Junping Hong |Data science | Best Researcher Award

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Dr. Junping Hong |Data science | Best Researcher Award

Tsinghua University, China

Junping Hong is a doctoral student at Tsinghua University specializing in Data Science and Information Technology. With a solid academic foundation from both Lanzhou University and Tsinghua University, he has cultivated expertise in Bayesian learning and time series analysis on graphs. His research contributions include impactful publications in leading journals such as IEEE Transactions on Signal and Information Processing over Networks and Entropy. Junping’s scholarly work reflects his commitment to advancing knowledge in statistical modeling and neural networks. In addition to his research, he has served as a teaching assistant in Bayesian learning and contributed as a reviewer for prestigious conferences including ICASSP and ICLR.

Profile

Scopus

🎓 Education 

Junping Hong holds a Bachelor’s degree in Computer Science from Lanzhou University (2008–2012), a Master’s degree in Data Science from Tsinghua University (2019–2022), and is currently pursuing his Ph.D. at Tsinghua University (2023–present). His academic path reflects a strong progression in quantitative analysis, machine learning, and statistical inference. During his Master’s and Ph.D. training, Junping has delved into specialized topics like Bayesian learning and time series forecasting, building a strong foundation for academic research and practical applications in data science. His academic tenure at one of China’s leading institutions supports his ongoing contributions to the field.

💼 Experience 

Junping Hong has gained valuable academic and research experience throughout his graduate studies. He has worked as a teaching assistant for a course on Bayesian Learning, where he provided instructional support and helped students grasp advanced statistical concepts. Junping also has peer-review experience, having reviewed submissions for major international conferences such as ICASSP and ICLR, which reflects his standing within the academic community. His research experience spans areas like time series forecasting and Bayesian neural networks, and he actively contributes to high-impact journals. These roles underline his deep involvement in the academic and research ecosystem.

🏅 Awards and Honors 

While specific awards or honors are not listed, Junping Hong’s publication “Multivariate time series forecasting with GARCH models on graphs” was recognized among the Top 25 most downloaded articles in IEEE Transactions on Signal and Information Processing over Networks between September 2023 and September 2024. This achievement highlights the significance and relevance of his research within the global academic community. Furthermore, his role as a reviewer for top-tier conferences and his involvement in cutting-edge machine learning research emphasize his emerging reputation in the field of data science.

🔬 Research Focus 

Junping Hong’s research centers on Bayesian Learning and time series analysis on graphs and networks. His work addresses key challenges in predictive modeling and uncertainty estimation by integrating Bayesian inference with graph-based methods. His 2025 publication in Entropy on Minimax Bayesian Neural Networks showcases his interest in combining probabilistic reasoning with deep learning for robust decision-making. Junping also explores the use of GARCH models for multivariate time series forecasting in structured data environments, such as graphs, demonstrating his ability to work across theoretical and applied dimensions of data science. His research aims to advance both the interpretability and performance of machine learning systems.

đź“ť Conclusion

Dr. Junping Hong is a highly promising researcher with strong academic training, impactful publications, and a clear focus on high-value research areas in data science and Bayesian learning. His ongoing work at Tsinghua University and involvement in top-tier academic venues underline his potential for long-term contributions to the field. While still in the early stages of his Ph.D., his trajectory suggests significant promise. With more leadership roles, real-world implementation, and recognition, he would be an excellent candidate for the Best Researcher Award – General Category, especially in the emerging researcher segment.

Publication

  • Title: Entropy Map Might Be Chaotic
    Year: 2021
    Authors: J. Hong, W. Kin

 

  • Title: Multivariate Time Series Forecasting with GARCH Models on Graphs
    Year: 2023
    Authors: J. Hong, Y. Yan, E. E. Kuruoglu, W. K. Chan

 

  • Title: Minimax Bayesian Neural Networks
    Year: 2025
    Authors: J. Hong, E. E. Kuruoglu

 

Loay Aladib | Information Sciences |Young Scientist Award

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Mr. Loay Aladib | Information Sciences |Young Scientist Award

PhD Research Scholar, University of Wollongong

Loay Aladib is a PhD Research Scholar at the University of Wollongong, Australia, specializing in Software Engineering and Artificial Intelligence. With 12+ years of experience, he has led software teams to build efficient, elegant solutions across diverse platforms. His core strength lies in blending practical software design with deep technical research. Loay is currently immersed in developing advanced frameworks that integrate Runtime Verification with stream processing systems like Apache Spark. His work ensures scalable, real-time monitoring of distributed systems, driving innovations in safety-critical applications such as IoT and smart cities. Adept in various languages, frameworks, and cloud platforms, Loay exemplifies the role of a modern software architect and researcher. His commitment to excellence, adaptability, and problem-solving mindset has positioned him at the forefront of next-generation computing solutions. Loay is not only a passionate coder but also a visionary problem solver working at the intersection of code, logic, and impact.

Profile

Scholar

🎓 Education 

 Loay Aladib is currently pursuing a PhD in Software Engineering and Artificial Intelligence at the University of Wollongong, focusing on real-time distributed systems and formal verification techniques. His doctoral research bridges theoretical concepts like Linear Temporal Logic with practical big data stream frameworks. Before this, Loay earned his bachelor’s degree in computer science/software engineering, followed by a postgraduate specialization (details not provided). His academic journey is driven by a desire to enhance software system reliability through intelligent automation and scalable architecture. Loay’s education is marked by continuous learning, technical proficiency, and interdisciplinary exploration, equipping him with the skills to address complex challenges in AI and software development. His PhD candidacy reflects his commitment to research excellence, innovation, and the application of formal methods in real-world computing environments. Through rigorous academic training and practical insight, Loay has developed a unique profile combining academic depth with industry-savvy problem-solving capabilities.

💼 Experience 

 With 12+ years of robust professional experience, Loay Aladib has led software development teams across various tech sectors, delivering high-performance, scalable solutions tailored to business and user needs. His role has spanned full-stack development, system architecture, and collaborative product engineering. Loay consistently combines technical depth with strategic vision, enabling teams to innovate rapidly while maintaining code quality and performance. Beyond corporate roles, his current work as a PhD researcher involves designing AI-integrated monitoring systems for distributed data streams, showcasing a unique fusion of industry expertise with academic rigor. Loay is proficient in cloud platforms, diverse programming languages, and modern software engineering practices. He brings creativity and adaptability to every project, turning technical constraints into scalable solutions. His leadership experience includes mentoring junior developers, managing cross-functional teams, and aligning technical goals with organizational objectives. Loay thrives in dynamic, challenge-driven environments, always pushing boundaries in software reliability and intelligent system design.

🏅 Awards and Honors 

 Loay Aladib has been nominated for prestigious awards such as the Best Researcher Award and Young Scientist Award in recognition of his impactful contributions to the intersection of software engineering and artificial intelligence. While specific award wins are yet to be listed, his nomination itself speaks volumes about the originality, utility, and future relevance of his work. His real-time verification framework for Apache Spark has received positive attention from peers and professionals alike for enhancing system reliability in distributed data environments. Loay’s IEEE membership also signifies his recognition and affiliation within a global community of innovators. His work is not just academic but designed for high-impact real-world applications, particularly in areas like smart cities, IoT, and industrial automation. These contributions underscore his potential for receiving honors in innovation, research excellence, and technology leadership. With several publications and ongoing collaborations, Loay stands out as a rising star in next-gen computing research.

🔬 Research Focus 

 Loay Aladib’s research is centered on enhancing real-time reliability in distributed systems using Runtime Verification (RV) and Linear Temporal Logic (LTL). His doctoral work proposes a novel runtime framework for monitoring LTL properties in streaming environments like Apache Spark, ensuring accurate real-time detection of safety and liveness violations in complex data flows. The research bridges formal verification with practical implementation, introducing scalable and reusable LTL patterns for system developers. A key application includes real-time weather data analysis, where his model detected anomalies in temperature and wind speed conditions efficiently. Loay’s research aims to simplify the adoption of formal methods in industry by making verification tools modular, performant, and adaptable across platforms like Apache Flink and beyond. This work has high potential for applications in IoT, smart infrastructure, and mission-critical industrial monitoring. By aligning correctness with efficiency, Loay’s focus sets a new standard in stream-processing reliability and system assurance.

âś… Conclusion

Loay Aladib is a strong and promising candidate for the Young Scientist Award, especially given his interdisciplinary expertise, real-world applicability of his research, and dedication to bridging theoretical rigor with scalable implementation. While there is scope for academic profile expansion through more citations, publications, and collaborations, his current trajectory and innovations reflect significant potential. Recognizing his work through this award would not only be a timely encouragement but also propel further impactful contributions in AI-driven software engineering and distributed systems.

Publication

Author: Aladib, L. & Lee, S.P.
Title: Pattern Detection and Design Rationale Traceability: An Integrated Approach to Software Design Quality
Year: 2018
Citations: 10

Author: Aladib, L.
Title: Case Study of Object Constraints Language (OCL) Tools
Year: 2014
Citations: 3

Author: Aladib, L., Su, G., & Yang, J.
Title: Real-Time Monitoring of LTL Properties in Distributed Stream Processing Applications
Year: 2025
Citations: 0

Author: Aladib, L.
Title: Detecting Design Pattern and Tracing Its Design Rationale
Year: 2017
Citations: 0

Author: Loay, A.
Title: Detecting Design Pattern and Tracing Its Design Rationale / Loay Aladib
Year: 2017
Citations: 0

 

Author: Aladib, L.
Title: Task Management System (TMS) for University of Malaya Research Student
Year: 2015
Citations: 0

Author: Aladib, L.
Title: Case Study of Student Registration System (SRS) Domain
Year: 2015
Citations: 0

Author: Aladib, L., Fey, C.H., Ling, S.T.C., & Thamutharam, Y.N.
Title: Case Study of Online Properties Auction System (OPAS) Domain
Year: 2014
Citations: 0

Author: Aladib, L., Ling, S.T.C., Thamutharam, Y.N., Rosli, M.N. bin, & Ridzuan, E.A.
Title: Software Requirements Specification (SRS) Web Publishing System Domain
Year: 2014
Citations: 0

Ferdib Al Islam | Machine Learning | Excellence in Research

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Mr. Ferdib Al Islam | Machine Learning | Excellence in Research

Assistant Professor, Northern University of Business and Technology Khulna Bangladesh

Ferdib-Al-Islam is an Assistant Professor at Northern University of Business and Technology Khulna, Bangladesh. He holds a Master’s and Bachelor’s degree in Computer Science and Engineering from Khulna University of Engineering & Technology (KUET) and Bangabandhu Sheikh Mujibur Rahman Science and Technology University (BSMRSTU), respectively. His research expertise encompasses Machine Learning, Deep Learning, IoT, Data Science, and Computer Vision. Ferdib’s career includes experience as a software engineer in IoT R&D and lecturer roles, contributing significantly to academic and research pursuits.

Publication Profile

Google Scholar

🎓 Education

M.Sc. Eng. in Computer Science and Engineering from KUET (2023) – GPA: 3.50. B.Sc. Eng. in Computer Science and Engineering from BSMRSTU (2018) – GPA: 3.55. HSC in Science from Govt. PC College, Bagerhat (2012) – 5.00. SSC in Science from Bagerhat Govt. Secondary School (2010) – 5.00

đź’Ľ Experience

Ferdib has progressed from an intern to a Senior Lecturer, and now an Assistant Professor at Northern University of Business and Technology Khulna. He served as a Lecturer in Computer Science and Engineering from March 2020 to January 2024. His career also includes a Jr. Software Engineer role at W3 Engineers Ltd. in the IoT R&D sector. Ferdib brings practical industry experience into his academic roles, fostering innovation and research.

🏆 Awards & Honors

Ferdib has earned notable accolades, including the Gold Award at Semarak International Research Article Competition III 2024 for his work on Autism Spectrum Disorder detection. He also received the Best Paper Award at ICETIS 2021 for his research on Diabetes Mellitus prediction and the Honorable Mention Award at BDML 2020 for his IoT-based health monitoring tool.

🔬 Research Focus

Ferdib’s primary research interests are in Machine Learning, Deep Learning, IoT, Large Language Models, and Computer Vision. His work focuses on the application of AI techniques to healthcare, predictive modeling, and intelligent systems. His aim is to leverage machine learning for real-world applications like healthcare diagnostics, smart monitoring systems, and data-driven insights in various fields.

Conclusion

Ferdib Al-Islam is an exceptional researcher with notable accomplishments in machine learning, deep learning, and IoT. His commitment to advancing knowledge in these areas, demonstrated by his numerous awards and research contributions, marks him as a leading figure in his field. However, fostering greater interdisciplinary collaborations and increasing his global academic presence will be beneficial for his continued growth as a researcher. He is undoubtedly a deserving candidate for the Excellence in Research award, given his dedication, achievements, and potential for further contributions to the scientific community.

Publication Top Notes

  • Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques
    • Year: 2021
    • Authors: L Akter, Ferdib-Al-Islam, MM Islam, MS Al-Rakhami, MR Haque
    • Citation: 73
  • An IoT Enabled Health Monitoring Kit Using Non-Invasive Health Parameters
    • Year: 2021
    • Authors: A Das, SD Katha, MS Sadi, Ferdib-Al-Islam
    • Citation: 31
  • Hepatocellular Carcinoma Patient’s Survival Prediction Using Oversampling and Machine Learning Techniques
    • Year: 2021
    • Authors: Ferdib-Al-Islam, L Akter, MM Islam
    • Citation: 21
  • An Enhanced Stroke Prediction Scheme Using SMOTE and Machine Learning Techniques
    • Year: 2021
    • Authors: Ferdib-Al-Islam, M Ghosh
    • Citation: 20
  • Early Identification of Parkinson’s Disease from Hand-drawn Images using Histogram of Oriented Gradients and Machine Learning Techniques
    • Year: 2020
    • Authors: Ferdib-Al-Islam, L Akter
    • Citation: 19
  • Dementia Identification for Diagnosing Alzheimer’s Disease using XGBoost Algorithm
    • Year: 2021
    • Authors: L Akter, Ferdib-Al-Islam
    • Citation: 17
  • COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning
    • Year: 2021
    • Authors: P Saha, MS Sadi, OFMRR Aranya, S Jahan, FA Islam
    • Citation: 9
  • Detection of Hepatitis C Virus Progressed Patient’s Liver Condition Using Machine Learning
    • Year: 2022
    • Authors: Ferdib-Al-Islam, L Akter
    • Citation: 6*
  • Diabetes Mellitus Prediction and Feature Importance Score Finding Using Extreme Gradient Boosting
    • Year: 2021
    • Authors: L Akter, Ferdib-Al-Islam
    • Citation: 4
  • COV-Doctor: A Machine Learning Based Scheme for Early Identification of COVID-19 in Patients
    • Year: 2022
    • Authors: Ferdib-Al-Islam, M Ghosh
    • Citation: 3*
  • Breast Cancer Risk Prediction Using Different Clustering Techniques
    • Year: 2022
    • Authors: L Akter, M Raihan, M Raihan, M Sarker, M Ghosh, N Alvi, Ferdib-Al-Islam
    • Citation: 3
  • Crop-RecFIS: Machine Learning Classifiers for Crop Recommendation and Feature Importance Scores Calculation
    • Year: 2023
    • Authors: MS Sanim, KM Hasan, MM Alam, MAA Walid, MR Islam
    • Citation: 2
  • Prediction of Dementia Using SMOTE Based Oversampling and Stacking Classifier
    • Year: 2023
    • Authors: Ferdib-Al-Islam, MS Sanim, MR Islam, S Rahman, R Afzal, KM Hasan
    • Citation: 2*
  • An Ensemble Learning Model to Detect COVID-19 Pneumonia from Chest CT Scan
    • Year: 2022
    • Authors: PC Shill
    • Citation: 2

 

 

 

Nastaran Mehrabi Hashjin | Artificial intelligence | Best Researcher Award

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Mr. Nastaran Mehrabi Hashjin | Artificial intelligence | Best Researcher Award

Mr at Shahid beheshti university Iran

Nastaran Mehrabi Hashjin is a researcher and engineer with a background in control and electronic engineering. She specializes in AI, medical imaging, and brain-computer interfaces. With a focus on diagnosing Alzheimer’s disease and optimizing AI algorithms, she has published multiple articles in top-tier journals. Nastaran has also contributed to UAV monitoring and fault detection in power systems. A member of Iran’s National Elite Foundation, she actively engages in research on neural networks, optimization algorithms, and advanced medical data processing. She is proficient in programming, circuit design, and VLSI systems, showcasing her technical acumen.

Profile

Scopus

Orcid

Scholar

🎓 Education

Ph.D. Candidate, Control Engineering (2023-2024) Shahid Beheshti University, Tehran, Iran | Elite Foundation Member. M.Sc. in Control Engineering (2021-2024) Shahid Beheshti University, Tehran, Iran | GPA: 3.54/4.0 | Alzheimer’s diagnosis using neural networks. B.Sc. in Electronic Engineering (2016-2020) Shomal University, Amol, Iran | GPA: 3.52/4.0 | PIR sensor system design

đź’Ľ Experience

Graduate: System Identification, Non-linear Control, Modern Systems. Undergraduate: Electromagnetism, Electrical Machines, Labs Engineer, Tarashe Pardazane Jahan. Designed electronic circuits for smart doors. Intern, Mazandaran Electric Company. Energy distribution monitoring and operational map updates.

🏆 Awards and Honors

Member, Iran’s National Elite Foundation. Ph.D. admission via Elite Foundation. Certifications in neuroscience and medical imaging (TUMS). Advanced neuroscience and AI courses (Shahid Beheshti University)

🔬 Research Focus

AI-driven fault detection in power plants. Brain-computer interfaces and Alzheimer’s diagnostics. Optimization algorithms: Type-3 fuzzy logic, HO algorithm. Medical imaging: FSL, CONN, Freesurfer

🌟 Conclusion

Nastaran Mehrabi Hashjin is an exemplary candidate for the Best Researcher Award due to their innovative contributions to AI-driven fault diagnosis, optimization, and medical imaging. Their rigorous academic record, diverse expertise, and impactful publications make them a strong contender. Addressing minor gaps in global collaboration and community engagement will further enhance their standing as a leader in their field.

Publications đź“š

Bhanu Shrestha | Computer Science | Best Researcher Award

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Prof. Bhanu Shrestha | Computer Science | Best Researcher Award

Professor at Kwangwoon University, South Korea

Bhanu Shrestha is a Full Professor at Kwangwoon University, Korea. He holds expertise in electronic engineering with a background in various academic and professional roles, including Assistant and Associate Professorships at the same institution. A native of Nepal, he is passionate about research, education, and cultural exchange. Bhanu has also published books, research papers, and contributed to documentaries and music albums. His involvement extends to editorial positions, being Editor-in-Chief of the International Journal of Advanced Engineering since 2018. He is known for his academic and social contributions, particularly in the field of information technology and engineering.

 

Profile

Scopus

Orcid

 🎓 Education

Ph.D. in Electronic Engineering, Kwangwoon University, Seoul, Korea (2004-2008). M.S. in Electronic Engineering, Kwangwoon University, Seoul, Korea (2002-2004). B.S. in Electronic Engineering, Kwangwoon University, Seoul, Korea (1994-1998). S.L.C. from Bhim Secondary School, Dolakha, Nepal (1982)

đź’Ľ Experience

Adjunct Professor, Kwangwoon University, Korea (2008-2011). Assistant Professor, Kwangwoon University, Korea (2011-2016). Associate Professor, Kwangwoon University, Korea (2016-2021). Full Professor, Kwangwoon University, Korea (2021-Present). Bhanu Shrestha has held various academic positions, contributing significantly to the field of electronic engineering and nurturing the next generation of engineers. He has also been involved in leadership roles in professional organizations.

🏆 Awards and Honors

Achievement Award, IIBC Korea (2015). Best Paper Award, ISSAC 214 & ICACT 2014, IIBC, Seoul, Korea (2014). Excellent Paper Award, Korea Institute of Information Technology (2012). Certificate of Honorary Citizenship, Seong-buk District Office, Seoul, Korea (2012). Bellwave Excellent Paper Award, Korea Electromagnetic Engineering Society (2005). Vidhyabhusan Padak (Gold Medal), Nepal (2009) Bhanu has earned several prestigious awards, recognizing his academic excellence and contributions to research and development in engineering.

🔬 Research Focus

Bhanu Shrestha’s research interests include Internet of Things (IoT), artificial intelligence (AI), microelectromechanical systems (MEMS), and fuzzy logic applications in communication technology. His work also explores biosensors and nanotechnology for environmental remediation. As a guest editor for multiple journals, Bhanu’s research aims to push the boundaries of innovation in electronic engineering and its practical applications, particularly in smart systems and AI.

Conclusion

Bhanu Shrestha’s contributions to electronic engineering and technology, coupled with his leadership in academia, make him a highly deserving candidate for the Best Researcher Award. His accomplishments in publishing influential works, along with his global impact in the fields of artificial intelligence, IoT, and nanotechnology, showcase his research excellence. With continued collaboration and expansion into emerging areas, he is well-positioned to make even more significant strides in the scientific community, further cementing his legacy as a top researcher.

Publications đź“š

Selvaraj Palanisamy | Mathematics | Young Scientist Award

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Dr. Selvaraj Palanisamy | Mathematics | Young Scientist Award

Dr at Amrita Vishwa Vidyapeetham, Chennai India

Dr. Selvaraj Palanisamy is an Assistant Professor of Mathematics (Senior Grade) at Amrita Vishwa Vidyapeetham, Chennai, India. Born on April 11, 1989, he specializes in dynamical systems, stabilization, and tracking control, earning his Ph.D. from Anna University in 2017. With extensive postdoctoral research experience in South Korea, Dr. Palanisamy is an active researcher in industrial and applied mathematics, participating in global conferences and collaborations.

Profile

Scholar

🎓 Education 

Ph.D. (2014–2017): Mathematics, Anna University, Chennai. M.Phil. (2011–2013): Mathematics, Bharathiar University, Coimbatore. M.Sc. (2009–2011): Mathematics, Bharathiar University, Coimbatore (71.30%). B.Sc. (2006–2009): Mathematics, Bharathiar University, Coimbatore (86.1%).

💼 Experience 

Junior Research Fellow: Anna University, India (2014–2017). Postdoctoral Researcher: Chungbuk National University, South Korea (2017–2024). Assistant Professor: Amrita Vishwa Vidyapeetham, Chennai (2024–Present).

🏆 Awards and Honors

GATE Qualification (2013). SET for Assistant Professor (2017). CIAM Travel Grants (2019, 2023). Excellent Researcher Award, Chungbuk National University (2021). IMU Travel Grant, ICM (2022).

🔬 Research Focus 

Dr. Palanisamy’s research explores stabilization and tracking control in dynamical systems, focusing on applied and industrial mathematics.

Publications đź“š

  1. Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation
    • Journal: Neural Networks
    • Year: 2018
    • Citations: 133

 

  1. Fault-tolerant SMC for Takagi–Sugeno fuzzy systems with time-varying delay and actuator saturation
    • Journal: IET Control Theory & Applications
    • Year: 2017
    • Citations: 86

 

  1. Observer-based synchronization of complex dynamical networks under actuator saturation and probabilistic faults
    • Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
    • Year: 2019
    • Citations: 65

 

  1. Adaptive reliable output tracking of networked control systems against actuator faults
    • Journal: Journal of the Franklin Institute
    • Year: 2017
    • Citations: 62

 

  1. Robust fault-tolerant H_\infinity control for offshore steel jacket platforms via sampled-data approach
    • Journal: Journal of the Franklin Institute
    • Year: 2015
    • Citations: 59

 

  1. Modified Repetitive Control Design for Nonlinear Systems With Time Delay Based on TS Fuzzy Model
    • Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
    • Year: 2020
    • Citations: 56

 

  1. Synchronization of fractional-order complex dynamical network with random coupling delay, actuator faults and saturation
    • Journal: Nonlinear Dynamics
    • Year: 2018
    • Citations: 54

 

  1. Equivalent-input-disturbance-based repetitive tracking control for Takagi–Sugeno fuzzy systems with saturating actuator
    • Journal: IET Control Theory & Applications
    • Year: 2016
    • Citations: 53

 

  1. Disturbance and uncertainty rejection performance for fractional-order complex dynamical networks
    • Journal: Neural Networks
    • Year: 2019
    • Citations: 51

Petros Barmpas | Statistical Analysis | Best Researcher Award

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Mr. Petros Barmpas | Statistical Analysis | Best Researcher Award 

Mr at University of The, Greece

Petros Barmpas is a dedicated researcher with a strong background in computer science and biomedical informatics. He has been advancing his academic journey at the University of Thessaly since 2020, following his graduation from the University of Patras with a degree in Computer Engineering and Informatics in 2019. His career is marked by an impressive series of publications and contributions to significant projects in the fields of machine learning, clustering methodologies, and the study of healthcare informatics. Petros is married and a proud father, balancing his professional and personal life with dedication and resilience. His contributions in AI and data analysis have cemented his reputation in both academic and practical applications of computational science.

Profile

Scopus

Education 🎓

Petros Barmpas embarked on his academic career by completing high school in Kozani from 2010 to 2013. He then pursued his Bachelor’s degree in Computer Engineering and Informatics at the University of Patras, graduating in 2019. Currently, Petros is enrolled at the University of Thessaly, where he has been deepening his expertise in Computer Science and Biomedical Informatics since 2020. His educational path has equipped him with extensive knowledge and skills in computational algorithms, data analysis, and advanced informatics, supporting his impactful research and publications.

Experience 🧑‍🏫

Since the onset of his academic pursuits, Petros Barmpas has engaged in various research initiatives, focusing on the practical application of AI and machine learning. His participation in the ATHLOS project exemplifies his commitment to large-scale data analysis and unsupervised learning. Throughout his time at the University of Thessaly and during collaborations with peers, he has demonstrated exceptional expertise in hierarchical clustering, ensemble regressors, and hyperdimensional computing approaches. Petros’ professional journey showcases a consistent dedication to solving complex computational problems and advancing methodologies in biomedical informatics.

Awards and Honors 🏆

Petros Barmpas has earned recognition through numerous high-impact publications in prestigious conferences and journals. His co-authored works in IEEE Congresses and journals like the Springer Journal Health Information Science and Systems underscore his influential presence in the research community. The breadth of his collaborative work reflects the trust and respect he commands among fellow researchers, showcasing his commitment to advancing scientific understanding in AI applications and public health data analysis.

Research Focus 🔍

Petros Barmpas’ research primarily explores innovative approaches to machine learning and data clustering. His interests lie in hyperdimensional computing, hierarchical clustering ensemble methods, and the development of predictive models for healthcare studies. He has applied these techniques to diverse areas, including opioid management during the pandemic and socio-demographic analysis in large-scale studies. Through collaborations and independent work, Petros has contributed to refining algorithms that enhance prediction power and adaptability, positioning his work at the intersection of AI and public health informatics.

Conclusionđź“ť

Petros Barmpas is a distinguished researcher with significant accomplishments in computer science and biomedical informatics. His extensive publication record, innovative contributions to data clustering and health informatics, and strong collaborative efforts underscore his suitability for the Best Researcher Award. Addressing areas such as publishing in higher-impact journals and increasing international engagement could further augment his candidacy. Overall, his academic achievements, commitment to interdisciplinary research, and consistent output make him a deserving nominee for recognition.

Publications đź“š

  1. Conference Paper: Hyperdimensional Computing Approaches in Single Cell RNA Sequencing Classification
    Year: 2024
    Authors: Barmpas, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
    Source: 2024 IEEE Congress on Evolutionary Computation, CEC 2024 – Proceedings

 

  1. Conference Paper: HCER: Hierarchical Clustering-Ensemble Regressor
    Year: 2024
    Authors: Barmpas, P., Anagnostou, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
    Source: Communications in Computer and Information Science, 2141 CCIS, pp. 369–378

 

  1. Article (Open access): The Development and Validation of the Pandemic Medication-Assisted Treatment Questionnaire for the Assessment of Pandemic Crises Impact on Medication Management and Administration for Patients with Opioid Use Disorders
    Year: 2023
    Authors: Leventelis, C., Katsouli, A., Stavropoulos, V., Veskoukis, A.S., Tsironi, M.
    Source: NAD Nordic Studies on Alcohol and Drugs, 40(1), pp. 76–94

 

  1. Conference Paper: Neural Networks Voting for Projection Based Ensemble Classifiers
    Year: 2023
    Authors: Anagnostou, P., Barmpas, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
    Source: Proceedings of the 2023 IEEE International Conference on Big Data, BigData 2023, pp. 4567–4574

 

  1. Article (Open access): A Divisive Hierarchical Clustering Methodology for Enhancing the Ensemble Prediction Power in Large Scale Population Studies: The ATHLOS Project
    Year: 2022
    Authors: Barmpas, P., Tasoulis, S., Vrahatis, A.G., Plagianakos, V.P., Panagiotakos, D.
    Source: Health Information Science and Systems, 10(1), 6

Hyung-Pil Chang | Deep Learning | Best Researcher Award

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Mr. Hyung-Pil Chang | Deep Learning | Best Researcher Award

Mr at Korea University,  South Korea

Hyung-pil Chang is a dedicated graduate student at Korea University, pursuing a Ph.D. in Computer Science and Engineering. With a keen interest in deep learning and speech processing, he focuses on enhancing communication between humans and machines. He has contributed to several innovative projects in voice conversion and speech recognition, demonstrating a commitment to advancing technology in these fields. In addition to his academic pursuits, Chang actively engages in various sports and cultural activities, reflecting a well-rounded personality. His passion for research is complemented by his desire to develop practical solutions for real-world problems in artificial intelligence.

Profile

Scopus

Orcid

Scholar

Education 🎓

Hyung-pil Chang began his academic journey at Hansung University, where he earned a Bachelor of Science in Information System and Engineering from March 2014 to February 2020. He continued his studies at Korea University, obtaining a Master of Science in Computer Science and Engineering from 2020 to 2022. Currently, he is pursuing his Doctor of Philosophy in the same field at Korea University, enhancing his knowledge and expertise in deep learning, speech recognition, and human-computer interaction.

Experience đź’Ľ

Chang has gained valuable experience as a research assistant at Korea University’s Artificial Intelligence Laboratory since March 2020, working under the guidance of Prof. Dongsuk Yook. He has also served as a teaching assistant for undergraduate courses in Artificial Intelligence and Machine Learning, honing his teaching skills and sharing his knowledge with students. Additionally, he briefly worked in the Voice Generation Team at KT on a multi-modal project, where he contributed to advancements in voice conversion technologies, further solidifying his practical experience in the field.

Awards and Honors 🏆

Hyung-pil Chang has received recognition for his academic and research achievements, including publications in reputable journals such as MDPI Applied Sciences and IEEE Access. His contributions to voice conversion and speaker anonymization research have garnered attention in the field of speech processing. While specific awards are not listed, his active participation in conferences and collaboration on innovative projects highlight his commitment to excellence in research and development, positioning him as an emerging talent in artificial intelligence and deep learning.

Research Focus 🔬

Chang’s research centers on enhancing communication between people and machines, particularly in speech processing. He aims to improve speech recognition models using self-training techniques on large amounts of unlabeled data. His work also explores explainable AI and the development of a general-purpose domain agent capable of interacting with humans across various tasks, including vision and natural language processing. Key areas of focus include speech recognition, synthesis, voice conversion, and human-computer interaction, contributing to advancements in multi-modal language models.

đź“ť Conclusion

Hyung-pil Chang demonstrates strong potential as a leading researcher in deep learning and speech processing. His academic background, research contributions, and innovative spirit position him well for the Best Researcher Award. By focusing on collaboration, expanding his publication record, and engaging more with the broader community, he can enhance his impact even further. Given his current trajectory, he is well on his way to making significant contributions to his field and is a worthy candidate for recognition.

Publications Top Notes

  • Wav2wav: Wave-to-Wave Voice Conversion
    C Jeong, H Chang, IC Yoo, D Yook
    Applied Sciences, 2024, 14(10), 4251.

 

  • Zero-Shot Unseen Speaker Anonymization via Voice Conversion
    HP Chang, IC Yoo, C Jeong, D Yook
    IEEE Access, 2022, 10, 130190-130199.

 

  • CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
    D Yook, G Han, HP Chang, IC Yoo
    Applied Sciences, 2024, 14(20), 9595.

 

 

 

Rouholla Bagheri| Deep Learning | Best Researcher Award

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Assoc Prof Dr. Rouholla Bagheri | Thermodynamics | Best Researcher Award

Assoc Prof Dr. Ferdowsi University of Mashhad Iran

Dr. Rouholla Bagheri is an Assistant Professor in the Department of Management at Ferdowsi University of Mashhad, Iran. He holds a Ph.D. in Systems Management from Shahid Beheshti University, focusing on knowledge networks in the automotive sector. With a distinguished academic record, Dr. Bagheri has received numerous awards, including the 26th National Outstanding Student Award. His research interests span IoT healthcare systems, supply chain networks, and multi-objective optimization. An active member of various professional associations, he has published extensively in peer-reviewed journals, contributing significantly to the fields of information systems and management.

 

Publication profile

 

🎓 Educational Background

Ph.D. in Systems Management (2013-2018) Shahid Beheshti University, Iran Dissertation: Design a Model of Developing Knowledge Networks in the Car Engine Research Center, GPA: A+. MBA (2012) Amirkabir University, Iran, GPA: A. B.Eng. in Computer Software Engineering (2005)
Bahonar University, Iran, GPA: B

đź“š Current Professional Memberships

Member, International Scientific Committee and Editorial Review Board, World Academy of Science, Engineering, and Technology. Member, Council of Knowledge Management in Iran. Member, AIS (Association for Information Systems) of Iran. Member, AKS (Association for Knowledge Management) of Iran. Head, Business Intelligence Department, Association of Management of Iran

🏆 Honors and Awards

Distinguished Assistant Professor, Ferdowsi University (2022). National Outstanding Student Award Winner (2012, 2017). National Science Foundation Award (2013). Book of the Year Award in Information Systems and Management (2010)

Publication

    1. Assessing dimensions influencing IoT implementation readiness in industries: A fuzzy DEMATEL and fuzzy AHP analysis
      Authors: MZ Nezhad, J Nazarian-Jashnabadi, J Rezazadeh, M Mehraeen, …
      Year: 2023

     

    1. BERT-deep CNN: State of the art for sentiment analysis of COVID-19 tweets
      Authors: JH Joloudari, S Hussain, MA Nematollahi, R Bagheri, F Fazl, …
      Year: 2023

     

    1. The mediator role of KM process for creative organizational learning case study: knowledge based companies
      Authors: R Bagheri, MR Hamidizadeh, P Sabbagh
      Year: 2015

     

    1. Examining the impact of product innovation and pricing capability on the international performance of exporting companies with the mediating role of competitive advantage
      Authors: J Rezazadeh, R Bagheri, S Karimi, J Nazarian-Jashnabadi, MZ Nezhad
      Year: 2023

     

    1. The relationship of knowledge management and organizational performance in Science and Technology Parks of Iran
      Authors: MA Haghighi, R Bagheri, PS Kalat
      Year: 2015

     

    1. The Evaluation of Knowledge Management Maturity Level in a Research Organization
      Authors: R Bagheri, P Eslami, S Mirfakhraee, M Yarjanli
      Year: 2013

     

    1. Factors affecting the implementation of the blue ocean strategy: A case study of Medicom production manufacturing company
      Authors: R Bagheri, SP Eslami, M Yarjanli, N Ghafoorifard
      Year: 2013

     

    1. Modelling the factors affecting the implementation of knowledge networks
      Authors: A Rezaeian, R Bagheri
      Year: 2017

     

    1. Revolutionizing supply chain sustainability: An additive manufacturing-enabled optimization model for minimizing waste and costs
      Authors: P Roozkhosh, A Pooya, O Soleimani Fard, R Bagheri
      Year: 2024

     

    1. Robust cooperative maximal covering location problem: A case study of the locating Tele-Taxi stations in Tabriz, Iran
      Authors: H Rezazadeh, S Moghtased-Azar, MS Kisomi, R Bagheri
      Year: 2018

     

    1. Examining the Relationship between organizational Climate and Entrepreneurship with regard to Staff’s Locus of Control in Industry Companies in Iran
      Authors: R Bagheri, M Yarjanli, R Mowlanapour, N Mahdinasab
      Year: 2016

     

    1. Investigating the Effect of Perceived Ethical Leadership on Knowledge Hiding: A Case Study on an Automobile Factory
      Authors: F Imani, G Eslami, R Bagheri
      Year: 2022

    Conclusion 🎓

    Rouholla Bagheri exemplifies the qualities of a strong candidate for the Best Researcher Award, with a robust educational background, a significant publication portfolio, and numerous accolades. By focusing on applied research, enhancing collaborative efforts, and increasing public engagement, he can further amplify his impact in the field of information systems and management. His dedication to advancing knowledge and fostering innovation positions him as a valuable asset to academia and beyond.

Akarshani Amarasinghe | Artificial Intelligence | Young Scientist Award

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Ms. Akarshani Amarasinghe | Artificial Intelligence | Young Scientist Award

Lecturer at  University of Sri Jayewardenepura, Sri Lanka 

M.C. Akarshani Amarasinghe is an accomplished academic and researcher pursuing a PhD in Computer Engineering at the University of Sri Jayewardenepura, Sri Lanka. With a strong background in machine learning, image processing, and drone technology, her work focuses on innovative solutions for public health and agriculture. She has contributed to impactful research projects, such as identifying dengue mosquito breeding sites via drones and optimizing pesticide usage in arable lands. Alongside her research, Akarshani has extensive teaching experience, is a mentor for Google Summer of Code, and holds several prestigious awards for her research excellence.

Publication profile

Scholar

🎓 Higher Education

01.2024 – Present PhD in Computer Engineering, Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka Supervisors: Dr. Udaya Wijenayake, Prof. K.L. Jayaratne Research: Path planning algorithm for achieving multiple goals.. 2011 – 2016. BSc (Hons) in Computer Science, University of Colombo School of Computing, Sri Lanka Second Class Upper Division (3.25/4 – Four-Year Program)

🔬 Recent Research

D4D (Drone for Dengue) Sustainable Computing Research Group, University of Colombo School of Computing. Research on machine learning and image processing for identifying dengue mosquito breeding sites via drone images. Advisors: Prof. T.N.K. De Zoysa, Dr. C.I. Keppitiyagama. 2017 – Present GitHub Project

🧑‍🏫 Teaching Experience

03.02.2020 – Present Lecturer, Department of Computer Engineering, Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka Subjects: Operating Systems, Data Mining, Natural Language Processing, Quality Engineering, Compilers.01.01.2019 – 31.01.2020 Assistant Lecturer, Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology Subjects: Software Engineering Concepts, Programming, Distributed Systems. 01.02.2018 – 30.12.2018 Assistant Lecturer, University of Colombo School of Computing Subjects: Programming Using C, Data Structures, Operating Systems. 2018 Visiting Lecturer, National Institute of Business Management, Sri Lanka Subject: Image Processing. 02.01.2018 – 01.02.2018 Instructor, University of Colombo School of Computing

🏅 Awards and Achievements

2023 Student Research Project of the Year at the National ICT Awards, NBQSA 2023. 2022 Best Paper in AI and ML Track, ICARC 2022. 2019 N2Women Travel Grant to attend ACM SenSys 2019. 2017 N2Women Travel Grant to attend MobiSys Women’s Workshop

🏢 Professional Service

2023 – Present Treasurer, Past Pupils’ Association, Sadhu Daham Pasala, Sri Lanka. 2015 – Present Committee Member, Thumbowila Api Welfare Society. 2012 – 2016 Committee Member, AIESEC Colombo – South

Publication

  1. Identifying mosquito breeding sites via drone images
    Authors: C Suduwella, A Amarasinghe, L Niroshan, C Elvitigala, K De Zoysa, …
    Conference: Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems
    Year: 2017
    Citations: 29

 

  1. A machine learning approach for identifying mosquito breeding sites via drone images
    Authors: A Amarasinghe, C Suduwella, C Elvitigala, L Niroshan, RJ Amaraweera, …
    Conference: Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems
    Year: 2017
    Citations: 15

 

  1. Suppressing dengue via a drone system
    Authors: A Amarasinghe, C Suduwella, L Niroshan, C Elvitigala, K De Zoysa, …
    Conference: 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions
    Year: 2017
    Citations: 15

 

  1. A swarm of crop spraying drones solution for optimizing safe pesticide usage in arable lands
    Authors: A Amarasinghe, VB Wijesuriya, D Ganepola, L Jayaratne
    Conference: Proceedings of the 17th Conference on Embedded Networked Sensor Systems
    Year: 2019
    Citations: 10

 

  1. A path planning algorithm for an autonomous drone against the overuse of pesticides
    Authors: A Amarasinghe, VB Wijesuriya, L Jayaratne
    Conference: 2021 10th International Conference on Information and Automation for Sustainability
    Year: 2021
    Citations: 5

 

  1. Drones vs dengue: a drone-based mosquito control system for preventing dengue
    Authors: A Amarasinghe, VB Wijesuriya
    Conference: 2020 RIVF International Conference on Computing and Communication Technologies
    Year: 2020
    Citations: 4

 

  1. Stimme: a chat application for communicating with hearing impaired persons
    Authors: A Amarasinghe, VB Wijesuriya
    Conference: 2019 14th Conference on Industrial and Information Systems (ICIIS)
    Year: 2019
    Citations: 4

âś… Conclusion

M.C. Akarshani Amarasinghe is an excellent candidate for the Research for Young Scientist Award. Her innovative contributions to drone technology for health and agriculture, leadership roles, and technical skills make her stand out. With continued expansion of her research portfolio and international exposure, she has the potential to achieve even greater recognition in the future.