Le Yao | Computer Science | Best Researcher Award

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Prof. Le Yao | Computer Science | Best Researcher Award

Prof. Le Yao, Hangzhou Normal University, China

Le Yao is an accomplished Associate Professor at the School of Mathematics, Hangzhou Normal University, China. With a strong background in control science and engineering, he specializes in data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. His research focuses on deep learning, interpretable modeling, and causal analysis for industrial applications. Le Yao has been actively involved in multiple funded projects supported by NSFC and the China Postdoctoral Science Foundation. He has an impressive academic record, with numerous high-impact publications in IEEE Transactions and other renowned journals. Recognized for his contributions, he has received prestigious awards, including the National Scholarship for Ph.D. and Outstanding Dissertation Awards. His innovative work bridges the gap between theoretical advancements and practical applications in industrial processes, making significant contributions to smart manufacturing and intelligent systems.

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Summary of Suitability for the ‘Research for Best Researcher Award’

Le Yao is an exceptional candidate for the ‘Research for Best Researcher Award,’ given his impressive academic journey, extensive research contributions, and leadership in the field of industrial data-driven modeling. His work focuses on crucial areas such as soft sensor modeling, quality prediction, fault diagnosis, and causal analysis, with significant contributions to process control in industrial settings. His innovations in deep learning, causal analysis, and interpretable process modeling have greatly advanced the application of machine learning techniques to complex, large-scale industrial systems.

Notably, his research on scalable and distributed parallel modeling for big process data, combined with his exploration of probabilistic modeling and causal discovery methods, reflects a profound understanding of both theoretical and practical aspects of industrial systems. His ability to fuse domain knowledge with data-driven techniques has led to breakthroughs in process quality prediction and fault detection, impacting industries significantly. Furthermore, Le Yao has successfully secured competitive research funding from prestigious sources, such as the National Natural Science Foundation of China (NSFC) and the China Postdoctoral Science Foundation, demonstrating his capability to lead high-level research initiatives.

๐ŸŽ“ Education

Le Yao holds a Ph.D. in Control Science and Engineering from Zhejiang University (2019), where he specialized in big process data modeling, quality prediction, and process monitoring. His doctoral studies were pivotal in advancing soft sensor modeling techniques for industrial applications. Prior to his Ph.D., he earned an M.S. (2015) from Jiangnan University, where he focused on soft sensor modeling and system identification. His bachelor’s degree (2012) was also from Jiangnan University, where he developed a strong foundation in control science and engineering. Throughout his academic journey, Le Yao has consistently demonstrated excellence, securing prestigious scholarships and honors. His multidisciplinary expertise enables him to develop innovative solutions for industrial automation, smart manufacturing, and data-driven decision-making. His research contributions have influenced numerous industrial applications, bridging the gap between academic advancements and real-world implementations.

๐Ÿ’ผ Professional Experienceย 

Le Yao is currently an Associate Professor at Hangzhou Normal University (2022โ€“present), where he leads research on deep learning, causal analysis, and interpretable modeling for industrial systems. Prior to this, he served as a Postdoctoral Researcher (2019โ€“2022) at Zhejiang Universityโ€™s Institute of Industrial Process Control, focusing on deep learning-driven process modeling and process knowledge fusion. During his postdoctoral tenure, he was awarded research grants from NSFC and the China Postdoctoral Science Foundation. His expertise spans scalable and distributed parallel modeling, soft sensor applications, and quality prediction in large-scale industrial systems. Le Yaoโ€™s research integrates advanced computational techniques with practical industrial challenges, driving innovation in smart manufacturing. His leadership in industrial data analytics and AI-driven process control has positioned him as a key contributor to the field, influencing both academic research and industry practices.

๐Ÿ… Awards and Recognition

Le Yao has been recognized with numerous prestigious awards for his academic and research contributions. He received the 2020 Outstanding Dissertation Award from the Chinese Institute of Electronics and was named an Outstanding Graduate by Zhejiang University and Zhejiang Province in 2019. His research excellence has been acknowledged through multiple National Scholarships for Ph.D. students (2017, 2018). His work has been featured in top-tier conferences, earning him Best Paper Finalist awards at IEEE DDCLS (2018) and China Process Control Conferences (2016, 2017, 2018). These accolades reflect his outstanding contributions to industrial process modeling, soft sensing, and causal analysis. His innovative approaches to quality prediction and fault diagnosis have significantly impacted the field, earning him recognition from both academic institutions and industry leaders. Le Yaoโ€™s commitment to excellence continues to drive his research endeavors, making him a prominent figure in data-driven industrial applications.

๐ŸŒ Research Skills On Computer Science

Le Yaoโ€™s research expertise spans multiple domains, including data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. He specializes in deep learning techniques for process optimization and interpretable modeling to enhance decision-making in industrial environments. His work on scalable and distributed parallel modeling has introduced novel methodologies for handling big process data efficiently. His causal analysis research integrates process knowledge with data-driven approaches, improving anomaly detection and fault diagnosis. He has developed advanced deep learning models incorporating hierarchical extreme learning machines and probabilistic latent variable regression. His research contributions have been implemented in real-world industrial applications, optimizing quality prediction and process control. With a strong foundation in control engineering, statistics, and artificial intelligence, Le Yao continues to advance the field by bridging theoretical research with industrial needs.

๐Ÿ“– Publication Top Notes

  • Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application

    • Authors: L Yao, Z Ge
    • Citation: 295
    • Year: 2017
    • Journal: IEEE Transactions on Industrial Electronics, 65 (2), 1490-1498

  • Big data quality prediction in the process industry: A distributed parallel modeling framework

    • Authors: L Yao, Z Ge
    • Citation: 108
    • Year: 2018
    • Journal: Journal of Process Control, 68, 1-13

  • Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure

    • Authors: B Shen, L Yao, Z Ge
    • Citation: 102
    • Year: 2020
    • Journal: Control Engineering Practice, 94, 104198

  • Scalable semisupervised GMM for big data quality prediction in multimode processes

    • Authors: L Yao, Z Ge
    • Citation: 90
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (5), 3681-3692

  • Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data

    • Authors: L Yao, Z Ge
    • Citation: 80
    • Year: 2016
    • Journal: IEEE Transactions on Automation Science and Engineering, 14 (1), 126-138

  • Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2019
    • Journal: Engineering Applications of Artificial Intelligence, 81, 450-465

  • Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2017
    • Journal: Control Engineering Practice, 61, 72-80

  • Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction

    • Authors: L Yao, Z Ge
    • Citation: 61
    • Year: 2020
    • Journal: IEEE Transactions on Industrial Informatics, 17 (6), 3782-3792

  • Online updating soft sensor modeling and industrial application based on selectively integrated moving window approach

    • Authors: L Yao, Z Ge
    • Citation: 60
    • Year: 2017
    • Journal: IEEE Transactions on Instrumentation and Measurement, 66 (8), 1985-1993

  • Parallel computing and SGD-based DPMM for soft sensor development with large-scale semisupervised data

    • Authors: W Shao, L Yao, Z Ge, Z Song
    • Citation: 53
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (8), 6362-6373

Iustina Ivanova | Computer Science | Best Researcher Award

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Mrs. Iustina Ivanova | Computer Science | Best Researcher Award

๐Ÿ‘คย Mrs. Iustina Ivanova, FBK, Italy

Iustina Ivanova is an accomplished researcher in the field of Artificial Intelligence (AI) with a focus on computer vision and machine learning applications in real-world scenarios. She holds a Masterโ€™s degree in Artificial Intelligence from the University of Southampton, where she earned distinction for her research on neural networks for object detection. Currently, Iustina is engaged in AI research in smart agriculture at the Fondazione Bruno Kessler in Italy. Over the years, she has contributed to a variety of high-impact projects, including developing a recommender system for outdoor sport climbers and researching sensors for sports activity analysis. Her work has earned her several well-regarded publications and recognition in the AI and computer vision communities.

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๐ŸŒŸย Summary of Suitability for the Research for Best Researcher Award

Iustina Ivanova demonstrates exceptional qualifications for the “Research for Best Researcher Award.” Her academic background, professional experience, and research contributions highlight her significant impact on the fields of artificial intelligence (AI), machine learning, and computer vision. Her academic journey is distinguished by a Masterโ€™s degree in Artificial Intelligence with distinction from the University of Southampton and ongoing research pursuits during her Ph.D. studies. While her Ph.D. remains incomplete, the work she has undertakenโ€”such as her contributions to recommender systems and computer visionโ€”showcases her ability to address complex, real-world problems.

Professionally, Iustina’s research experience is diverse and impactful. At the Fondazione Bruno Kessler, she has been actively involved in applying AI to smart agriculture, addressing sustainability and innovation in the domain. Her previous roles, including as a Computer Vision Data Scientist and Data Science Moderator, further demonstrate her ability to bridge academia and industry.

๐ŸŽ“ย Education

Iustina Ivanova has an impressive academic background in computer science and AI. She completed her Master of Science in Artificial Intelligence with distinction at the University of Southampton, UK, in 2018. Before that, she earned a Specialist degree in Software Engineering from Bauman Moscow State Technical University, Russia, in 2013. In 2019, she pursued a PhD in Computer Science at the Free University of Bolzano, Italy, although she later decided to focus more on practical AI applications. Her academic journey includes notable achievements such as developing research in neural networks for object detection, which has been the cornerstone of her professional career in AI.

๐Ÿ’ผย ย Professional Experienceย 

Iustina Ivanova has a diverse and robust professional background in AI and computer vision. She currently works as a researcher at the Fondazione Bruno Kessler, Italy, specializing in the use of AI for smart agriculture. Prior to this, Iustina served as a Data Science Moderator at Netology, Russia, where she designed and delivered online courses in statistics and mathematics for data science students. She also worked as a Computer Vision Data Scientist at OCRV, Russia, where she helped develop a video-based tracking system for railway workers, focusing on object detection and worker time measurement. Iustina’s role as a teacher of informatics and mathematics at Repetitor.ru involved preparing high school students for their final exams, ensuring that many students successfully entered top universities. Throughout her career, she has collaborated on numerous innovative projects in AI, particularly in outdoor sports and smart agriculture.

๐Ÿ…Awards and Recognitionย 

Iustina Ivanovaโ€™s dedication and excellence in the field of AI have been recognized through multiple prestigious awards and accolades. Notably, she won several editions of the NOI Hackathon, including the SFSCON Edition (2021, 2022, 2024) and the Open Data Hub Edition (2022), showcasing her ability to create cutting-edge solutions in AI and data science. Her contributions to research and development in AI for sports activity analysis and computer vision have been published in highly regarded journals and conferences, such as the ACM Conference on Recommender Systems and IEEE Conferences. Iustina has also received recognition for her teaching contributions, inspiring future generations of data scientists. Her projects, especially those related to sports climbersโ€™ recommender systems and sensor data analysis, have received wide acclaim for their innovation and real-world impact.

๐ŸŒ Research Skills On Computer Science

Iustina Ivanovaโ€™s research expertise spans artificial intelligence, machine learning, computer vision, and recommender systems. She is particularly skilled in using AI techniques to solve complex problems in real-world applications. Her work with neural networks for object detection and sensor data analysis has led to significant advancements in both sports and smart agriculture sectors. Iustina is proficient in Python, OpenCV, machine learning frameworks like PyTorch and TensorFlow, and data analysis tools such as Jupyter Notebook and Git. She is well-versed in the development of recommender systems and has implemented innovative solutions for outdoor sports, including climbing crag recommendations. Her interdisciplinary approach combines knowledge from software engineering, data science, and AI to design systems that enhance user experience and improve decision-making. Iustina is committed to the continual development of her skills, evident in her participation in advanced data science and deep learning courses, as well as her extensive practical work in AI.

๐Ÿ“– Publication Top Notes

  • Climbing crags repetitive choices and recommendations
    • Author: Ivanova, I.
    • Citation: Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
    • Year: 2023
    • Pages: 1158โ€“1164
  • How can we model climbers’ future visits from their past records?
    • Authors: Ivanova, I., Wald, M.
    • Citation: UMAP 2023 – Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2023
    • Pages: 60โ€“65
  • Introducing Context in Climbing Crags Recommender System in Arco, Italy
    • Authors: Ivanova, I.A., Wald, M.
    • Citation: International Conference on Intelligent User Interfaces, Proceedings IUI
    • Year: 2023
    • Pages: 12โ€“15
  • Climbing crags recommender system in Arco, Italy: a comparative study
    • Authors: Ivanova, I., Wald, M.
    • Citation: Frontiers in Big Data
    • Year: 2023
    • Volume: 6, Article: 1214029
  • Map and Content-Based Climbing Recommender System
    • Authors: Ivanova, I.A., Buriro, A., Ricci, F.
    • Citation: UMAP2022 – Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2022
    • Pages: 41โ€“45
  • Climbing Route Difficulty Grade Prediction and Explanation
    • Authors: Andric, M., Ivanova, I., Ricci, F.
    • Citation: ACM International Conference Proceeding Series
    • Year: 2021
    • Pages: 285โ€“292
  • Climber behavior modeling and recommendation
    • Author: Ivanova, I.
    • Citation: UMAP 2021 – Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2021
    • Pages: 298โ€“303
  • Knowledge-based recommendations for climbers
    • Authors: Ivanova, I., Andriฤ‡, M., Ricci, F.
    • Citation: CEUR Workshop Proceedings
    • Year: 2021
    • Volume: 2960
  • Climbing activity recognition and measurement with sensor data analysis
    • Authors: Ivanova, I., Andric, M., Janes, A., Ricci, F., Zini, F.
    • Citation: ICMI 2020 Companion – Companion Publication of the 2020 International Conference on Multimodal Interaction
    • Year: 2020
    • Pages: 245โ€“249
  • Video and Sensor-Based Rope Pulling Detection in Sport Climbing
    • Authors: Ivanova, I., Andriฤ‡, M., Moaveninejad, S., Janes, A., Ricci, F.
    • Citation: MMSports 2020 – Proceedings of the 3rd International Workshop on Multimedia Content Analysis in Sports
    • Year: 2020
    • Pages: 53โ€“60

Raghad K Mohammed | Computer Science | Academic Excellence Award

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Dr. Raghad K Mohammed | Computer Science | Academic Excellence Award

๐Ÿ‘ค Dr. Raghad K Mohammed, College of Computer Science and Information Technology, Iraq

Raghad Khaled Mohammed, born on September 29, 1978, is a dedicated academic professional specializing in Computer Networks. She serves as a Lecturer at the College of Dentistry, University of Baghdad, where she has contributed significantly to education and research since 2005. A Muslim, married, and a mother of two, Raghad has consistently balanced her personal and professional life with distinction. Her academic journey began with a Bachelor’s degree from Al-Rafidain in 2002, followed by a Master’s in Computer Networks from the University of Technology in 2005. She is currently pursuing a PhD in Computer Science and Information Technology at the University of Anbar. Her professional roles have included leadership positions, such as Head of the Planning and Quality Assurance Units, showcasing her commitment to academic excellence and institutional development.

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๐ŸŒŸย Suitability for the Research for Academic Excellence Award

Summary of Suitability
Raghad Khaled Mohammedโ€™s extensive academic journey and professional accomplishments demonstrate her dedication to higher education and research, making her a strong candidate for the Research for Academic Excellence Award. With a career spanning nearly two decades, her contributions as a lecturer at the University of Baghdadโ€™s College of Dentistry, along with leadership roles in quality assurance, planning, and continuing education, reflect her commitment to fostering academic and institutional excellence.

๐ŸŽ“ย ย Education

Raghad Khaled Mohammed’s academic qualifications reflect her dedication to advancing knowledge in Computer Science. She earned her Bachelorโ€™s degree in 2002 from Al-Rafidain, laying the foundation for her career. In 2005, she completed a Masterโ€™s degree in Computer Networks from the Informatics Institute for Graduate Studies, University of Technology, Baghdad. This specialization equipped her with technical expertise in designing and managing network systems. Currently, she is pursuing a PhD in Computer Science and Information Technology at the University of Anbar, demonstrating her commitment to lifelong learning and academic growth. Her academic progression highlights her passion for integrating innovative solutions and knowledge-sharing within the field of computer science, with a focus on practical applications that benefit both academia and industry.

๐Ÿ’ผย ย Professional Experience

Raghad Khaled Mohammed has a rich professional journey at the University of Baghdad. She started as an Assistant Lecturer in 2005, demonstrating a strong foundation in teaching and academic research. From 2006 to 2009, she led the Planning Department, showcasing her organizational and strategic planning skills. In 2010, she was promoted to Lecturer, reflecting her academic and professional growth. Between 2016 and 2018, she excelled as the Head of the Quality Assurance Unit, where she implemented initiatives to enhance educational standards. Her leadership continued in 2024 as the Head of the Continuing Education Unit, focusing on faculty and student skill development. Raghad’s multifaceted roles underline her expertise in education, administration, and her dedication to fostering an environment of continuous improvement and innovation.

๐Ÿ…ย Awards and Recognition

Raghad Khaled Mohammed’s career is marked by achievements and recognition in academia. Her contributions to quality assurance earned her institutional accolades during her tenure as the Head of the Quality Assurance Unit. Her innovative initiatives in the Planning Department were lauded for their impact on academic progress and administrative efficiency. As a researcher and educator, she has been acknowledged for her role in advancing the field of Computer Networks, earning respect among peers and students alike. Raghad has also been recognized for her leadership in Continuing Education, where she played a pivotal role in professional development programs. These accolades affirm her commitment to academic excellence and her ability to inspire positive change within her institution.

๐ŸŒ Research Skills On Computer Science

Raghad Khaled Mohammed possesses diverse research skills, particularly in Computer Networks and Information Technology. Her expertise includes network architecture design, security protocols, and system optimization. She is skilled in using advanced simulation tools and programming languages to develop innovative solutions for complex networking challenges. Raghad’s research focuses on bridging the gap between theoretical concepts and real-world applications, aiming to enhance efficiency and cybersecurity in digital systems. Her ability to integrate interdisciplinary approaches, coupled with her technical expertise, ensures impactful contributions to academia and industry. With ongoing doctoral studies, her research skills continue to evolve, driving advancements in Computer Science and Information Technology.

๐Ÿ“– Publication Top Notes

Title: U-Net for Genomic Sequencing: A Novel Approach to DNA Sequence Classification
  • Authors: Mohammed, R.K.; Alrawi, A.T.H.; Dawood, A.J.
    Year: 2024
    Journal: Alexandria Engineering Journal
    Volume and Pages: 96, pp. 323โ€“331
    Citations: 0
Title: Optimizing Genetic Prediction: Define-by-Run DL Approach in DNA Sequencing
  • Authors: Mohammed, R.K.; Alrawi, A.T.H.; Dawood, A.J.
    Year: 2023
    Journal: Journal of Intelligent Systems
    Volume and Pages: 32(1), Article ID: 20230130
    Citations: 0
Title: Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Transfer Learning
  • Authors: Al-Saffar, R.; Mohammed, R.K.; Abed, W.M.; Hussain, O.F.
    Year: 2022
    Journal: NeuroQuantology
    Volume and Pages: 20(1), pp. 105โ€“119
    Citations: 1