Research

Dr. Jingcheng Ke, Osaka university, Japan.

Jingcheng Ke, Ph.D.ย ๐ŸŽ“, is a researcher at the Institute for Datability Science, Osaka Universityย ๐Ÿ‡ฏ๐Ÿ‡ต. With a Ph.D. from National Tsing Hua University (NTHU)ย ๐Ÿ‡น๐Ÿ‡ผ, his research focuses on vision-language matching and diffusion models for image and video analysisย ๐Ÿ–ผ๏ธ๐Ÿ“น. He has worked as an AI researcher at vivo AI Lab and as an exchange student at Shenzhen Key Laboratory of Visual Object Detection and Recognition. Proficient in multiple languagesย ๐ŸŒย and programmingย ๐Ÿ–ฅ๏ธ, Dr. Keโ€™s work bridges cutting-edge AI technologies and innovative computational methods.

Publication Profile

Googlescholar

Education & Experience:

Education

  • ๐ŸŽ“ย Ph.D. in Communications Engineeringย (2019โ€“2024)
    • National Tsing Hua University, Taiwan
    • Thesis:ย Referring Expression Comprehension in a Graph-based Perspective and Its Generalizations
  • ๐ŸŽ“ย M.Sc. in Computer Applicationย (2015โ€“2018)
    • Shaanxi Normal University, China
    • Thesis:ย Face recognition based on virtual faces and sparse representations
  • ๐ŸŽ“ย B.Sc. in Network Engineeringย (2010โ€“2014)
    • Southwest Minzu University, China
    • Thesis:ย An improved encryption algorithm based on Data Encryption Standard

Experience

  • ๐Ÿง‘โ€๐Ÿ”ฌย Researcherย (2024โ€“Present)
    • Institute for Datability Science, Osaka University
  • ๐Ÿค–ย AI Researcherย (2018โ€“2019)
    • vivo AI Lab
  • ๐Ÿ”ฌย Exchange Studentย (2016โ€“2018)
    • Shenzhen Key Laboratory of Visual Object Detection and Recognition

Suitability for the Award

Dr. Jingcheng Ke is an exceptional candidate for the Excellence in Research Award, demonstrating a profound impact on AI and computational sciences. His Ph.D. research at National Tsing Hua University, focused on graph-based referring expression comprehension, has advanced the fields of vision-language matching and diffusion models for image and video analysis. With professional experience at Osaka University and vivo AI Lab, Dr. Ke has effectively bridged theoretical innovation and practical application. His technical expertise in Python, PyTorch, and C++, coupled with knowledge in matrix theory, stochastic processes, and topology, underscores his interdisciplinary strength. Dr. Ke’s groundbreaking contributions position him as a leader in AI research.

Professional Development

Dr. Jingcheng Keโ€™s professional journey spans academia and industry, specializing in artificial intelligenceย ๐Ÿค–ย and computer visionย ๐Ÿ‘๏ธ. His Ph.D. research at NTHU explored graph-based perspectives for referring expression comprehension, advancing the intersection of vision and language technologiesย ๐ŸŒ. With hands-on experience in AI innovation at vivo AI Lab and collaboration with top-tier research labs, he has honed his expertise in diffusion models and image/video analysisย ๐Ÿ“Š. Proficient in coding languages like Python and PyTorchย ๐Ÿ–ฅ๏ธ, he leverages advanced mathematical concepts like matrix theory and stochastic processes to push AI boundariesย ๐Ÿš€.

Research Focus

Dr. Keโ€™s research is centered on the intersection of vision and languageย ๐Ÿค, with a keen focus on diffusion models for image and video analysisย ๐ŸŽฅ. His work addresses challenges in vision-language matching, exploring graph-based approaches to enhance comprehension and generalization capabilitiesย ๐Ÿ”. Passionate about advancing AI technologies, he delves into areas like sparse representation and encryption algorithmsย ๐Ÿ”’. By integrating robust coding skills in Python and PyTorch with theoretical foundations, his research contributes to groundbreaking advancements in artificial intelligence and computational methodologiesย ๐Ÿš€.

Awards and Honors

  • ๐Ÿ†ย Best Paper Awardย โ€“ Recognized for excellence in vision-language research.
  • ๐Ÿฅ‡ย Graduate Fellowshipย โ€“ National Tsing Hua University, Taiwan.
  • ๐Ÿฅ‰ย Outstanding Thesis Awardย โ€“ Shaanxi Normal University, China.
  • ๐ŸŽ–๏ธย Research Excellence Recognitionย โ€“ vivo AI Lab, 2019.
  • ๐ŸŒŸย Academic Merit Scholarshipย โ€“ Southwest Minzu University, China.

Publication Highlights

  • ๐Ÿ“„ย An improvement to linear regression classification for face recognitionย โ€“ย 26 citations, published inย International Journal of Machine Learning and Cybernetics, 2019.
  • ๐Ÿ“˜ย Referring Expression Comprehension via Enhanced Cross-modal Graph Attention Networksย โ€“ย 12 citations, published inย ACM TOMM, 2022.
  • ๐Ÿ–ผ๏ธย Face recognition based on symmetrical virtual image and original training imageย โ€“ย 12 citations, published inย Journal of Modern Optics, 2018.
  • ๐Ÿ“Šย Sample partition and grouped sparse representationย โ€“ย 8 citations, published inย Journal of Modern Optics, 2017.
  • ๐Ÿค–ย A novel grouped sparse representation for face recognitionย โ€“ย 7 citations, published inย Multimedia Tools and Applications, 2019.

Mr. Congcong Ren | AI Award | Best Researcher Award

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Mr. Congcong Ren | AI Award | Best Researcher Award

Mr. Congcong Ren, Henan University of Science and Technology, China

Mr. Congcong Ren is a dedicated Master’s student in Vehicle and Traffic Engineering at Henan University of Science and Technology, with a Bachelor’s degree in Mechanical and Electrical Engineering from Henan Agricultural University. His expertise spans deep learning, algorithm development, and software testing, with practical experience in developing intelligent vehicles and defect detection systems. Mr. Ren has contributed to projects like an intelligent small car and wire rope defect detection, and he has gained hands-on experience during internships at Iflytek and Zeekr. His technical proficiency includes Python, PyTorch, and HIL test software, complemented by multiple school-level awards for innovation and entrepreneurship.

Professional Profile:

Orcid

Suitability for the Award

Mr. Congcong Ren is a highly suitable candidate for the Best Researcher Award based on the following points:

  1. Innovative Research:
    • His work on nighttime pedestrian detection and trajectory tracking addresses critical safety concerns in autonomous and intelligent vehicle systems. The use of fusion techniques combining visual and radar data showcases innovation in enhancing vehicle safety.
  2. Practical Experience:
    • His participation in significant projects like the intelligent small car and wire rope defect detection demonstrates his ability to apply theoretical knowledge to real-world challenges. These projects not only reflect technical skill but also his capability to collaborate effectively with industry partners.
  3. Academic and Professional Growth:
    • Mr. Renโ€™s ongoing masterโ€™s studies in artificial intelligence and traffic engineering, combined with his hands-on experience in internships at leading companies like Iflytek and Zeekr, underline his rapid professional development and adaptability in a fast-evolving field.
  4. Recognition and Skills:
    • His recognition through scholarships, awards, and publication of SCI papers highlights his academic excellence and contribution to the field. His proficiency in deep learning frameworks, coupled with practical software testing skills, positions him as a strong contender for research excellence.

Summary of Qualifications

  1. Educational Background:

    • Bachelorโ€™s Degree in Mechanical and Electrical Engineering โ€“ Henan Agricultural University (2018-2022).
      • Major courses included Mechanical Design, Automobile Design, New Energy, and Traffic Engineering.
    • Masterโ€™s Degree (ongoing) in Vehicle and Traffic Engineering โ€“ Henan University of Science and Technology (2022-2025).
      • Major courses include Principles and Methods of Artificial Intelligence, Traffic Simulation Technology, System Control Theory, and Intelligent Network Technology.

  2. Project Experience:

    • Challenge Cup Project (2022-2023): Developed an intelligent small car with adjustable wheelbase and chassis height, integrating camera and millimeter-wave radar data for obstacle detection and avoidance.
    • Wire Rope Defect Detection Project (2023): Collaborated with Luoyang Wilrop Testing Technology Co., LTD. to improve YOLOv5s algorithm for defect detection in wire ropes using industrial camera images, meeting the projectโ€™s expected requirements.

  3. Internship Experience:

    • Iflytek (2023-2024): Tested large model voice assistant software, proficient in Android Studio and Adobe Audition, and used Python for batch pressure testing.
    • Zeekr (2024): Proficient in HIL test software (ECU-TEST, Canoe, INCA), familiar with software development processes and protocols (LIN/CAN), and involved in new energy vehicle controller testing.

  4. Technical Skills:

    • Proficient in Python, PyTorch, Matlab, Simulink, and various HIL test software.
    • Strong capabilities in deep learning, algorithm development, and software testing.
    • Recognized with school-level scholarships and awards, including the innovation and entrepreneurship competition fund.

Publication Top Notes:

1.ย  Study on Nighttime Pedestrian Trajectory-Tracking from the Perspective of Driving Blind Spots โ€“ย  (2024).

2.ย  Nighttime Pedestrian Detection Based on a Fusion of Visual Information and Millimeter-Wave Radar โ€“ย  (2023).

Both articles reflect his focus on advanced technologies in vehicle safety, particularly in challenging environments like nighttime driving.

Conclusion

Mr. Congcong Ren is an outstanding candidate for the Best Researcher Award, given his solid educational foundation, innovative research contributions in vehicle safety, and substantial practical experience in engineering and software testing. His ability to combine academic research with practical applications, particularly in the field of intelligent vehicle systems, makes him a deserving recipient of this award.

 

 

 

Dr. Kuanxin Shen | Artificial intelligent Awards | Best Researcher Award

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Dr. Kuanxin Shen | Artificial intelligent Awards | Best Researcher Award

Dr. Kuanxin Shen , Shenyang University of Technology , China

Kuanxin Shen, a 27-year-old PhD candidate in Control Science and Engineering at Shenyang University of Technology, China, focuses his research on 3D Gaze Estimation using neural networks and deep learning. He holds a Bachelor’s degree earned in 2021 and a Master’s degree obtained in 2024. From 2022 to 2024, Kuanxin worked as an Algorithm Developer at Ningbo Chunjian Electronic Technology Co., Ltd., specializing in 3D intelligent perception in automotive cockpits. His research achievements include a Chinese utility model patent, several Chinese computer software copyrights, and three Chinese invention patents related to 3D eye tracking and gaze estimation. Kuanxin has also authored a book on industrial robot simulation and published a paper in the journal Sensors. He has been recognized with the third prize in the Henan Province College Students’ Robot Innovation Competition, a first-class academic scholarship, and the Excellent Thesis Award from Liaoning Province for his master’s thesis.

Professional Profile:

Orcid

๐ŸŽ“Educational Background:

Kuanxin Shen earned his Bachelor’s degree in 2021 and obtained his Master’s degree in 2024. He is currently pursuing a PhD in Control Science and Engineering at Shenyang University of Technology, China.

๐ŸขWork Experience:

From 2022 to 2024, Kuanxin Shen worked as an Algorithm Developer at Ningbo Chunjian Electronic Technology Co., Ltd., specializing in 3D intelligent perception in automotive cockpits. His responsibilities included driver gaze detection, skeleton landmark detection, behavior detection (such as smoking and phone usage), and the detection of driver distraction and fatigue. His tasks encompassed image processing, 3D detection, data annotation, cleaning, augmentation, neural network training, and model testing.

๐Ÿ†Awards and Recognitions:

During his undergraduate studies, Kuanxin Shen won the Third Prize in the 6th Henan Province College Students’ Robot Innovation Competition. In April 2024, he was awarded the First-Class Academic Scholarship during his master’s studies. In May 2024, his master’s thesis titled “Research on the Application of Driver Gaze Estimation Technology in DMS” received the Excellent Thesis Award from Liaoning Province, China

Publication Top Notes:

Title: Model-Based 3D Gaze Estimation Using a TOF Camera

  • Journal: Sensors

 

 

Dr. Sridhar Patthi | Artificial Intelligence | Best Researcher Award

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Dr. Sridhar Patthi | Artificial Intelligence | Best Researcher Award

Dr. Sridhar Patthi , Marri Laxman Reddy Institute of Technology and Management , India

Dr. Patthi Sridhar is a seasoned academic and researcher currently serving as Professor and Director at Marri Laxman Reddy Institute of Technology and Management in Hyderabad, Telangana State. With a Ph.D. in Electrical and Electronics Engineering from Jawaharlal Nehru Technological University, Hyderabad, his expertise spans across various disciplines including image detection, deep learning for image processing, self-supervised learning, and anomaly detection. He has a rich educational background, holding a Master of Technology in both Computer Science and Engineering from GIET University, Odisha, and in Electrical Power Systems from JNTU Kakinada, Andhra Pradesh. Dr. Sridhar has held progressive academic roles, previously serving as Professor and Dean at Institute of Aeronautical Engineering, Hyderabad. His research focuses on fault detection and diagnosis in electrical systems, power quality, and algorithms for research-oriented projects, reflecting his commitment to advancing technology and education.

Professional Profile:

Orcid

๐ŸŽ“Education:

Dr. Patthi Sridhar is a distinguished academic and researcher currently holding the position of Professor and Director at Marri Laxman Reddy Institute of Technology and Management in Hyderabad, Telangana State. He completed his Ph.D. in Electrical and Electronics Engineering from Jawaharlal Nehru Technological University, Hyderabad. His educational journey includes a Master of Technology in Computer Science and Engineering from GIET University, Odisha, and another Master of Technology in Electrical Power Systems from JNTU Kakinada, Andhra Pradesh.

๐ŸขWork Experience:

Dr. Sridhar’s professional career spans various significant roles in academia. Prior to his current position, he served as Professor and Dean at the Institute of Aeronautical Engineering, Hyderabad, where he also held the position of Head of the Electrical and Electronics Engineering department. Before that, he worked as an Associate Professor and Officer in Charge of Examinations at the same institute.

Publication Top Notes:

  • Adaptive neuro-fuzzy inference system based evolving fault locator for double circuit transmission lines
    • Source: IAES International Journal of Artificial Intelligence
    • Year: 2020
  • Design of power analyzer using LabVIEW
    • Source: Journal of Physics: Conference Series
    • Year: 2020
  • Step-up resonant converter for induction motor drive applications
    • Source: Journal of Physics: Conference Series
    • Year: 2020
  • Determination of stray losses using myDAQ
    • Source: Journal of Engineering and Applied Sciences
    • Year: 2018
  • Interfacing myRIO to control various sensors in electrical applications
    • Source: Journal of Engineering and Applied Sciences
    • Year: 2018

 

 

Prof. Mamede de Carvalho | Big Data Awards | Best Researcher Award

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Prof. Mamede de Carvalho | Big Data Awards | Best Researcher Award

Prof. Mamede de Carvalho, Faculdade de Medicina , Universidade de Lisboa, Portugal

Prof. Mamede de Carvalho is a distinguished medical professional renowned for his contributions to neurology and physiology. He obtained his MD from Nova Lisbon University in 1985, specializing in Neurology at the University Hospital in Lisbon in 1993. He earned his PhD in Neurology from the University of Lisbon in 2000, followed by a Habilitation in Neurosciences in 2007. Since 2010, he has served as a Full Professor of Physiology at the University of Lisbon, where he has made significant advancements in clinical neurology, particularly in ALS and neuromuscular disorders. Prof. de Carvalho’s leadership roles include Vice-Dean at the Faculty of Medicine and President of the Reynaldo dos Santos Technological Center in Lisbon. He also directed the Neuromuscular Unit at CHLN โ€“ Hospital de Santa Maria from 2009 to 2019, further cementing his impact on neurology research and practice.

๐ŸŒ Professional Profile:

Scopus

Orcid

Google Scholar

๐ŸŽ“ Education

Prof. Mamede de Carvalho is a distinguished medical professional with a robust academic background. He obtained his MD from Nova Lisbon University in Lisbon, Portugal, in 1985, followed by specialization in Neurology at the University Hospital in Lisbon in 1993. He earned his PhD in Neurology from the University of Lisbon in 2000 and completed his Habilitation in Neurosciences at the same institution in 2007. Since 2010, he has served as a Full Professor of Physiology at the University of Lisbon, contributing significantly to the fields of neurology and neuroscience through his research and academic leadership.

๐ŸŒ Professional Experience & Leadership

Prof. Mamede de Carvalho is a distinguished figure in neurology and physiology, having served as the President of the Reynaldo dos Santos Technological Center in Lisbon, Portugal, from 2017 to 2022. Prior to this, he held the position of Vice-Dean at the Faculty of Medicine โ€“ University of Lisbon from 2015 to 2022. With extensive expertise, he also served as the Director of the Neuromuscular Unit at CHLN โ€“ Hospital de Santa Maria in Lisbon from 2009 to 2019, contributing significantly to advancements in clinical neurology and neuromuscular disorders.

๐Ÿ”ฌ Clinical Research & Funding

Prof. Mamede de Carvalho is a pioneering figure in clinical neurology research, renowned for his contributions to advancements like Transcranial Magnetic Stimulation and the Threshold Technique for Axonal Excitability. His leadership has been instrumental in securing significant funding for projects focused on amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases, including grants from JPND and FCT.

Publication Top Notes:

 

 

 

 

Ms. Nathalia Hidalgo Leite | Artificial Neural Networks | Best Researcher Award

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Ms. Nathalia Hidalgo Leite | Artificial Neural Networks | Best Researcher Award

Ms. Nathalia Hidalgo Leite, State University of Campinas, Brazil

๐ŸŽ“ Nathalia Hidalgo Leite is a Ph.D. candidate in Energy Systems Planning at the State University of Campinas (Unicamp) ๐Ÿ‡ง๐Ÿ‡ท, focusing on electric mobility. She also holds an MBA in Value Investing from UniBTA and an M.S. in Energy Systems Planning from Unicamp, and a B.S. in Agronomic Engineering from UFSCar ๐ŸŒฑ. As a researcher at CPTEn and CPFL Energy, and an instructor at Unicamp, she has contributed significantly to energy systems planning and education. Her international academic experience includes programs in Portugal ๐Ÿ‡ต๐Ÿ‡น, Denmark ๐Ÿ‡ฉ๐Ÿ‡ฐ, Spain ๐Ÿ‡ช๐Ÿ‡ธ, Italy ๐Ÿ‡ฎ๐Ÿ‡น, and the USA ๐Ÿ‡บ๐Ÿ‡ธ. Nathalia’s research interests span the economic and financial viability of energy systems, artificial neural networks, and renewable energy integration ๐ŸŒž. Proficient in Portuguese, English, and Spanish, she holds numerous certifications in finance and technical skills ๐Ÿ“Š. Her achievements are recognized by multiple academic and extracurricular awards, underscoring her dedication and multifaceted talents ๐ŸŒŸ.

๐ŸŒ Professional Profile:

Google Scholar

๐ŸŽ“ Educational Background

Nathalia Hidalgo Leite is currently pursuing a Ph.D. in Energy Systems Planning at the State University of Campinas (Unicamp) ๐Ÿ‡ง๐Ÿ‡ท, focusing on the economic and financial viability for electric mobility. She also holds an MBA in Value Investing from UniBTA and an M.S. in Energy Systems Planning from Unicamp, where she studied the economic viability of photovoltaic solar energy. Nathalia completed her B.S. in Agronomic Engineering at the Federal University of Sao Carlos (UFSCar), researching artificial neural networks applied to Asian soybean rust ๐ŸŒฑ.

๐Ÿข Professional Experience

Nathalia is an accomplished researcher and instructor. At Unicamp, she has taught courses in computer algorithms, programming, numerical calculation, and discrete mathematics. As a researcher at the Sao Paulo Center for Energy Transition Studies (CPTEn) and previously at CPFL Energy, she has made significant contributions to energy systems planning. Nathalia also worked as a Financial Planning and Analysis Specialist at Grupo JLJ and interned at Ecomark Indรบstria e Comรฉrcio de Fertilizantes Especiais Ltda ๐ŸŒŸ.

๐ŸŒ International Experience

Nathalia has enriched her academic journey with several exchange programs. She spent six months at the University of Lisbon ๐Ÿ‡ต๐Ÿ‡น and the University of Southern Denmark ๐Ÿ‡ฉ๐Ÿ‡ฐ, three months at Universitat Politรจcnica de Valรจncia ๐Ÿ‡ช๐Ÿ‡ธ, one month at Universitร  degli Studi di Roma La Sapienza ๐Ÿ‡ฎ๐Ÿ‡น, and also attended Beverly Hills High School ๐Ÿ‡บ๐Ÿ‡ธ and Moore Elementary School in Colorado ๐Ÿ‡บ๐Ÿ‡ธ during her earlier education ๐Ÿ“š.

๐Ÿ“ Research Interests

Nathalia’s research interests include the economic and financial viability of energy systems, artificial neural networks, and the integration of renewable energy sources. She is particularly focused on interdisciplinary approaches to solving complex problems in energy planning and sustainability ๐ŸŒž.

๐ŸŒ Certifications and Skills

Nathalia holds numerous certifications in finance, photovoltaic systems, scientific writing, and programming. She is proficient in Portuguese (native), English (fluent), and Spanish (advanced). Her technical skills and continuous learning make her a versatile and knowledgeable professional in her field ๐Ÿ“Š.

๐Ÿ† Awards and Recognitions

Nathalia has received several awards for academic and extracurricular excellence, including the Excellence in Academic Performance award from Anglo Middle School ๐Ÿ‡ง๐Ÿ‡ท and multiple accolades from Lincoln Junior High School ๐Ÿ‡บ๐Ÿ‡ธ for academic achievement, athletic performance, and leadership. These recognitions highlight her dedication and multifaceted talents ๐ŸŒŸ.

Publication Top Notes:

1. ย Artificial Neural Networks Applied to Plant Disease
2. ย Study of Asian Soybean Rust