Paulo Eugênio da Costa Filho | Artificial Intelligence | Best Researcher Award

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Mr. Paulo Eugênio da Costa Filho | Artificial Intelligence | Best Researcher Award

Researcher at Federal University of Rio Grande do Norte, Brazil

Paulo Eugênio da Costa Filho is a dedicated Brazilian researcher and educator in the field of Food Science and Technology, with a strong focus on Food Microbiology. He has played pivotal roles in advancing food safety and quality, particularly through microbiological analysis of food and food products. With over two decades of academic and scientific experience, he serves as a full professor at the Federal University of Ceará (UFC). Prof. Costa Filho is also recognized for his involvement in graduate education and leadership in various research projects and academic societies.

Profile

Education :

The academic journey of this accomplished scholar reflects a deep-rooted commitment to excellence in food science and engineering. They hold a Bachelor’s degree in Food Engineering from the Federal University of Ceará (1993), which laid a strong foundation in the principles of food processing and safety. Advancing their expertise, they pursued both a Master’s (1997) and a PhD (2003) in Food Science and Technology at the Federal University of Viçosa, where they honed their research skills and specialized knowledge in food systems. Their academic path culminated with a prestigious postdoctoral research tenure at Université Laval, Canada (2009), further enriching their global perspective and scholarly contributions to the field.

Experience :

Since 2016, the researcher has served as a Full Professor in the Department of Food Technology at the Federal University of Ceará (UFC), where they specialize in the Microbiology of Foods. In this capacity, they have played a pivotal role in shaping both academic and research directions within the field. As Coordinator of the Graduate Program in Food Science and Technology at UFC, they have demonstrated strong leadership in advancing graduate education, curriculum development, and research collaboration. Their international experience includes a valuable period as a Visiting Researcher at Université Laval in Canada, where they completed a postdoctoral fellowship, further enriching their expertise and fostering cross-border scientific exchange.

Awards and Recognitions :

Prof. Paulo Eugênio da Costa Filho is a CNPq Research Productivity Fellow – Level 1D, a prestigious recognition awarded to researchers with a consistent and influential scientific output in Brazil. This honor reflects his long-standing contributions to advancing food microbiology and food safety through innovative research and academic leadership. His impactful role in graduate education is equally distinguished; Prof. Costa Filho has been nationally recognized for his dedication to mentoring future scientists and for strengthening the graduate training infrastructure in Food Science and Technology across Brazil. His efforts have significantly influenced both academic excellence and professional development in the field.

Research Focus :

The researcher’s work in Food Microbiology is distinguished by a comprehensive and applied focus on critical areas impacting food safety and innovation. Their research emphasizes the study of pathogenic microorganisms in foods, addressing public health concerns through advanced microbiological quality control practices. They actively investigate antimicrobial compounds and bacterial biofilms, contributing to the understanding and mitigation of microbial resistance in food environments. A significant part of their work involves the development and validation of analytical methods for the precise detection and control of microorganisms, ensuring the reliability and safety of food systems. Additionally, they explore the functional properties of probiotic and antimicrobial food components, aiming to enhance the nutritional and protective qualities of food products. This multifaceted research approach reflects a strong commitment to advancing food microbiology through both scientific rigor and real-world application.

Research  Skills :

With extensive expertise in microbiological analysis of foods, this professional is deeply committed to advancing food safety and quality assurance through rigorous scientific approaches. Their work emphasizes the design of microbiological research methodologies tailored to emerging foodborne challenges and technological innovations. In addition to research, they play a pivotal role in graduate student mentorship and thesis supervision, nurturing the next generation of food scientists with a focus on critical thinking and applied microbiology. Their capacity for project coordination and academic leadership has consistently driven collaborative initiatives, strengthened interdisciplinary networks, and elevated the standards of both research output and educational excellence.

Pulication Top Notes : 

Internet of Smart Grid Things (IoSGT): Prototyping a Real Cloud-Edge Testbed

Authors: H. Santos, P. Eugênio, L. Marques, H. Oliveira, D. Rosário, E. Nogueira, et al.

Source: Anais do XIV Simpósio Brasileiro de Computação Ubíqua e Pervasiva

Citations: 7

Year: 2022

Predictive Fraud Detection: An Intelligent Method for Internet of Smart Grid Things Systems

Authors: L. Bastos, B. Martins, H. Santos, I. Medeiros, P. Eugênio, L. Marques, et al.

Source: Journal of Internet Services and Applications, Vol. 14(1), pp. 160–176

Citations: 5

Year: 2023

Analysis of Electrical Signals by Machine Learning for Classification of Individualized Electronics on the Internet of Smart Grid Things (IoSGT) Architecture

Authors: L. Marques, P. Eugênio, L. Bastos, H. Santos, D. Rosário, E. Nogueira, et al.

Source: Journal of Internet Services and Applications, Vol. 14(1), pp. 124–135

Citations: 2

Year: 2023

Virtualized 5G Testbed using OpenAirInterface: Tutorial and Benchmarking Tests

Authors: M. Dória, V. Sousa, A. Campos, N. Oliveira, P. Eduardo, C. Lima, J. Guilherme, et al.

Source: Journal of Internet Services and Applications, Vol. 15(1), pp. 523–535

Citations: Not yet cited

Year: 2024

Conclusion :

Paulo Eugênio da Costa Filho is a strong candidate for the Best Researcher Award, particularly for awards that value practical innovation, interdisciplinary research, and technology for public good. His profile showcases a rare blend of technicaldepth, creative application, and community impact, all rooted in scientific rigor and hands-on implementation. With cntinued development in publication strategy and international networking, he has the potential to become a leading figure in applied computing and sustainable technology solutions not just in Brazil, but globally.

Hamna Baig | Artificial Intelligence | Young Researcher Award

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Ms. Hamna Baig | Artificial Intelligence | Young Researcher Award

Research Internee | COMSATS University Islamabad, Attock Campus | Pakistan

Hamna Baig 🎓 is a passionate and award-winning Electrical Engineering graduate from COMSATS University Islamabad, Attock Campus. A gold medalist 🥇 with a CGPA of 3.66, she blends academic brilliance with innovative research in AI, IoT, and robotics 🤖. Hamna’s dynamic work spans smart environments, RF sensing, and machine learning applications 💡. She has published multiple research papers 📚, led various technical projects, and participated in prestigious conferences 🏛️. Her leadership roles and technical writing expertise further reflect her versatility 🧠. Hamna aims to revolutionize engineering solutions through creativity, technology, and social impact 🌍.

Professional profile : 

Google Scholar

Orcid 

Summary of Suitability : 

Hamna Baig exemplifies the essence of a young and emerging researcher through her exceptional academic performance, innovative contributions to AI-driven engineering, and a prolific portfolio of research publications. A gold medalist in Electrical Engineering from COMSATS University Islamabad, she has demonstrated consistent excellence in both theoretical knowledge and practical application. With multiple high-impact publications, advanced project implementations, and recognized conference presentations, she brings outstanding promise to the future of intelligent systems and healthcare engineering. Her dedication to interdisciplinary innovation, backed by hands-on experience and leadership roles, showcases her as a rising star in engineering research.

🔹 Education & Experience :

📘 Education:

  • 🎓 B.Sc. Electrical Engineering, COMSATS University Islamabad, Attock Campus (2020–2024) – CGPA: 3.66/4.00, Gold Medalist 🏅

  • 📑 Final Year Project: AI-based Environmental Control Model for Smart Homes 🏠🤖

🧑‍💼 Experience:

  • 🧪 Internee, Electrical & Computer Engineering Dept., COMSATS, under PEC GIT Program (2024–Present)

  • ⚡ Internee, Ghazi-Barotha Hydro Power Plant (GBHPP), WAPDA (2023)

  • 🖋️ Technical Writer (Electrical/Electronics), CDR Professionals (2023–Present)

Professional Development :

Hamna Baig has actively pursued professional growth through certifications, leadership, and community engagement 🌱. She completed the prestigious “Machine Learning Specialization” by DeepLearning.AI 🤖, “Generative AI for Everyone” 🧠, and several tech courses from Stanford, Yonsei, and the University of Michigan via Coursera 🎓. As a proactive learner, she enhances her skills in AI, IoT, wireless communication, and public speaking 🎤. Hamna has held key roles such as President of the Sports Society 🏸, Co-Campus Director of AICP 🧑‍🔬, and VP of COMSATS Science Society. Her drive to uplift communities and advance technology sets her apart 🌟.

Research Focus : 

Hamna’s research centers on the integration of Artificial Intelligence and Machine Learning into real-world electrical and biomedical systems 🤖🧠. She explores SDR-based gait monitoring for Parkinson’s patients 🧓, AI-controlled environmental systems for energy-efficient smart homes 🌡️, and intelligent robotic applications in agriculture 🤖🍎. Her work emphasizes non-invasive health monitoring using RF sensing 🛏️ and AI-powered automation solutions. She is deeply invested in translating complex algorithms into practical, user-centric applications that elevate health, comfort, and productivity ⚡. Her interdisciplinary approach bridges electrical engineering with innovative computing solutions 🔌📊.

Awards & Honors :

  • 🏆 Awards & Certificates:

    • 🥇 Gold Medalist, COMSATS University Islamabad (2024)

    • 🧾 Certificate of Gratitude, ICTIS Conference, UET Peshawar (2024)

    • 📜 Certificate of Gratitude, ICCSI Conference, University of Haripur (2024)

    • 🧠 ML Specialization Certificate, DeepLearning.AI – Stanford (2023)

    • 🧬 Generative AI for Everyone – DeepLearning.AI (2025)

    • 🧏‍♀️ Public Speaking Specialization – University of Michigan (2024)

    • 📶 Wireless Communications Course – Yonsei University (2024)

    • 🎓 Prime Minister’s Youth Laptop Scheme Awardee (2023)

    • 🥇 Winner – IoT Pick and Place Robotic Competition, COMSATS (2024)

    • 🧒 Student of the Year – COMSATS University, Attock (2023)

Publication Top Notes : 

  • • Title: Intelligent Frozen Gait Monitoring using Software Defined Radio Frequency Sensing
    Citation: Electronics, 14(8), 1603, 2025
    Authors: Khan, M. B., Baig, H., Hayat, R., Tanoli, S. A. K., Rehman, M., Thakor, V. A., & Haider, D.
    Year: 2025

  • • Title: Machine Learning-Based Estimation of End Effector Position in Three-Dimension Robotic Workspace
    Citation: IJIST Journal (Impact Factor: 4.312)
    Authors: Baig, H., Ahmed, E., Jadoon, I., & Pakistan, K. C. A.
    Year: 2024

  • • Title: A Robotic Approach for Fruit Harvesting with Machine Learning-Based Joint Angles Prediction
    Citation: Submitted to ICCSI – International Conference on Computational Sciences and Innovations
    Authors: Baig, H., Baig, A.A, Ahmed, E., Jadoon, I., & Pakistan
    Year: 2024

  • • Title: Artificial Intelligence Based Adaptive Fan Control in Office Settings for Energy Efficiency
    Citation: Submitted to ICCIS – Proceedings to Springer Journal
    Authors: Baig, H.
    Year: 2024

  • • Title: A Robotic Arm Based Intelligent Biopsy System
    Citation: Submitted to ICCIS – Kohat University, Springer Proceedings
    Authors: Baig, H.
    Year: 2024

  • • Title: Design of an Intelligent Wireless Channel State Information Sensing System to Prevent Bedsores
    Citation: IEEE Sensors Journal (Under Review)
    Authors: Baig, H.
    Year: 2024

  • • Title: Enhancing Home Comfort and Energy Consumption with an Artificial Intelligence-Based Environmental Sensing Control Model
    Citation: PeerJ (Computer Science) (Under Review)
    Authors: Baig, H.
    Year: 2024

  • • Title: Breathing Techniques Redefined: The Pros and Cons of Traditional Methods and the Promise of SDRF Sensing
    Citation: Elsevier – Digital Communications and Networks (Under Review)
    Authors: Baig, H.
    Year: 2024

Conclusion : 

  • Hamna Baig not only meets but exceeds the expectations of a Young Researcher Award recipient. Her innovative mindset, research productivity, and real-world problem-solving approach make her an ideal candidate. Her work is not just academically sound but socially impactful—especially in the domains of healthcare and automation. She is a beacon of excellence and innovation, representing the future of engineering research. 🌟

 

Dr. Haochen Li | Machine Learning | Best Researcher Award

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Dr. Haochen Li | Machine Learning | Best Researcher Award

Dr. Haochen Li, Taiyuan University of Science and Technology, China

Dr. Haochen Li is an accomplished researcher specializing in electrical engineering, with a strong emphasis on power electronics, power systems, and data-driven optimization techniques. His academic journey has been marked by significant contributions to the development of intelligent power flow control and renewable energy integration. His research focuses on applying advanced machine learning techniques, such as graph-based neural networks, to improve power grid stability, reliability, and efficiency. With multiple high-impact publications in top-tier journals, Haochen Li has made notable strides in tackling challenges in microgrid systems, power flow optimization, and spatiotemporal power predictions. His innovative approaches have garnered recognition from the research community, positioning him as a leading figure in modern electrical power system advancements.

Profile:

Orcid

Scopus

Education:

Dr.  Haochen Li has pursued a rigorous academic path, building expertise in electrical engineering and control systems. He completed his undergraduate studies in Electrical Engineering and Automation, followed by a master’s degree in Power Electronics and Electric Drives, where he specialized in microgrid system control technologies. Currently, he is pursuing a Ph.D. in Control Engineering, focusing on the application of data mining techniques in power systems. His educational background has provided him with a strong foundation in both theoretical and applied research, enabling him to develop innovative solutions for optimizing power system performance.

Experience:

Dr. Haochen Li has been actively involved in academia and research, contributing to the advancement of electrical and control engineering. He is currently associated with the Taiyuan University of Science and Technology, where he engages in cutting-edge research on power flow optimization and renewable energy integration. His experience spans multiple collaborative projects, where he has worked alongside leading experts to develop intelligent algorithms for power system management. Through his academic endeavors, he has gained expertise in modeling and simulation of power systems, integrating artificial intelligence techniques into energy management, and analyzing grid uncertainties for enhanced performance.

Research Interests:

Dr. Haochen Li’s research interests revolve around the intersection of power systems and data science, with a particular focus on:

  • Power Flow Optimization ⚡ – Developing intelligent algorithms to enhance the efficiency of electricity transmission.

  • Renewable Energy Integration 🌍 – Designing predictive models for wind and solar energy systems.

  • Graph Neural Networks in Power Systems 🤖 – Utilizing AI-driven techniques for improving grid stability and reliability.

  • Spatiotemporal Data Analysis ⏳ – Leveraging big data approaches to enhance power grid forecasting.

  • Microgrid System Control 🔋 – Implementing advanced control strategies for distributed energy resources.

Awards:

Dr. Haochen Li’s contributions to power system research have been recognized through various academic and research accolades. His outstanding work in data-driven optimization for power flow calculations has been acknowledged by prestigious institutions. Additionally, his research on renewable energy forecasting has earned him recognition in international conferences and journal publications. His ability to bridge theoretical research with practical applications has positioned him as a key innovator in the field.

Publications:

  • Physics-Guided Chebyshev Graph Convolution Network for Optimal Power Flow

    • Publication Year: 2025

  • Graph Attention Convolution Network for Power Flow Calculation Considering Grid Uncertainty

    • Publication Year: 2025

  • Joint Missing Power Data Recovery Based on Spatiotemporal Correlation of Multiple Wind Farms

    • Publication Year: 2024

  • Spatiotemporal Coupling Calculation-Based Short-Term Wind Farm Cluster Power Prediction

    • Publication Year: 2023

Conclusion:

Dr. Haochen Li is a highly dedicated researcher whose work has significantly contributed to the field of power system engineering. His expertise in artificial intelligence, power flow optimization, and renewable energy forecasting has positioned him as a thought leader in the integration of smart grid technologies. With a strong publication record, ongoing innovative research, and a commitment to enhancing power system reliability, he is a deserving candidate for the Best Researcher Award. His ability to merge theoretical advancements with real-world applications showcases his potential to lead future innovations in intelligent power systems.

Dr. Ryszard Ćwiertniak | Artificial Intelligence | Best Researcher Award

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Dr. Ryszard Ćwiertniak | Artificial Intelligence | Best Researcher Award

Dr. Ryszard Ćwiertniak, Krakow University of Economics, Poland

Dr. Ryszard Ćwiertniak 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.

🌍 Professional Profile:

Orcid

Google Scholar

🏆 Suitability for Best Researcher Award 

Dr. Ryszard Ćwiertniak’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.

🎓 Education 

Dr. Ryszard Ćwiertniak earned his PhD in Management and Quality Sciences from the University of Economics in Krakow (2019), focusing on innovation management. He also holds a Master’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.

💼 Professional Experience 

Dr. Ćwiertniak currently serves as an academic teacher at Krakow University of Economics, specializing in technology and product ecology. Previously, he was the Rector’s Representative for Quality of Education and E-learning at the College of Economics and Computer Science (2020–2024). 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.

🏅 Awards and Honors 

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

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

🔬 Research Focus

Dr. Ćwiertniak’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.

📊 Publication Top Notes:

  1. Rola potencjału innowacyjnego w modelach biznesowych nowoczesnych organizacji – próba oceny

    • Citations: 11
    • Year: 2015

  2. Zarządzanie portfelem projektów w organizacji: Koncepcje i kierunki badań

    • Citations: 4
    • Year: 2018

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

    • Citations: 3
    • Year: 2022

  2. Kształtowanie portfela projektów w zarządzaniu 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 Xi’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. 🚀📡

🌍 Professional Profile:

Google Scholar

🏆 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. 🏆🚀

🎓 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. 🎓📡🔬

💼 Experience 

Dr. Yingbin Wang is a Senior Engineer at the National Key Laboratory of Science and Technology on Space Microwave, Xi’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. 🚀📶

🏅 Awards & Honors 

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. 🏆📡

🔬 Research Focus 

Dr. Yingbin Wang’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. 🚀📡🛰️

📖 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 AI 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.

🌍 Professional Profile:

Google Scholar

🏆 Suitability for Award 

Dr. Xin Wang’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. Wang’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.

🎓 Education 

  • Ph.D. in Control Science and Engineering (2015-2020) – Zhejiang University, supervised by Prof. Peng Cheng & Prof. Jiming Chen, specializing in AI Security and Distributed Intelligence.
  • Visiting Scholar in Information Security (2018-2019) – 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.

💼 Experience 

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

🏅 Awards and Honors 

  • Taishan Scholars Award (2024) 🏅 – Recognized for research excellence in AI security and distributed systems.
  • Leader of Youth Innovation Team (2022) 🚀 – Acknowledged for driving innovation in Shandong Higher Education Institutions.
  • Second Prize, Shandong Provincial Science and Technology Progress Award (2022) 🏆 – Contributions to federated learning and privacy-preserving AI.
  • Best Paper Award, CCSICC’21 📄 – Vulnerability Analysis for IoT Devices in Multi-Agent Systems.
  • Best Paper Award, ICAUS’24 ✈️ – Optimized Data Collection for UAVs in Industrial IoT Environments.

🔬 Research Focus 

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.

📊 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

 

Prof. Ching Yee Suen | Artificial Intelligence | Best Researcher Award

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Prof. Ching Yee Suen | Artificial Intelligence | Best Researcher Award

Prof. Ching Yee Suen, Concordia University, Canada

Prof. Ching Yee Suen is a globally recognized expert in Pattern Recognition, AI, and Document Analysis. As the Founding Director and Co-Director of CENPARMI at Concordia University, he has shaped advancements in handwriting recognition, multiple classifiers, and font analysis. A Fellow of IEEE, IAPR, and the Royal Society of Canada, he has mentored 120+ graduate students and 100 visiting scientists. With 550+ research papers, 16 books, and an h-index of 74, his contributions are widely cited. His innovations power millions of devices worldwide. He has led $20M+ research projects, collaborated with global industries, and serves as an editor for top-tier journals.

🌍 Professional Profile:

Google Scholar

🏆 Suitability for Best Researcher Award 

Prof. Suen is an exceptional candidate for the Best Researcher Award due to his pioneering contributions in AI, pattern recognition, and handwriting analysis. His research has real-world impact, with millions of users benefiting from his handwriting recognition algorithms. He has received top international awards, including the King-Sun Fu Prize (2021) and ICDAR Award (2005). As a leading AI researcher, he has secured $20M+ in funding, supervised over 120 Ph.D. and master’s students, and led groundbreaking industrial collaborations. His global influence, leadership in AI, and outstanding research output make him a worthy recipient of this prestigious honor.

🎓 Education 

Prof. Ching Yee Suen holds a Ph.D. from the University of British Columbia (UBC), Vancouver, and a Master’s degree from the University of Hong Kong. His academic journey has been marked by a deep focus on Artificial Intelligence, Pattern Recognition, and Computational Vision. His early research laid the foundation for his groundbreaking work in handwriting recognition, document analysis, and AI-powered classification systems. He has spent sabbatical leaves at MIT, McGill University, Ecole Polytechnique, and IBM, further expanding his expertise. His academic credentials have established him as a leading scholar in AI and pattern recognition on a global scale.

💼 Experience 

With a career spanning 50+ years, Prof. Suen has held key leadership roles at Concordia University, serving as Chairman of Computer Science, Associate Dean (Research), and Concordia Chair in AI & Pattern Recognition. He is the Founding Director and Co-Director of CENPARMI, where he has driven cutting-edge research. He has supervised 120+ graduate students and collaborated with top institutions worldwide. As a consultant to Microsoft, Xerox, Canada Post, and the US Congress, his work has had real-world impact. His editorial leadership in top AI journals and conference organization further cements his global influence in the research community.

🏅 Awards and Honors

Prof. Suen’s excellence is recognized globally, earning him top honors in AI and pattern recognition. He received the King-Sun Fu Prize (2021) 🏆, the ICDAR Award (2005) 🎖️, and the Elsevier Distinguished Editorial Award (2016)📜. His Concordia Lifetime Research Achievement Award (2008) and Teaching Excellence Award (1995) 🎓 highlight his impact in academia. Internationally, he was honored with the Gold Medal from the University of Bari, Italy (2012) 🥇. As a Fellow of IEEE, IAPR, and the Royal Society of Canada, his contributions to AI, document analysis, and handwriting recognition are celebrated at the highest levels.

🔬 Research Focus 

Prof. Suen specializes in Pattern Recognition, Artificial Intelligence, and Document Analysis. His innovations in handwriting recognition, fake coin detection, license plate recognition, and multi-classifier systems have transformed industry applications. His research integrates AI, deep learning, and image processing to solve complex problems in computer vision, natural language processing, and fraud detection. His high-impact contributions are widely used in mobile devices, banking security, and postal services. His multi-disciplinary approach in AI has led to real-world solutions adopted by Microsoft, Bell Canada, Canada Post, and global tech firms, making him a pioneer in intelligent pattern analysis.

📊 Publication Top notes:

  • Title: Developing Knowledge Management Metrics for Measuring Intellectual Capital
    • Year: 2000
    • Citations: 442
  • Title: Modified Hebbian Learning for Curve and Surface Fitting
    • Year: 1992
    • Citations: 322
  • Title: N-Gram Statistics for Natural Language Understanding and Text Processing
    • Year: 1979
    • Citations: 315
  • Title: Analysis and Design of a Decision Tree Based on Entropy Reduction and Its Application to Large Character Set Recognition
    • Year: 1984
    • Citations: 176
  • Title: Large Tree Classifier with Heuristic Search and Global Training
    • Year: 1987
    • Citations: 102

 

 

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

🏆 Suitability for Award 

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.

🎓 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.

👨‍🏫 Experience 

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.

🏅 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.

🔍 Research Focus 

Dr. Vamsi Inturi’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.

📚 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

 

Dr. Punitha A | Machine Learning | Women Researcher Award

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Dr. Punitha A | Machine Learning | Women Researcher Award

Dr. Punitha A | K Ramakrishnan College of Technology | India

Dr. A. Punitha is a distinguished professor with 20 years of experience in the Electronics and Communication Engineering field. She is currently a faculty member at M.A.M School of Engineering, Trichy, where she also serves in leadership roles like NBA Coordinator, Head of the Department, and R&D In-Charge. Dr. Punitha is highly involved in research, especially in AI, IoT, and machine learning applications, and has received multiple research grants. Her work includes real-time monitoring systems, intrusion detection, and bio mask development. She is a prolific academic, with numerous publications and active contributions to conferences 📚👩‍🏫🤖.

Professional Profile:

SCOPUS

Suitability for Women Researcher Award

Dr. A. Punitha is highly suitable for the Women Researcher Award due to her extensive experience, leadership in academia, and significant contributions to the fields of Electronics and Communication Engineering, particularly in cutting-edge technologies such as AI, IoT, and machine learning.Dr. Punitha’s research focuses on innovative and impactful fields such as AI, IoT, and machine learning applications. She has worked on various cutting-edge projects, including real-time monitoring systems, intrusion detection systems, and bio mask development, which directly address real-world challenges. Her work in these domains exemplifies her contribution to advancing technology and creating solutions that have the potential to significantly benefit society.

Education and Experience

  • Ph.D. in Electronics and Communication Engineering 🎓
  • M.E. in Electronics and Communication Engineering 🎓
  • Total Experience: 20 Years ⏳
  • NBA Coordinator & Head of Department of ECE 🏫
  • R&D In-Charge, MAMSE 🧪
  • IIC Convener & Innovation Ambassador 🚀
  • International Conference Coordinator 🌍
  • Japanese Language Training Coordinator 🇯🇵
  • Coordinated AICTE and Tamil Nadu Science funding projects 💸

Professional Development

Dr. A. Punitha is an accomplished academic who actively contributes to the growth of her department and the institution. She has played a significant role in organizing faculty development programs, seminars, and workshops. Her involvement in innovation and research is evident through her leadership in receiving multiple grants, such as the Rs. 3.5 lakh AICTE ATAL fund and Tamil Nadu Science and Technology funds. Dr. Punitha has also acted as a resource person in webinars and conferences, discussing vital topics such as NEP 2020 and OBE. Her dedication to improving teaching quality and research at MAMSE remains evident 🌱📚💡.

Research Focus

Dr. A. Punitha’s research is centered around leveraging advanced technologies like AI, IoT, and machine learning to solve real-world problems. Her work explores areas such as intrusion detection in wireless sensor networks, brain tumor detection using CNN, and real-time monitoring systems like drowsy driving detection. She is also focusing on developing bio masks for sanitization and enhancing food processing in Industry 5.0 using AI. Dr. Punitha aims to create innovative solutions that contribute to both the academic and practical fields of technology 🌐🤖🔬.

Awards and Honors

  • Received Rs. 3.5 Lakh from AICTE ATAL for Faculty Development Program (2024) 💰
  • Funded Rs. 2.8 Lakh by Tamil Nadu Science and Technology for “Bio Mask Project” 💡
  • Awarded Rs. 20,000 for “Intra Project Expo 2021” by Tamil Nadu Science and Technology 🎉
  • Webinar Resource Person for “NEP 2020” and “OBE” at MAMSE 🎤
  • Co-principal Investigator for AICTE and Tamil Nadu Science-funded projects 🏆
  • Acted as Organizing Committee Member for National Conference with CSIR funding (Rs. 50,000) 🗣️

Publication Top notes:

  • “Dynamically stabilized recurrent neural network optimized with intensified sand cat swarm optimization for intrusion detection in wireless sensor network”
  • “Enhancing the Food Processing in Industry 5.0 Based on Artificial Intelligence”– Cited by: 1️⃣
  • “REAL TIME MONITORING AND DETECTION OF DROWSY DRIVING”
  • “Smart Method for Tollgate Billing System Using RSSI”  – Cited by: 3️⃣
  • “Privacy preservation and authentication on secure geographical routing in VANET” – Cited by: 6️⃣
  • “Secure group authentication technique for VANET” – Cited by: 5️⃣
  • “Location verification technique for secure geographical routing in VANET” – Cited by: 2️⃣

 

 

 

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. 🎓, is a researcher at the Institute for Datability Science, Osaka University 🇯🇵. With a Ph.D. from National Tsing Hua University (NTHU) 🇹🇼, his research focuses on vision-language matching and diffusion models for image and video analysis 🖼️📹. 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 languages 🌏 and programming 🖥️, Dr. Ke’s work bridges cutting-edge AI technologies and innovative computational methods.

Publication Profile

Googlescholar

Education & Experience:

Education

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

Experience

  • 🧑‍🔬 Researcher (2024–Present)
    • Institute for Datability Science, Osaka University
  • 🤖 AI Researcher (2018–2019)
    • vivo AI Lab
  • 🔬 Exchange Student (2016–2018)
    • 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 Ke’s professional journey spans academia and industry, specializing in artificial intelligence 🤖 and computer vision 👁️. His Ph.D. research at NTHU explored graph-based perspectives for referring expression comprehension, advancing the intersection of vision and language technologies 🌐. 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 analysis 📊. Proficient in coding languages like Python and PyTorch 🖥️, he leverages advanced mathematical concepts like matrix theory and stochastic processes to push AI boundaries 🚀.

Research Focus

Dr. Ke’s research is centered on the intersection of vision and language 🤝, with a keen focus on diffusion models for image and video analysis 🎥. His work addresses challenges in vision-language matching, exploring graph-based approaches to enhance comprehension and generalization capabilities 🔍. Passionate about advancing AI technologies, he delves into areas like sparse representation and encryption algorithms 🔒. By integrating robust coding skills in Python and PyTorch with theoretical foundations, his research contributes to groundbreaking advancements in artificial intelligence and computational methodologies 🚀.

Awards and Honors

  • 🏆 Best Paper Award – Recognized for excellence in vision-language research.
  • 🥇 Graduate Fellowship – National Tsing Hua University, Taiwan.
  • 🥉 Outstanding Thesis Award – Shaanxi Normal University, China.
  • 🎖️ Research Excellence Recognition – vivo AI Lab, 2019.
  • 🌟 Academic Merit Scholarship – Southwest Minzu University, China.

Publication Highlights

  • 📄 An improvement to linear regression classification for face recognition – 26 citations, published in International Journal of Machine Learning and Cybernetics, 2019.
  • 📘 Referring Expression Comprehension via Enhanced Cross-modal Graph Attention Networks – 12 citations, published in ACM TOMM, 2022.
  • 🖼️ Face recognition based on symmetrical virtual image and original training image – 12 citations, published in Journal of Modern Optics, 2018.
  • 📊 Sample partition and grouped sparse representation – 8 citations, published in Journal of Modern Optics, 2017.
  • 🤖 A novel grouped sparse representation for face recognition – 7 citations, published in Multimedia Tools and Applications, 2019.