Humaun Kabir | Computer Science and Engineering | Editorial Board Member

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Md. Humaun Kabir | Computer Science and Engineering | Editorial Board Member

Bangamata Sheikh Fojilatunnesa Mujib Science & Technology University | Bangladesh

Md. Humaun Kabir is an accomplished academic in computer science and engineering whose work bridges advanced computing and biomedical intelligence. He began his academic journey in applied physics and electronic engineering at a leading national university before advancing into graduate study in the same field and later joining a prestigious Japanese institution as a doctoral research fellow. His professional experience includes serving as an assistant professor on study leave at Jamalpur Science and Technology University, where he previously held key administrative roles such as department chair and additional director of the ICT cell. His research interests span biomedical signal and image processing, bioinformatics, brain–computer interfaces, machine learning, and deep learning, with a strong record of publications in national and international venues. He possesses skills in algorithm development, data modeling, neural network design, statistical learning, digital imaging techniques, and computational intelligence. His contributions have earned recognition through academic responsibilities, research achievements, and active involvement in scientific communities. He continues to collaborate globally, contributing to interdisciplinary advancements that support healthcare analytics and intelligent systems. Overall, he is a committed researcher and educator whose work reflects dedication to scientific progress, innovation, and the development of impactful computational technologies.

Profile: Googlescholar

Featured Publications

Islam, M. R., Mojumder, M. R. H., Moshwan, R., Jannatul Islam, A. S. M., Islam, M. A., Rahman, M. S., & Kabir, M. H. (2022). Strain-Driven Optical, Electronic, and Mechanical Properties of Inorganic Halide Perovskite CsGeBr₃. ECS Journal of Solid State Science and Technology, 11(3), 033001

Kabir, M. H., Mahmood, S., Al Shiam, A., Musa Miah, A. S., Shin, J., & Molla, M. K. I. (2023). Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification. Mathematics, 11(8), 1921.

Shin, J., Musa Miah, A. S., Kabir, M. H., Rahim, M. A., & Al Shiam, A. (2024). A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities. IEEE Access.

 

HMIMSA Younes | Application of Artificial Intelligence in Agriculture | Best Researcher Award

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Prof. HMIMSA Younes | Application of Artificial Intelligence in Agriculture | Best Researcher Award

Abdelmalek Essaadi University | Morocco

Professor Younes Hmimsa is a distinguished Moroccan academic and researcher serving as a Full Professor at the Polydisciplinary Faculty of Larache, Abdelmalek Essaadi University. His academic background spans animal biology, plant biotechnology, ecology, and wealth management, reflecting a multidisciplinary approach to environmental and agricultural sciences. With advanced degrees from the University of Tetouan and the University of Alicante, he has developed extensive expertise in plant biology, agroecology, and biodiversity conservation. Professionally, he has been a key figure in multiple international research programs such as PRIMA, ARIMNET, and EVOlea, focusing on agrobiodiversity, climate resilience, and sustainable agricultural systems. His research interests include plant phenology, genetic diversity, agroecosystem sustainability, and the socio-ecological dynamics of traditional farming systems in Mediterranean regions. He possesses strong research skills in agromorphological characterization, environmental data modeling, and interdisciplinary collaboration. Professor Hmimsa has coordinated numerous international conferences, seminars, and research networks that bridge scientific innovation and rural development. His scholarly achievements are complemented by prestigious roles as a reviewer, evaluator, and collaborator with global research institutions. A recipient of several academic honors and author of influential publications, he continues to advance sustainable agricultural practices and ecological research in Morocco and beyond, inspiring both scientific and community engagement.

Profile: ORCID

Featured Publications

Zhengyi Yao | Artificial Intelligence | Best Researcher Award

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Mr. Zhengyi Yao | Artificial Intelligence | Best Researcher Award

Sichuan Normal University |China

Zhengyi Yao, from Neijiang, Sichuan, China, is a dedicated researcher affiliated with Sichuan Normal University, holding both bachelor’s and master’s degrees in Computer Science and Technology. His work primarily focuses on the Internet of Things (IoT), cybersecurity, cryptography, and artificial intelligence (AI). With a growing presence in academic publishing, he has contributed to several high-impact journals indexed in SCI and Scopus. Mr. Yao has demonstrated a strong commitment to advancing secure, intelligent systems, particularly in logistics and industrial applications. His interdisciplinary approach blends theoretical research with practical implementation, contributing to emerging technologies such as blockchain-enabled IIoT and quantum cryptography. In addition to publishing five journal articles and securing seven patents, he actively contributes to the field through applied innovations aimed at enhancing privacy protection and data security. As a passionate technologist, Mr. Yao is continually exploring transformative solutions in smart systems, emphasizing the ethical and secure integration of AI in modern digital infrastructure.

Profile

Education

Zhengyi Yao completed his academic training at Sichuan Normal University, earning both his bachelor’s and master’s degrees in Computer Science and Technology. His undergraduate studies provided a solid foundation in software development, algorithms, and system architecture, while his postgraduate work emphasized advanced topics such as artificial intelligence, cybersecurity, and cryptographic methods. During his graduate years, he engaged deeply with interdisciplinary studies, aligning computer science with real-world applications in logistics, IoT, and secure communication systems. His academic performance has been marked by consistent excellence and a proactive engagement in research-driven projects. While enrolled, he also explored the practical aspects of emerging technologies, developing tools and frameworks to support digital transformation in industrial systems. His education has been instrumental in shaping his scientific outlook, fostering a commitment to ethical innovation and robust digital security. These academic experiences continue to inform his contributions to academic research and patent development in the tech and security domains.

Experience

Zhengyi Yao has gained substantial experience as a researcher and innovator in the fields of IoT, cybersecurity, cryptography, and AI. While at Sichuan Normal University, he actively participated in multiple collaborative research efforts that examined the integration of blockchain with IIoT systems and privacy-focused AI applications in logistics. Despite limited consultancy and editorial appointments, his practical contributions are demonstrated through five SCI/Scopus-indexed journal publications and seven patents. He has co-authored research tackling challenges in smart logistics security, 5G-based blockchain sensors, and quantum cryptography, showcasing his capability to bridge theoretical and applied computing. Through independent and team-driven efforts, Mr. Yao has contributed to designing secure systems that support data integrity and user privacy in dynamic industrial environments. His hands-on research experience, supported by solid academic training, underpins his drive to innovate in secure computing technologies and has positioned him as a promising young professional in China’s growing digital research landscape

Research Focus

Zhengyi Yao’s research centers on the intersection of emerging technologies like IoT, blockchain, AI, and cybersecurity, with a strong focus on intelligent logistics systems. He explores secure device communication, privacy-preserving data protocols, and cryptographic models for industrial systems. His work on blockchain-enabled IIoT platforms aims to fortify command operations against cyber threats, while his investigations into quantum cryptography are pushing the boundaries of next-generation digital security. One of his key contributions is the development of 5G-based universal blockchain smart sensors, combining speed, scalability, and trust for dynamic logistics applications. His research also examines how AI can be ethically and securely integrated into cyber-physical environments to optimize data flow, user privacy, and system integrity. Through published works and patented innovations, he is shaping solutions to critical security challenges facing smart logistics and industrial platforms. His forward-thinking approach promotes safer, more resilient infrastructures in an increasingly connected digital world.

Publications

Sensitive Data Privacy Protection of Carrier in Intelligent Logistics System
Year: 2024
Citation:2

Blockchain-enabled device command operation security for Industrial Internet of Things
Year: 2023
Citation:12

5G-BSS: 5G-Based Universal Blockchain Smart Sensors
Year: 2022
Citation:2

Conclusion

Zhengyi Yao exemplifies the qualities of a dedicated and innovative researcher, with notable contributions to smart logistics, cybersecurity, and cryptographic technologies. His blend of academic rigor and applied invention positions him as a rising leader in secure digital systems.

Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

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Dr. Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

Dr. Chan-Uk Yeom is a Research Professor at the Research Institute of IT, Chosun University, Korea. He specializes in time series data analysis using deep learning, granular computing, adaptive neuro-fuzzy inference systems, high-dimensional data clustering, and biosignal-based biometrics. Dr. Yeom has held several research positions, including at the Division of AI Convergence College at Chosun University and the Center of IT-BioConvergence System Agriculture at Chonnam National University. His work integrates artificial intelligence, fuzzy systems, and granular models for practical applications such as healthcare, biometrics, and energy efficiency. Dr. Yeom has published extensively in high-impact journals and conferences, holds multiple patents, and has received numerous awards for his innovative research contributions. He actively teaches courses related to AI healthcare applications and electronic engineering. His collaboration and problem-solving skills have been demonstrated through his involvement in competitive AI research challenges and global innovation camps.

Professional Profile

Education

Dr. Yeom completed his entire higher education at Chosun University, Korea. He earned his Ph.D. in Engineering (2022) from the Department of Control and Instrumentation Engineering, with a dissertation on fuzzy-based granular model design using hierarchical structures under the supervision of Prof. Keun-Chang Kwak. Prior to this, he obtained his M.S. in Engineering (2017), focusing on ELM predictors using TSK fuzzy rules and random clustering, and his B.S. in Engineering (2016) in Control and Instrumentation Robotics. His academic work laid a strong foundation in machine learning, granular computing, and fuzzy inference systems, which became the core of his future research trajectory. Throughout his education, Dr. Yeom demonstrated academic excellence, leading to multiple thesis awards, and developed expertise in AI-driven applications for healthcare, energy optimization, and biometrics.

Experience

Currently, Dr. Yeom serves as a Research Professor at the Research Institute of IT, Chosun University (since January 2025). Previously, he was a Research Professor at Chosun University’s Division of AI Convergence College (2023–2024) and a Postdoctoral Researcher at the Center of IT-BioConvergence System Agriculture, Chonnam National University (2022–2023). His extensive research spans user authentication technologies using multi-biosignals, brain-body interface development using AI multi-sensing, and optimization of solar-based thermal storage systems. In addition to research, Dr. Yeom has contributed to teaching undergraduate courses, including AI healthcare applications, electronic experiments, capstone design, and open-source software. He is also experienced in mentorship, student internships, and providing special employment lectures. His active participation in national and international research projects and conferences reflects his global engagement and multidisciplinary expertise in artificial intelligence, healthcare, biometrics, and advanced fuzzy models.

Research Interests

Dr. Yeom’s research integrates deep learning, granular computing, and adaptive neuro-fuzzy systems to solve complex problems in healthcare, biometrics, energy efficiency, and time series data analysis. His innovative work focuses on designing hierarchical fuzzy granular models, developing incremental granular models with particle swarm optimization, and applying AI-driven methods to biosignal-based biometric authentication. Dr. Yeom has developed cutting-edge models for predicting energy efficiency, vehicle fuel consumption, water purification processes, and disease classification from ECG signals. His contributions also extend to explainable AI, emotion recognition, and non-contact biosignal acquisition using 3D-CNN. In addition to academic publications, he has secured multiple patents related to ECG-based personal identification methods, intelligent prediction systems, and granular neural networks. His interdisciplinary approach combines theoretical modeling, real-world applications, and collaborative AI system design, advancing the fields of biomedical informatics, neuro-fuzzy computing, and healthcare convergence technologies.

Awards

Dr. Yeom has received numerous awards recognizing his academic excellence. He earned multiple Excellent Thesis Awards from prestigious conferences, including the International Conference on Next Generation Computing (ICNGC 2024), the Korea Institute of Information Technology (KIIT Autumn Conference 2024), and the Annual Conference of Korea Information Processing Society (ACK 2024). His doctoral work was recognized at Chosun University’s 2021 Graduate School Doctoral Degree Award Ceremony. He also received the Outstanding Presentation Paper Award at the 2020 Korean Smart Media Society Spring Conference and the Excellent Thesis Award at the Korea Information Processing Society 2018 Spring Conference. Earlier, his problem-solving capabilities were showcased as a finalist and top 9 team at the 2018 AI R&D Challenge and during participation in the 2016 Global Entrepreneurship Korea Camp. These honors highlight his sustained contributions to AI research, innovation, and applied technological development.

Conclusion

Dr. Chan-Uk Yeom is a dynamic researcher whose pioneering contributions to granular computing, neuro-fuzzy systems, and AI healthcare applications demonstrate his exceptional expertise, innovative thinking, and global scientific impact, making him a valuable contributor to the advancement of next-generation intelligent systems.

 Publications

  • A Design of CGK-Based Granular Model Using Hierarchical Structure

    Applied Sciences
    2022-03 | Journal article | Author
    DOI: 10.3390/app12063154
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

  • Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

    Applied Sciences
    2020-11 | Journal article | Author
    DOI: 10.3390/app10238495
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

Elyas Rostami | Mechanical Engineering | Best Researcher Award

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Dr. Elyas Rostami | Mechanical Engineering | Best Researcher Award

Dr. Elyas Rostami is a distinguished faculty member at Buein Zahra Technical and Engineering University, specializing in Aerospace Engineering 🚀. With a solid academic background, he earned his B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, followed by an M.Sc. in Aerospace Engineering ✈️ from Tarbiat Modares University, and completed his PhD 🎓 at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles 📄, books 📚, and research projects 🔍 to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization 💧, fluid mechanics 🌊, and sustainable energy applications ⚡. As a dedicated educator 👨‍🏫 and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations 🤝, publishing, and mentoring.

Profile

Education 🎓

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, laying the foundation for his technical expertise. He pursued his M.Sc. in Aerospace Engineering ✈️ at Tarbiat Modares University, where he refined his knowledge in aerodynamics, propulsion, and fluid mechanics 🌬️. Driven by academic excellence, he earned his Ph.D. in Aerospace Engineering 🚀 from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems 🔥, and energy-efficient solutions 💡. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems 🔧. Through rigorous coursework, experimental research, and publications 📝, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠💪, enabling him to address modern aerospace challenges with innovative thinking 🚀.

Experience 👨‍🏫

Dr. Elyas Rostami serves as a faculty member 👨‍🏫 at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering 🚀. His career spans academic teaching 📖, research leadership 🔬, article peer reviewing 🧐, and supervising research projects 🔍. He has authored multiple scientific papers 📝 and books 📚, advancing the field of propulsion systems, spray technology 💧, and fluid mechanics 🌊. Beyond academia, Dr. Rostami actively engages with industry via research collaborations 🤝 and applied projects that explore new energies 🌱 and aerospace applications ✈️. His professional journey demonstrates versatility and dedication, contributing both as an educator and innovator 🎓⚙️. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions 🏅

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction 🎖️. His scholarly contributions earned him recognition through research publications in reputable journals 🏅, invitations to review articles 🧐, and participation in collaborative research projects 🤝. He has authored a scientific book 📘 and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion 🔥 and fluid dynamics 🌊 has positioned him as a valued academic and research professional. The depth of his work reflects both national and international acknowledgment, solidifying his reputation as a committed and impactful researcher 🌍. Dr. Rostami’s work embodies passion for engineering, teaching excellence, and scientific advancement 💡🔬.

Research Interests 🔬

Dr. Elyas Rostami’s research focuses on fluid mechanics 🌊, aerospace propulsion systems 🚀, spray and atomization processes 💧, and the use of renewable energies in industrial applications 🌱. His investigations address the thermodynamics of propulsion systems 🔥, advancing efficiency and performance in aerospace technology ✈️. He integrates experimental and computational approaches 🖥️ to explore new energy solutions and optimize spray behavior under different conditions ⚙️. Through research projects 🔬, journal articles 📄, and book authorship 📚, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation 💡, making significant strides in the development of sustainable and high-performance propulsion technologies 🌍🚀.

Publications 

Guoliang Wang | Control Science and Engineering | Best Researcher Award

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Prof. Guoliang Wang | Control Science and Engineering | Best Researcher Award

 Guoliang Wang is a Professor at the Department of Automation, School of Information and Control Engineering, Liaoning Petrochemical University. 📚 With extensive expertise in control theory and automation, he has made significant contributions to Markov jump systems, stochastic system theory, and big data-driven fault detection. 🚀 He has published 86 journal articles indexed in SCI and Scopus, authored books, and holds 9 patents. 🏅 As a postdoctoral researcher at Nanjing University of Science and Technology (2011-2016), he furthered his research in control engineering. 🎓 His professional memberships include the Chinese Association of Automation and the Chinese Mathematical Society. 🏆 He has received the Liaoning Province Natural Science Academic Achievement Second Prize for his contributions. His innovative work in optimization, reinforcement learning, and system modeling continues to impact academia and industry. 🌍

Profile

Education 🎓

Guoliang Wang earned his Ph.D. in Control Theory and Control Engineering from Northeastern University (2007-2010). 🎓 Prior to that, he completed his Master’s degree in Operations Research and Control Theory at the School of Science, Northeastern University (2004-2007). 📖 His academic foundation is built on advanced mathematical modeling, stochastic systems, and automation, equipping him with expertise in complex system analysis. 🏗️ His research has consistently focused on optimizing control mechanisms and enhancing stability in dynamic environments. As a Postdoctoral Researcher at Nanjing University of Science and Technology (2011-2016), he deepened his understanding of control theory, reinforcement learning, and system dynamics. 🏅 His education has been pivotal in developing innovative methodologies for automation, fault detection, and big data-driven decision-making.

Experience 👨‍🏫

Since March 2010, Guoliang Wang has been a Professor at Liaoning Petrochemical University, specializing in automation and control engineering. 🏫 He served as Associate Dean of the Department of Automation (2013-2014), leading academic and research initiatives. 🌍 His postdoctoral research at Nanjing University of Science and Technology (2011-2016) explored stochastic system applications and control theory advancements. 🔬 Over the years, he has led multiple research projects, consulted on industrial automation solutions, and contributed to major technological advancements. 💡 His work has resulted in 86 peer-reviewed journal publications, 9 patents, and significant contributions to adaptive dynamic programming. 🚀 As a member of various professional associations, he actively collaborates with international researchers and institutions. His expertise spans Markov jump systems, stochastic modeling, fault detection, and AI-driven automation strategies

Research Interests 🔬

Guoliang Wang’s research spans modeling and control of Markov jump systems, stochastic system applications, and AI-driven automation. 🤖 His work in fault detection, diagnosis, and big data-driven prediction has led to practical advancements in system optimization. 📊 He has proposed novel stabilizing controllers, developed reinforcement learning-based optimization models, and improved system performance through convex optimization techniques. 🔍 His expertise in stochastic control extends to image processing, predictive analytics, and adaptive dynamic programming. 📡 His research contributions have significantly enhanced system stability and reduced computational complexity in industrial automation. 💡 Through collaborations with global researchers, he continues to push the boundaries of automation, AI, and smart control systems. 🚀 His work integrates theoretical insights with real-world applications, ensuring a lasting impact on engineering and technology. 🌍

Guoliang Wang has been recognized for his outstanding contributions to automation and control engineering. 🏆 He received the prestigious Liaoning Province Natural Science Academic Achievement Second Prize for his groundbreaking research. 🏅 His achievements include being a member of elite research committees such as the Youth Committee of the Chinese Association of Automation and the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee. 🎖️ He has been an invited speaker at international conferences and has received commendations for his work in optimizing stochastic system control. 📜 His research impact is further reflected in his editorial board membership at the Journal of Liaoning Petrochemical University. ✍️ With numerous patents, consultancy projects, and high-impact research, he continues to receive nominations and accolades in automation, AI, and control system optimization

Publications 📚

  • Sampled-Data Stochastic Stabilization of Markovian Jump Systems via an Optimizing Mode-Separation Method

    IEEE Transactions on Cybernetics
    2025 | Journal article
    DOI: 10.1109/TCYB.2025.3534268
    CONTRIBUTORS: Guoliang Wang; Yaqiang Lyu; Guangxing Guo

  • Stabilization of Stochastic Markovian Jump Systems via a Network-Based Controller

    IEEE Transactions on Control of Network Systems
    2024-03 | Journal article
    DOI: 10.1109/TCNS.2024.3355044
    CONTRIBUTORS: Guoliang Wang; Siyong Song; Zhiqiang Li

  • Almost Sure Stabilization of Continuous-Time Semi-Markov Jump Systems via an Earliest Deadline First Scheduling Controller

    IEEE Transactions on Systems, Man, and Cybernetics: Systems
    2024-01 | Journal article
    DOI: 10.1109/TSMC.2023.3318610
    CONTRIBUTORS: Guoliang Wang; Yunshuai Ren; Zhiqiang Li

  • Stability and stabilisation of Markovian jump systems under fast switching: an averaging approach

    Journal of Control and Decision
    2023-10-02 | Journal article
    DOI: 10.1080/23307706.2022.2127427
    CONTRIBUTORS: Guoliang Wang; Yande Zhang; Yunshuai Ren

Mark Munger | Co-Curricular Learning | Best Researcher Award

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Dr. Mark Munger | Co-Curricular Learning | Best Researcher Award

Mark A. Munger, Pharm.D., F.C.C.P., F.A.C.C., F.H.F.S.A., HF-Cert., is a distinguished pharmacologist specializing in cardiovascular therapeutics. He is a Professor of Pharmacotherapy at the University of Utah, with extensive experience in clinical pharmacology, education, and health policy. Dr. Munger has served on numerous advisory boards, contributed to health policy initiatives, and played a key role in pharmacy education. His work spans research, teaching, and leadership roles, shaping advancements in cardiovascular pharmacotherapy. He is widely recognized for his contributions to healthcare, education, and clinical practice.

Profile

Education 🎓

Dr. Munger earned his Doctor of Pharmacy (1986) from the University of Illinois, a Bachelor of Science in Pharmacy (1983) from Oregon State University, and completed a Cardiovascular Pharmacology Research Fellowship (1986-87) at Case Western Reserve University and University Hospitals of Cleveland. His academic journey laid the foundation for his impactful career in cardiovascular research, clinical pharmacology, and pharmacy education.

Experience 👨‍🏫

Dr. Munger has held numerous leadership roles, including Associate Dean at the University of Utah College of Pharmacy (2003-2023), Director of Clinical Pharmacology at UTAH Heart Failure Prevention Program (2000-2012), and Chairperson of the Utah State Board of Pharmacy (2002-2004). His expertise has influenced health policies, clinical research, and pharmacy education. He has also served as a consultant and advisory board member for various healthcare and government organizations.

Research Interests 🔬

Dr. Munger’s research centers on cardiovascular pharmacotherapy, heart failure management, and drug safety. His work has contributed to advancements in pharmacological treatments for cardiovascular diseases, optimizing drug therapy for heart failure patients, and policy-making in healthcare. His studies integrate clinical trials, translational research, and healthcare innovations, enhancing patient outcomes and shaping pharmaceutical practices.

Dr. Munger has received numerous accolades, including Fellowships from the American College of Cardiology (2008), Heart Failure Society of America (2016), and American College of Clinical Pharmacy (1996). He was named University of Illinois College of Pharmacy Alumni of the Year (2008), earned multiple Teacher of the Year nominations, and received the Utah Society of Health-System Pharmacists Leadership Award (2009). His contributions to research and education have been recognized globally.

Publications 📚

  • Association of atrial fibrillation with lamotrigine: An observational cohort study

    Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy
    2025-01 | Journal article
    DOI: 10.1002/phar.4633
    CONTRIBUTORS: Sodam Kim; Landon Welch; Bertha De Los Santos; Przemysław B. Radwański; Mark A. Munger; Kibum Kim

  • Riluzole is associated with reduced risk of heart failure

    European Journal of Neurology
    2025-01 | Journal article
    DOI: 10.1111/ene.70033
    CONTRIBUTORS: Kibum Kim; Sodam Kim; Margaret Katana; Dmitry Terentyev; Przemysław B. Radwański; Mark A. Munger

  • Ivabradine and Atrial Fibrillation Incidence: A Nested Matching Study

    2025-01-12 | Preprint
    DOI: 10.1101/2025.01.10.25320367
    CONTRIBUTORS: Kibum Kim; Jasmeen Keur; Halie Anderson; Przemysław B. Radwański; Mark A. Munger

  • Association of Ventricular Arrhythmias with Lamotrigine: An Observational Cohort Study

    2024-09-24 | Preprint
    DOI: 10.1101/2024.09.10.24313446
    CONTRIBUTORS: Sodam Kim; Landon Welch; Bertha De Los Santos; Przemysław B. Radwański; Mark A. Munger; Kibum Kim

Yangyang Huang | Object detection | Excellence in Innovation

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Dr. Yangyang Huang | Object detection | Excellence in Innovation

Yangyang Huang is a Ph.D. student at the School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China. His research focuses on artificial intelligence, computer vision, and large models. He previously graduated from Wuhan University, where he developed a strong foundation in AI and computational sciences. Yangyang has contributed to significant research projects, including the Collaborative Innovation Major Project for Industry, University, and Research. His work, “LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling,” has gained notable citations. Passionate about AI advancements, he actively participates in academic collaborations and professional memberships, contributing to AI-driven innovations.

Profile

Education 🎓

Yangyang Huang completed his undergraduate studies at Wuhan University, where he gained expertise in artificial intelligence and computational sciences. Currently, he is pursuing his Ph.D. at the School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China. His doctoral research focuses on large vision models, unsupervised modeling, and object detection. He has been involved in cutting-edge AI research, particularly in deep learning and computer vision. His academic journey has been marked by significant contributions to AI-driven innovations, leading to multiple publications in high-impact journals. Yangyang actively collaborates with researchers in academia and industry, further strengthening his expertise in AI and machine learning applications.

Experience 👨‍🏫

Yangyang Huang has extensive research experience in artificial intelligence, computer vision, and large models. As a Ph.D. student at SCUT, he has been involved in the Collaborative Innovation Major Project for Industry, University, and Research. His research contributions include developing large vision models for open-world object detection, leading to highly cited publications. Yangyang has also participated in consultancy and industry projects, applying AI techniques to real-world problems. He has authored several journal articles indexed in SCI and Scopus and has contributed to the academic community through editorial roles. His collaborative research efforts have led to impactful AI advancements, making him a rising scholar in the field of AI and machine learning.

Research Interests 🔬

Yangyang Huang’s research primarily focuses on artificial intelligence, computer vision, and large models. His recent work, “LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling,” explores novel AI techniques for enhancing object detection capabilities. He specializes in deep learning, unsupervised learning, and AI-driven automation. His research interests include developing robust AI models for real-world applications, advancing AI ethics, and improving AI interpretability. Yangyang actively collaborates with academia and industry to bridge the gap between theoretical AI research and practical applications. His contributions extend to consultancy projects, AI innovation, and scholarly publications, making him a key contributor to AI advancements. 🚀

Awards & Recognitions 🏅

Yangyang Huang has received recognition for his outstanding contributions to artificial intelligence and computer vision. His research on large vision models and open-world object detection has been widely cited, earning him academic recognition. He has been nominated for prestigious research awards, including Best Researcher Award and Excellence in Research. His work in AI has been acknowledged through various grants and funding for industry-academic collaborative projects. Yangyang’s active participation in international conferences has led to best paper nominations and accolades for his innovative contributions. He is a member of esteemed professional organizations, further cementing his reputation as an emerging AI researcher.

Publications 📚

Lichen Shi | Mechanical Engineering | Best Researcher Award

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Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

 

Profile

Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

  • Developing an AI-based method for reading pointer meters using human-like reading sequences.
  • Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
  • Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
  • Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
  • Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

  • Intelligent Measurement with Deep Learning
  • Equipment Status Monitoring and Fault Diagnosis
  • Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

  • [1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
  • [2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) ：25186-25196. doi:
    10.1109/JSEN.2024.3417231
  • [3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
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Kaveri Hatti | Engineering| Women Researcher Award

Published on by Cognitive Scientist Awards

Mrs. Kaveri Hatti | Engineering| Women Researcher Award

 

 

Profile

Education

averi Hatti is a dedicated researcher and educator in the field of VLSI Design, Embedded Systems, and Hardware Security. She is currently pursuing a Ph.D. at Amrita School of Engineering, Bangalore, focusing on FPGA-based security architectures. With a strong academic background, she holds an M.Tech in VLSI Design and Embedded Systems from VTU Regional Office, Gulbarga, and a B.Tech in Electronics and Communication Engineering from SLN College of Engineering, Raichur.

Kaveri has extensive teaching experience, having served as a Lecturer at Tagore Memorial Polytechnic College and Government Polytechnic College in Raichur before joining Amrita School of Engineering, Bangalore, as a Teaching Assistant in 2022. Her expertise lies in FPGA design, Verilog, RTL design, and hardware security implementations, utilizing tools like Xilinx ISE, VIVADO, ModelSim, and Cadence.

 

Work experience

Kaveri Hatti has a strong background in academia, with extensive teaching experience spanning several years. She began her career as a Lecturer at Tagore Memorial Polytechnic College, Raichur, from August 2009 to December 2012, where she played a key role in instructing and mentoring students in electronics and communication engineering. Simultaneously, she also served as a Lecturer at Government Polytechnic College, Raichur, from June 2009 to June 2011. Currently, she is working as a Teaching Assistant at Amrita School of Engineering, Bangalore, since February 2022, contributing to research and assisting in the academic development of students in the field of VLSI Design and Embedded Systems.

Publication