Assist Prof Dr. Huiyun Zhang | Deep learn Awards | Best Researcher Award

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Assist Prof Dr. Huiyun Zhang | Deep learn Awards | Best Researcher Award

Assist Prof Dr. Huiyun Zhang, Henan University, China

Dr. Huiyun Zhang holds an M.S. and Ph.D. in Computer Application Technology and Pattern Recognition and Intelligence Systems, respectively, from Qinghai Normal University. She is currently an Assistant Professor at the School of Software, Henan University, China. Dr. Zhang’s research focuses on deep learning and speech emotion recognition (SER), where she has developed advanced models like MA-CapsNet-DA and CENN, integrating capsule networks, attention mechanisms, and Bi-LSTM to enhance SER accuracy. Her previous role as a research assistant at Baylor University provided valuable interdisciplinary experience. With over 20 publications in top-tier journals, Dr. Zhang has made significant contributions to the field, addressing challenges such as overfitting and model robustness. Her work, combined with her commitment to mentoring and interdisciplinary collaboration, underscores her impactful role in advancing both research and education.

Professional Profile:

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Suitability for the Award

  1. Innovative Research:
    • Dr. Zhang’s development of advanced deep learning models for SER represents significant innovation. Her work on MA-CapsNet-DA and CENN addresses critical challenges in speech emotion recognition, enhancing the accuracy and robustness of these systems.
  2. Research Impact:
    • Her publications in reputable journals and conferences reflect her substantial contributions to the field of deep learning and SER. Her research has practical implications for emotion recognition technology, which is increasingly relevant in various applications.
  3. Leadership and Collaboration:
    • Her experience as an Assistant Professor and her role in interdisciplinary research collaborations underscore her leadership and influence in the field. Her work at Baylor University and Henan University demonstrates her commitment to advancing research and fostering academic growth.
  4. Educational Contributions:
    • Dr. Zhang’s involvement in mentoring and educational projects highlights her dedication to advancing knowledge and supporting the next generation of researchers in artificial intelligence and machine learning.

Summary of Qualifications

  1. Educational Background:

    • M.S. in Computer Application Technology (Qinghai Normal University, 2020).
    • Ph.D. in Pattern Recognition and Intelligence Systems (Qinghai Normal University, 2024).
    • Her educational background reflects a strong foundation in both technology and computer science, culminating in advanced research in pattern recognition and intelligence systems.

  2. Professional Experience:

    • Assistant Professor, School of Software, Henan University, China.
    • Research Assistant, Data Science and Artificial Intelligence Program, Baylor University, USA (one year).
    • Her current role as an Assistant Professor involves advancing research in deep learning and speech emotion recognition (SER). Her previous research assistantship at Baylor University provided valuable interdisciplinary experience.

  3. Research Focus and Contributions:

    • Dr. Zhang’s research is centered on speech emotion recognition (SER), deep learning, and data science. She has developed innovative models such as MA-CapsNet-DA and CENN, which integrate capsule networks, attention mechanisms, and Bi-LSTM to enhance SER accuracy.
    • Her work addresses challenges such as overfitting and model robustness, contributing novel metrics and techniques to improve SER systems.
    • Published over 20 papers in top-tier journals including Expert Systems with Applications and Knowledge-Based Systems, reflecting her significant impact in her field.

  4. Contributions to Research and Development:

    • Dr. Zhang’s innovations in deep learning architectures for SER, including capsule networks and attention mechanisms, are cutting-edge contributions that advance the field.
    • Her role as a visiting scholar and collaboration with Baylor University have broadened her research perspectives and fostered interdisciplinary projects.

Publication Top Notes:

“An Improved Capsule Network for Speech Emotion Recognition” (2022), a book chapter in Communications in Computer and Information Science.

“Research on Speech Emotion Recognition Method Based A-CapsNet” (2022), published in Applied Sciences.

“Attention-Based Convolution Skip Bidirectional Long Short-Term Memory Network for Speech Emotion Recognition” (2021), published in IEEE Access.

These publications demonstrate her advanced research in SER and deep learning models, with notable contributions to improving recognition accuracy and model performance.

Conclusion

Assistant Prof. Dr. Huiyun Zhang is highly suitable for the Best Researcher Award due to her significant contributions to speech emotion recognition and deep learning. Her innovative research, extensive publication record, and active role in academic and community engagement demonstrate her excellence and impact in her field. Dr. Zhang’s work not only advances theoretical understanding but also addresses practical challenges in emotion recognition technology, making her an outstanding candidate for this prestigious award.

 

 

 

Prof. Dr. robin gras | Deep Learning Awards | Best Researcher Award

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Prof. Dr. robin gras | Deep Learning Awards | Best Researcher Award

Prof. Dr. robin gras , University of Windsor , Canada

Professor Dr. Robin has an extensive academic and professional background in computer science. He obtained his Bachelor’s (1987-1992), Master’s (1992-1994), and Ph.D. (1994-1997) from the University of Rennes I, France. He also achieved a Habilitation à Diriger des Recherches in Computer Science from the same institution in 2004. Dr. Robin is a tenured Full Professor at the University of Windsor, Canada, where he has been teaching since 2006. He has held various academic and research positions, including Acting CSO at Movyl Technologies in the United States, and senior scientific roles at the Swiss Institute of Bioinformatics and INRIA in France. Dr. Robin has supervised numerous graduate and undergraduate students and has been recognized with awards such as the Best Overall Paper Award at CIBCB 2008. His research interests encompass bioinformatics, machine learning, and artificial intelligence.

Professional Profile:

Google Scholar

🎓Education:

Professor Dr. Robin has an extensive academic background in computer science, having obtained his Bachelor’s degree from the University of Rennes I, France, where he studied from 1987 to 1992. He continued his education at the same institution, earning a Master’s degree in Computer Science from 1992 to 1994, followed by a Doctorate (Ph.D.) in Computer Science from 1994 to 1997. Furthering his academic qualifications, Dr. Robin achieved a Habilitation à Diriger des Recherches in Computer Science from the University of Rennes I between 1998 and 2004.

🏢Work Experience:

Professor Dr. Robin holds a tenured Full Professor position in Computer Science at the University of Windsor, Canada, since July 2016. He concurrently serves as Acting CSO at Movyl Technologies in the United States, a role he has held since June 2016. At the University of Windsor, he has had cross-appointments with the Great Lakes Institute for Environmental Research (July 2012 to December 2018) and the Biological Science department (July 2012 to June 2017). Dr. Robin was a Faculty Member in Argumentation Studies from September 2016 to September 2017 and previously held a cross-appointment with the Biological Science department from July 2007 to June 2012. He served as a tenured Associate Professor in Computer Science from May 2010 to June 2016, and before that, he was a tenure-track Associate Professor at the School of Computer Science at the same university.

🏆Awards:

Professor Dr. Robin has been recognized with numerous awards throughout his academic career. He received the Best Overall Paper Award at CIBCB 2008 for his paper titled “Evolutionary Strategy with Greedy Probe Selection Heuristics for the Non-Unique Oligonucleotide Probe Selection Problem.” In 2007, he was honored with the Recognition of Excellence in Research, Scholarship, and Creative Activity from the University of Windsor. Earlier in his academic journey, he was awarded a scholarship for his Ph.D. thesis in 1994 and a scholarship for his Master’s degree in 1993.

Publication Top Notes:

  • Improving protein identification from peptide mass fingerprinting through a parameterized multi‐level scoring algorithm and an optimized peak detection
    • Cited by: 202
  • A molecular scanner to automate proteomic research and to display proteome images
    • Cited by: 185
  • An individual-based evolving predator-prey ecosystem simulation using a fuzzy cognitive map as the behavior model
    • Cited by: 147
  • Popitam: towards new heuristic strategies to improve protein identification from tandem mass spectrometry data
    • Cited by: 137
  • Rule extraction from random forest: the RF+ HC methods
    • Cited by: 76

 

 

Dr. Siwei Guan | Deep Learning Award | Best Researcher Award

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Dr. Siwei Guan | Deep Learning Award | Best Researcher Award

Dr. Siwei Guan, Hangzhou Dianzi university, China

Dr. Siwei Guan, currently pursuing a Doctorate in Electronic Science and Technology at Hangzhou Dianzi University, China, stands at the forefront of groundbreaking research in anomaly detection. With a Master’s degree from the same university and a Bachelor’s from Jiangxi Normal University, his expertise shines in innovative approaches to multivariate time series data. Driven by a passion for advancement, his work, published in esteemed journals like Computer & Security and IEEE Sensors Journal, showcases pioneering techniques utilizing variational autoencoders and temporal neural networks. Supported by prestigious funding from the National Key Research and Development Program of China and the National Natural Science Foundation of China, he actively contributes to peer review activities, ensuring the quality of academic discourse. Dr. Guan’s dedication and achievements underscore his invaluable contributions to electronic science and technology, propelling the field forward with each innovative stride. 🌟

Professional Profile:

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🏫 Education:

Dr. Siwei Guan is currently pursuing a Doctorate in Electronic Science and Technology at Hangzhou Dianzi University, China, building upon his prior academic achievements. He holds a Master’s degree in Electronic Information from the same university and completed his Bachelor’s in Electronic Information Engineering at Jiangxi Normal University. His research focuses on innovative approaches to anomaly detection in multivariate time series data, as evidenced by his publications in reputable journals like Computer & Security and IEEE Sensors Journal.

💼 Work & Research:

As a Doctoral candidate, Dr. Siwei Guan is actively engaged in groundbreaking research, including the development of novel anomaly detection techniques using variational autoencoders and temporal neural networks. His work has received significant funding from prestigious institutions, including the National Key Research and Development Program of China and the National Natural Science Foundation of China. Additionally, he contributes to the academic community through peer review activities for esteemed journals such as Exper System with Application and ISA Transactions.

📊 Funding & Peer Review:

Dr. Siwei Guan has successfully secured funding to support his research endeavors, demonstrating the recognition and significance of his work in the field. Furthermore, his involvement in peer review activities reflects his commitment to advancing the scientific knowledge and contributing to the quality of research publications.

🌟 Achievements:

Dr. Siwei Guan’s contributions to the field of electronic science and technology have earned him recognition and support from prestigious funding programs and academic journals. With his dedication to innovative research and scholarly pursuits, he continues to make valuable contributions to the advancement of anomaly detection methodologies in multivariate time series data.

Publication Top Notes:

  1. Multivariate time series anomaly detection with variational autoencoder and spatial–temporal graph network
    • Published in Computers & Security, April 2024.
  2. Conditional normalizing flow for multivariate time series anomaly detection
    • Published in ISA Transactions, December 2023.
  3. TPAD: Temporal-Pattern-Based Neural Network Model for Anomaly Detection in Multivariate Time Series
    • Published in IEEE Sensors Journal, December 15, 2023.
  4. GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection
    • Published in Entropy, May 2022.