Huawen Liu | Machine Learning | Distinguished Scientist Award

Posted on 19/04/202519/04/2025 by Physicist Particle

Prof. Dr. Huawen Liu | Machine Learning | Distinguished Scientist Award

Professor at Shaoxing University, China

Prof. Huawen Liu 👨‍🏫, a distinguished academic at Shaoxing University 🇨🇳 since 2010, holds a Ph.D. and Ms.D. in Computer Science from Jilin University 🧠💻. He expanded his research globally as a postdoc at the University of South Australia 🇦🇺 (2012–2013) and a visiting fellow at the University of Texas at San Antonio 🇺🇸 (2018–2019). His work spans hash learning, AI, big data, and machine learning 🤖📊. With over 50 publications 📚 in top-tier journals, he actively contributes as an editor and conference organizer. He holds an H-index of 17 📈 and continues to shape intelligent computing worldwide 🌐.

Professional Profile:

Google Scholar

Scopus

🎓 Education & Experience

🎓 Ph.D. & Ms.D. in Computer Science from Jilin University, China 🇨🇳 (Top-10 university)
🧑‍🔬 Postdoctoral Researcher at University of South Australia 🇦🇺 (2012–2013)
🌍 Visiting Fellow at University of Texas at San Antonio, USA 🇺🇸 (2018–2019)
👨‍🏫 Professor at Shaoxing University since July 2010 🏫
📝 Over 50 peer-reviewed publications in high-impact journals and conferences 📚

🌱 Professional Development

Prof. Liu has actively participated in shaping the research community 🌐. He serves as the Editor-in-Chief (EIC) of the International Journal of Intelligence and Sustainable Computing 🧠💡, Associate Editor for International Journal of Artificial Intelligence and Tools 🛠️, and Editor for Mathematics ➗📘. He has also led special issues as Guest Editor in Neural Computing and Applications 🧮 and Computing and Informatics 💻. His involvement extends to organizing national and international conferences 🎤📅 and acting as a program committee member for IJCAI, AAAI, CVPR, and others 🤝📊, reflecting his strong engagement with the global AI and computing community.

🔍 Research Focus Category

Prof. Liu’s research lies at the intersection of artificial intelligence 🤖, machine learning 📚, and data science 📊. He specializes in hash learning, outlier detection, feature selection, and multimedia systems 🎥. His focus extends to practical applications in big data analytics 🗃️ and intelligent systems 💡. With a keen interest in mining patterns from complex datasets, his work contributes significantly to pattern recognition 🧠 and cybernetics 🛡️. He aims to bridge theory and real-world implementation through intelligent algorithms that enhance automated decision-making systems 🧮. His interdisciplinary approach empowers robust AI models with scalable and sustainable solutions 🌍.

🏆 Awards & Honors

📈 H-index of 17 according to Google Scholar 🧠
📝 Over 50 publications in leading journals such as IEEE TKDE, TNNLS, TMM, TSMC, and more 📚
🧑‍💼 Editor-in-Chief, Int. J. of Intelligence and Sustainable Computing
🛠️ Associate Editor, Int. J. of Artificial Intelligence and Tools
➗ Editor, Mathematics
🧮 Lead Guest Editor for Neural Computing and Applications (NCAA)
💻 Lead Guest Editor for Computing and Informatics (CAI)
🎤 Organising Chair for 2015 National Conf. of Theoretical Computer Science
📊 Organising Chair for 2014 China Conference on Data Mining
🎓 Program Committee Member for top AI conferences: IJCAI, AAAI, CVPR, ADMA, ICBK, KSEM

Publication Top Notes

🔍 1. Outlier Detection Using Local Density and Global Structure

Authors: H. Liu, Huawen; S. Zhang, Shichao; Z. Wu, Zongda; X. Li, Xuelong
Journal: Pattern Recognition, 2025
Citations: 7
Summary: This article proposes a novel outlier detection method combining local density estimation with global structural features. It’s likely useful for anomaly detection in high-dimensional or graph-structured data.

🧠 2. Select Your Own Counterparts: Self-Supervised Graph Contrastive Learning With Positive Sampling

Authors: Z. Wang, Zehong; D. Yu, Donghua; S. Shen, Shigen; S. Yao, Shuang; M. Guo, Maozu
Journal: IEEE Transactions on Neural Networks and Learning Systems, 2025
Citations: 2
Summary: Focuses on self-supervised learning with graph contrastive methods, improving representation learning by selecting reliable positive samples for contrastive training.

🗣️ 3. Amharic Spoken Digits Recognition Using Convolutional Neural Network

Authors: T.A. Ayall, Tewodros Alemu; C. Zhou, Chuangjun; H. Liu, Huawen; S.T. Abate, Solomon Teferra; M. Adjeisah, Michael
Journal: Journal of Big Data, 2024 (Open Access)
Citations: 3
Summary: Presents a CNN-based model for recognizing spoken digits in Amharic, an under-resourced African language — showcasing multilingual AI applications.

🧠 4. An Improved Deep Hashing Model for Image Retrieval With Binary Code Similarities

Authors: H. Liu, Huawen; Z. Wu, Zongda; M. Yin, Minghao; X. Zhu, Xinzhong; J. Lou, Jungang
Access: Open Access
Citations: 0
Summary: Describes a deep hashing method that optimizes binary similarity in hash code space for more effective image retrieval.

🧠 5. LGAD: Local and Global Attention Distillation for Efficient Semantic Segmentation

Authors: C. Wang, Chen; Y. Qi, Yafei; Q. Li, Qi; H. Liu, Huawen
Type: Conference Paper (Open Access)
Citations: 1
Summary: Proposes an attention distillation method combining local and global context for lightweight semantic segmentation, improving performance while keeping models efficient.

Conclusion:

Dr. Huawen Liu’s exceptional research contributions, leadership in academic organizations, and active engagement in the scientific community make him a strong candidate for the Distinguished Scientist Award. His sustained impact on the field of machine learning and AI, along with his contributions to both theoretical and applied research, exemplify the qualities deserving of such an esteemed recognition.

Ricardo Alberto Rodríguez-Carvajal | Technology | Best Researcher Award

Posted on 14/03/202514/03/2025 by Physicist Particle

Prof. Dr. Ricardo Alberto Rodríguez-Carvajal | Technology | Best Researcher Award

Research Professor at Universidad de Guanajuato, Mexico

Dr. Ricardo Alberto Rodríguez Carvajal is a distinguished professor and researcher at the Universidad de Guanajuato 🇲🇽. With a strong background in industrial engineering, strategic planning, and technology management, he has contributed significantly to academia and industry. His expertise spans sustainable development, energy security, and social innovation ⚡🌍. He has served in leadership roles at Universidad de Sonora and Universidad de Guanajuato, guiding doctoral research and technology transfer. As a member of the Sistema Nacional de Investigadores (SNI) and the National Solar Energy Association, he actively promotes renewable energy solutions ☀️🔬.

Professional Profile

Orcid

Education & Experience 📚👨‍🏫

Doctorate in Strategic Planning & Technology Management, UPAEP (2013) 🎓
Master’s in Computer Science, Instituto Tecnológico de Hermosillo (2006) 💻
Industrial & Systems Engineering, Universidad de Sonora (1999) 🏭
Postdoctoral Researcher, CONAHCYT – Universidad de Sonora (2023) 🏡🌱
Professor, Universidad de Guanajuato (2016–present) 👨‍🏫
Professor, Universidad Virtual del Estado de Guanajuato (2021–present) 🌐
Guest Lecturer, Instituto Politécnico Nacional (2021–2022) 🎓
Director of Innovation, Grupo Industrial SOGO (2015–2017) 🚀

Professional Development 🚀📖

Dr. Rodríguez Carvajal has dedicated his career to innovation, academia, and technology transfer. As an SNI Level 1 researcher, he contributes to the advancement of renewable energy and industrial engineering ⚡🏭. He has played a crucial role in technology commercialization, helping industries develop solar energy solutions ☀️. He also works as an academic advisor for doctoral and postgraduate students at UPAEP 📚. His leadership extends to editorial roles, project management, and participation in international networks like IATED and the National Association of Solar Energy 🌍. His goal is to bridge research and industry through innovation and sustainability.

Research Focus 🔬🌱

Dr. Rodríguez Carvajal’s research revolves around renewable energy, sustainability, and industrial innovation ⚡🏭. He specializes in solar energy technologies ☀️, helping develop tracking systems for photovoltaic panels. His work includes strategic planning for sustainable development 🌍, addressing energy, water, and food security challenges in Mexico. He is also involved in social innovation projects, leveraging technology to improve conditions in indigenous communities 🏡. His interdisciplinary approach combines engineering, environmental science, and policy to create impactful solutions. Through international collaborations, he contributes to the future of clean energy and industrial efficiency 🌍🔋.

Awards & Honors 🏆🎖

Member of Sistema Nacional de Investigadores (SNI), Level 1 (2023-2027, 2020-2022, 2013-2016) 🏅
PRODEP Recognition for Academic Excellence (2018-2021, 2015-2018) 🏆
National Researcher Fellowship, CONACyT (2008-2012) 🎓
PROMEP Doctoral Studies Fellowship (2008-2013) 🎖
Reviewer for Engineering Accreditation Council (CACEI) (2008-2014) 📜
Editorial Secretary, Renewable Energy Journal (2022-2024) 📰
Technical Advisor, National Evaluation Center (CENEVAL) (2002-2014) 🏛

Publication Top Notes

Absorption Capacities of Digital Transformation Technologies in the Agroindustry of Guanajuato
- Journal: Revista de Gestão Social e Ambiental
- Year: 2024
- DOI: 10.24857/rgsa.v18n9-159
- Citation: Author(s). “Absorption Capacities of Digital Transformation Technologies in the Agroindustry of Guanajuato.” Revista de Gestão Social e Ambiental, vol. 18, no. 9, 2024, DOI: 10.24857/rgsa.v18n9-159.
Perspective of Water-Use Programs in Agriculture in Guanajuato
- Journal: Agriculture
- Year: 2024
- DOI: 10.3390/agriculture14081258
- Citation: Author(s). “Perspective of Water-Use Programs in Agriculture in Guanajuato.” Agriculture, vol. 14, no. 8, 2024, DOI: 10.3390/agriculture14081258.
Enhancing the Productivity of Mexican Agriculture Through the Provision of Industry 4.0 Training for Local Farmers
- Conference: ICERI 2023
- Year: 2023
- DOI: 10.21125/iceri.2023.0549
- Citation: Author(s). “Enhancing the Productivity of Mexican Agriculture Through the Provision of Industry 4.0 Training for Local Farmers.” ICERI 2023 Proceedings, 2023, DOI: 10.21125/iceri.2023.0549.
Carbón Vegetal de Marabú, Un Aporte a la Energía Renovable
- Journal: Energías Renovables
- Year: 2023
- DOI: 10.59730/rer.v10n50a1
- Citation: Ricardo Alberto Rodríguez-Carvajal. “Carbón Vegetal de Marabú, Un Aporte a la Energía Renovable.” Energías Renovables, vol. 10, no. 50, 2023, DOI: 10.59730/rer.v10n50a1.
Correlating Disorder Microstructure and Magnetotransport of Carbon Nanowalls
- Journal: Applied Sciences
- Year: 2023
- DOI: 10.3390/app13042476
- Citation: Author(s). “Correlating Disorder Microstructure and Magnetotransport of Carbon Nanowalls.” Applied Sciences, vol. 13, no. 4, 2023, DOI: 10.3390/app13042476.

Vaneet | Machine Learning | Best Researcher Award

Posted on 19/02/202519/02/2025 by Physicist Particle

Prof. Dr. Vaneet | Machine Learning | Best Researcher Award

Professor at PURDUE UNIVERSITY, United States

vaneet aggarwal is a distinguished professor and university faculty scholar at purdue university, specializing in reinforcement learning, generative AI, quantum machine learning, and LLM alignment 🤖⚛️. With a Ph.D. from Princeton University 🎓 and extensive experience in industry and academia, he has made groundbreaking contributions to networking, robotics, healthcare, and computational biology 🌍🩺. He has served as a visiting professor at KAUST, IIIT Delhi, and IISc Bangalore 📚 and has led major research initiatives at AT&T Labs and Purdue CLAN Labs. His work has been recognized globally through high-impact publications and awards 🏅.

Professional Profile

Scopus

Education & Experience 🎓💼

📌 Education:

Ph.D. in Electrical Engineering – Princeton University, 2010 🎓 (GPA 4.0/4.0)
M.A. in Electrical Engineering – Princeton University, 2007 🏅 (GPA 4.0/4.0)
B.Tech in Electrical Engineering – IIT Kanpur, 2005 🎓 (GPA 9.6/10)

📌 Experience:

Purdue University (2015–Present) 🏫 – Professor & University Faculty Scholar
KAUST, Saudi Arabia (2022–2023) 🏝️ – Visiting Professor
IIIT Delhi (2022–2023) 🌏 – Adjunct Professor
Plaksha University (2022–2023) 📡 – Adjunct Professor
IISc Bangalore (2018–2019) 🏆 – VAJRA Adjunct Faculty
AT&T Labs Research, NJ (2010–2014) 📡 – Senior Member, Technical Staff
Columbia University, NY (2013–2014) 📚 – Adjunct Assistant Professor

Professional Development 🚀📚

vaneet aggarwal has consistently contributed to cutting-edge advancements in AI, machine learning, and quantum computing 🧠⚡. As Editor-in-Chief of the ACM Journal of Transportation Systems 🚗📖, he shapes global research trends. He has been a technical lead in Purdue’s AI and security programs, fostering industry collaborations 🤝💡. His leadership in AI decision-making, intelligent infrastructures, and computational biology has driven groundbreaking innovations 🏗️🔬. He frequently mentors Ph.D. students and collaborates with top institutions worldwide, ensuring continuous academic excellence and technological impact 🌍🎯. His work bridges fundamental research with real-world applications, influencing multiple industries 🚀.

Research Focus Areas 🔍💡

🔬 Artificial Intelligence & Machine Learning: Reinforcement learning, generative AI, LLM alignment 🤖
⚛️ Quantum Computing: Quantum machine learning, hidden Markov models 🧠
📡 Networking & Systems: Cloud computing, 5G/6G networks, network virtualization 🌐
🛠️ Optimization & Control: Combinatorial bandits, linear optimization ⚙️
🚗 Transportation & Robotics: AI for intelligent infrastructure and automation 🏎️
🩺 Healthcare & Biomedical AI: Drug discovery, computational biology, medical AI 🧬💊

His research transforms fundamental theories into real-world applications, influencing technology, healthcare, and sustainable infrastructure 🌍.

Awards & Honors 🏅🎖️

🏆 University Faculty Scholar, Purdue University (2024) 🏫
🎖️ Best Paper Award – NeurIPS Workshop on Cooperative AI (2021) 📝
📚 VAJRA Adjunct Faculty, IISc Bangalore (2018–2019) 🔬
🥇 Editor-in-Chief, ACM Journal of Transportation Systems (2022–Present) 🚗📖
🌍 Senior Member, IEEE & ACM ⚡
🏅 Best Research Contributions in AI & Quantum Computing 🤖⚛️

Publication Top Notes

1. Stochastic Submodular Bandits with Delayed Composite Anonymous Bandit Feedback

Authors: Mohammad Pedramfar, Vaneet Aggarwal
Published in: IEEE Transactions on Artificial Intelligence, 2025
Summary: This paper addresses the combinatorial multi-armed bandit problem with stochastic submodular rewards and delayed, composite anonymous feedback. The authors analyze three delay models—bounded adversarial, stochastic independent, and stochastic conditionally independent—and derive regret bounds for each. Their findings indicate that delays introduce an additive term in the regret, affecting overall performance.
Access: The paper is available as open access.

2. FilFL: Client Filtering for Optimized Client Participation in Federated Learning

Authors: Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini, Marco Canini
Published in: [No source information available]
Summary: This conference paper introduces FilFL, a method to enhance federated learning by optimizing client participation through a filtering mechanism. By selecting a subset of clients that maximizes a combinatorial objective function, FilFL aims to improve learning efficiency, accelerate convergence, and boost model accuracy. Empirical evaluations demonstrate benefits such as faster convergence and up to a 10% increase in test accuracy compared to scenarios without client filtering.
Access: The paper is available as open access.

3. Prism Blockchain Enabled Internet of Things with Deep Reinforcement Learning

Authors: Divija Swetha Gadiraju, Vaneet Aggarwal
Published in: Blockchain: Research and Applications, 2024
Summary: This article explores the integration of Prism blockchain technology with the Internet of Things (IoT) using deep reinforcement learning techniques. The approach aims to enhance security, scalability, and efficiency in IoT networks by leveraging the unique features of Prism blockchain and the adaptive capabilities of deep reinforcement learning.
Access: The paper is available as open access.

4. GLIDE: Multi-Agent Deep Reinforcement Learning for Coordinated UAV Control in Dynamic Military Environments

Authors: Divija Swetha Gadiraju, Prasenjit Karmakar, Vijay K. Shah, Vaneet Aggarwal
Published in: Information (Switzerland), 2024
Summary: GLIDE presents a multi-agent deep reinforcement learning framework designed for the coordinated control of unmanned aerial vehicles (UAVs) in dynamic military settings. The framework focuses on enhancing mission success rates and operational efficiency by enabling UAVs to adapt to changing environments and collaborate effectively.
Access: The paper is available as open access.

5. Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN

Authors: Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Mohamed-Slim Alouini, Vaneet Aggarwal
Published in: IEEE Networking Letters, 2024
Summary: This article proposes a method for estimating coverage manifolds in cellular networks using conditional Generative Adversarial Networks (GANs). The approach aims to achieve near-perfect coverage predictions, which are crucial for optimizing network performance and ensuring reliable communication services.
Access: The paper is available as open access.

Conclusion

vaneet aggarwal is a highly suitable candidate for the Best Researcher Award, given his strong publication record, leadership, and multidisciplinary impact. If he strengthens his global recognition, large-scale funding acquisition, and public engagement, he could be an even stronger contender for such an award.

Mehmet Yilmaz | Artificial Neural Networks | Best Researcher Award

Posted on 25/11/202427/11/2024 by Physicist Particle

Mr, Mehmet Yilmaz, Kayseri University, Turkey

Mehmet Yilmaz is a lecturer in the Department of Architecture and Urban Planning at Kayseri University, Turkey. With an academic background in Geomatic Engineering from Erciyes University, he brings expertise in geotechnical engineering, real estate valuation, and geographic information systems (GIS) to his role. Currently pursuing his doctorate, Mr. Yilmaz’s teaching and research contributions focus on engineering applications in urban environments, including courses on land measurement, urban information systems, and property law. His work is dedicated to exploring innovative solutions in GIS and urban planning, addressing practical challenges in real estate valuation and geotechnical engineering.

PROFILE

Orcid Profile

Educational Details

Mr. Mehmet Yilmaz is a faculty member at Kayseri University, Turkey, where he specializes in engineering and urban planning. He is currently pursuing his Doctorate in Geomatic Engineering at Erciyes University’s Institute of Science (Fen Bilimleri Enstitüsü), continuing his journey in the same field in which he obtained both his postgraduate degree (2019-2021) and undergraduate degree (2007-2012). This solid academic foundation has equipped him with specialized skills in geographic information systems, geotechnical engineering, and real estate valuation.

Professional Experience

Since 2018, Mr. Yilmaz has served as a lecturer at Kayseri University in the Tomarza Mustafa Akıncıoğlu Vocational School of Architecture and Urban Planning. He previously taught at Erciyes University in the same department (2017-2018). Throughout his career, he has taught a wide array of courses, including Land Measurement, Expropriation Techniques, Real Estate Law, Urban Information Systems, and Real Estate Valuation Techniques, as well as foundational courses such as Mathematics and Basic Law. His commitment to teaching and hands-on field knowledge has contributed to his expertise in applied engineering and planning education.

Research Interests

Mr. Yilmaz’s research interests span several critical areas within engineering and urban planning, including geotechnical engineering, real estate valuation, geographic information systems (GIS), and image processing. His research has previously focused on topics such as property tax loss in mass valuation, as exemplified by his postgraduate thesis, which investigated the impacts of mass valuation on tax losses in the Kayseri region. This study highlights his interest in the integration of GIS and valuation techniques to address real-world urban planning challenges.

Top Notable Publications

Mehmet Yilmaz (2024)
Title: Hiperspektral görüntülerde Relief-F algoritması ile band seçimi
Source: Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
Publication Date: 2024-04-02
DOI: 10.28948/ngumuh.1408200

Mehmet Yilmaz (2023)
Title: Investigation of Real Estate Tax Leakage Loss Rates with ANNs
Source: Buildings
Publication Date: 2023-09-28
DOI: 10.3390/buildings13102464
ISSN: 2075-5309

Mehmet Yilmaz (2021)
Title: Determination of Housing Prices with Mass Appraisal in Turkey
Source: Ankara V. International Scientific Research Congress
Publication Date: 2021-10-18
(Conference abstract, no DOI provided)

Conclusion

Mr. Mehmet Yilmaz’s academic background, teaching experience, research interests, certifications, and publication record collectively establish him as a dedicated researcher in the fields of geomatics, urban planning, and real estate valuation. His interdisciplinary approach, integrating advanced technologies like GIS, hyperspectral imaging, and neural networks, is noteworthy for solving real-world challenges in property valuation and urban information systems. Given these qualifications, Mr. Yilmaz is a strong candidate for the Research for Best Researcher Award, with demonstrated potential for further contributions to his field.

Xiao-Yong Zhang | AI-Driven | Best Researcher Award

Posted on 23/11/202423/11/2024 by Physicist Particle

Prof. Xiao-Yong Zhang, Shanghai Jiao Tong University School of Medicine, China

Dr. Xiao-Yong Zhang is a leading expert in medical imaging, with a special focus on using MRI and AI to advance diagnostic technology for brain health. He has held esteemed positions across top institutions in China and the U.S., contributing to neuroimaging through his roles as an editor, committee member, and principal investigator of high-impact research projects. Dr. Zhang’s innovative work in the quantitative detection of neuroinflammation and Alzheimer’s biomarkers underscores his commitment to advancing neurological diagnostics and personalized medicine.

PROFILE

Orcid Profile

Educational Details

Ph.D. in Medical Imaging (2007), Fourth Military Medical University, Xi’an, China

Master of Medicine (M.Med.) in Radiological Sciences (2004), Fourth Military Medical University, Xi’an, China

Bachelor of Science (B.S.) in Biomedical Engineering (1998), Fourth Military Medical University, Xi’an, China

Professional Experience

Professor (2024–Present), Shanghai Jiao Tong University School of Medicine

Associate Professor (2017–2024), Fudan University

Research Associate (2014–2016), Vanderbilt University, under Dr. John C. Gore

Postdoctoral Fellow (2009–2014), Georgia Institute of Technology and Emory University, under Dr. Xiaoping P. Hu

Engineer/Lecturer (2007–2009), Fourth Military Medical University

Research Interests

Magnetic Resonance Imaging (MRI): Developing visualization techniques for brain microenvironments.

Deep Learning Algorithms: Creating AI-driven diagnostic tools to improve the detection and understanding of neurological diseases.

Grants

Quantitative Detection of Neuroinflammation using Hydroxyl Proton Transfer MRI
(2022–2025) | National Natural Science Foundation of China (NSFC) | Principal Investigator

Imaging Biomarkers for Alzheimer’s Disease
(2020–2021) | Fudan-Cambridge collaboration | Principal Investigator

Glioma Genotyping using CEST-NOE MRI
(2020–2023) | Shanghai Science and Technology Committee (STCSM) | Principal Investigator

Label-Free NOE MR Imaging of Choline Phospholipids
(2019–2022) | National Natural Science Foundation of China (NSFC) | Principal Investigator

Global Analysis of Brain Functional and Metabolic Networks
(2018–2022) | Subproject of Shanghai Municipal Science and Technology Major Project | Principal Investigator

Memberships

Organization for Human Brain Mapping (OHBM) (since 2022)

Institute of Electrical and Electronics Engineers (IEEE) (since 2021)

American Association for the Advancement of Science (AAAS) (since 2016)

International Society for Magnetic Resonance (ISMRM) (since 2010)

Top Notable Publications

Zhang, Xiao-Yong et al. “HiFi-Syn: Hierarchical granularity discrimination for high-fidelity synthesis of MR images with structure preservation.” Medical Image Analysis, November 2024. DOI: 10.1016/j.media.2024.103390

Zhang, Xiao-Yong et al. “Resting State Brain Networks under Inverse Agonist versus Complete Knockout of the Cannabinoid Receptor 1.” ACS Chemical Neuroscience, April 17, 2024. DOI: 10.1021/acschemneuro.3c00804

Authors. “Benchmarking spatial clustering methods with spatially resolved transcriptomics data.” Nature Methods, April 2024. DOI: 10.1038/s41592-024-02215-8

Authors. “A neural signature for the subjective experience of threat anticipation under uncertainty.” Nature Communications, February 20, 2024. DOI: 10.1038/s41467-024-45433-6

Authors. “A Convolutional Neural Network Model for Distinguishing Hemangioblastomas From Other Cerebellar-and-Brainstem Tumors Using Contrast-Enhanced MRI.” Journal of Magnetic Resonance Imaging, 2024. DOI: 10.1002/jmri.29230

Zhang, Xiao-Yong et al. “CQformer: Learning Dynamics Across Slices in Medical Image Segmentation.” IEEE Transactions on Medical Imaging, 2024. DOI: 10.1109/TMI.2024.3477555

Authors. “A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders.” Medical Image Analysis, December 2023. DOI: 10.1016/j.media.2023.102932

Authors. “Downregulation of mGluR1-mediated signaling underlying autistic-like core symptoms in Shank1 P1812L-knock-in mice.” Translational Psychiatry, October 25, 2023. DOI: 10.1038/s41398-023-02626-9

Authors. “A neural signature for the subjective experience of threat anticipation under uncertainty.” Preprint, September 22, 2023. DOI: 10.1101/2023.09.20.558716

Conclusion

Professor Xiao-Yong Zhang’s extensive research in MRI, deep learning diagnostics, and successful collaborations place him as an exemplary candidate for the Research for Best Researcher Award. His contributions to medical imaging innovation, demonstrated research leadership, and commitment to interdisciplinary collaborations reflect the award’s values and criteria.

Eman Aldakheel | Artificial Intelligence | Best Researcher Award

Posted on 16/09/202416/09/2024 by Physicist Particle

Assist Prof Dr. Eman Aldakheel | Artificial Intelligence | Best Researcher Award

Assist Prof Dr. Eman Aldakheel, Princess Nourah Bint Abdulrahman University, Saudi Arabia

Assist. Prof. Dr. Eman Aldakheel is a faculty member at Princess Nourah Bint Abdulrahman University in Saudi Arabia. She specializes in a range of scientific fields, contributing to both academic teaching and research within her department. Dr. Aldakheel is involved in interdisciplinary studies and has participated in various research projects and academic initiatives, enhancing the university’s reputation in the region. She actively engages with students and colleagues, fostering a collaborative environment that promotes learning and research innovation.

PROFILE

Scopus Profile

Orcid Profile

Educational Details:

Dr. Eman Aldakheel holds a Doctor of Philosophy in Computer Science from the University of Illinois at Chicago, where her dissertation, titled “Deadlock Detector and Solver (DDS),” focused on developing solutions for deadlock issues in computer systems. She earned her Master of Science in Computer Science from Bowling Green State University in Ohio, with a thesis titled “A Cloud Computing Framework for Computer Science Education,” highlighting her interest in leveraging technology for educational advancement. Dr. Aldakheel completed her Bachelor of Science in Computer Science with Honors from Imam Abdulrahman bin Faisal University in Dammam, Saudi Arabia, laying the foundation for her career in academia and research.

Academic Experience:

Dr. Eman Aldakheel has an extensive teaching and training background, starting as an instructor at the New Horizons Institute in Khobar, Saudi Arabia, in 2007, where she trained students on ICDL and IC3 certifications and taught various computer-related courses. She later joined Imam Abdulrahman bin Faisal University (formerly Dammam University) as an instructor, teaching basic computer skills and Microsoft Office applications to students in the Geography department. She also taught computer basics to girls at Riyadh Al-Islam Schools, working with students from elementary to high school levels. From 2012 to 2020, Dr. Aldakheel served as a lecturer at Princess Nourah Bint Abdulrahman University, where she contributed as a research assistant on software engineering projects and taught object-oriented programming. Since Fall 2020, she has been an Assistant Professor at the same institution, teaching various computer science courses ranging from programming to natural language processing. Dr. Aldakheel effectively adapted to remote teaching tools like Blackboard, Teams, and Zoom during the COVID-19 pandemic, ensuring uninterrupted learning for her students.

Honors and Awards:

Dr. Eman Aldakheel has actively participated in prestigious academic workshops and conferences, including the CRA-Women Grad Cohort Workshop, which supports the professional development of women in computing. Her academic achievements have earned her multiple travel awards, including ACM’s SRC (Student Research Competition) Travel Award and the HPDC (High Performance Distributed Computing) Travel Award, both of which provided her with opportunities to present her research and engage with global experts in the field. These accolades reflect her commitment to advancing her knowledge and contributing to the broader academic community.

Service Activities:

Dr. Eman Aldakheel has played a pivotal role in fostering the growth and development of talented students at Princess Nourah Bint Abdulrahman University. She has been actively involved in planning programs and activities aimed at nurturing high-achieving students, ensuring they receive the support and opportunities needed to excel. Dr. Aldakheel designed and built the foundation for the “Foundations of Programming” (GN 044) course, recording its lectures to enhance learning accessibility. Additionally, she supervises the College of Computer and Information student magazine, encouraging student participation in scholarly activities. Her involvement extends to various committees, where she serves as a judge or supervisor for hackathons, contributing her expertise to inspire innovation and creativity among students.

Granted Projects:

Dr. Eman Aldakheel is actively involved in several significant research projects. In 2023, she contributed to the “Researchers Supporting Project” at Princess Nourah Bint Abdulrahman University, under project number PNURSP2023R409. She is also leading two research initiatives funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia. The first, under project number RI-44-0618, focuses on the “Detection and Identification of Plant Leaf Diseases using YOLOv4,” running from November 2022 to May 2024. The second project, WE-44-0279, explores the “Use of Modern Machine Learning Techniques to Combat Extremism and the Role of Women,” also from November 2022 to May 2024. These projects highlight Dr. Aldakheel’s expertise in machine learning and its application to various fields, from agriculture to social issues.

Top Notable Publications

“Performance of Rime-Ice Algorithm for Estimating the PEM Fuel Cell Parameters”

Authors: Ismaeel, A.A.K., Houssein, E.H., Khafaga, D.S., Aldakheel, E.A., Said, M.

Year: 2024

Journal: Energy Reports

Citations: 3

“Outlier Detection for Keystroke Biometric User Authentication”

Authors: Ismail, M.G., Salem, M.A.-M., El Ghany, M.A.A., Aldakheel, E.A., Abbas, S.

Year: 2024

Journal: PeerJ Computer Science

Citations: 0

“Mobile-UI-Repair: A Deep Learning-Based UI Smell Detection Technique for Mobile User Interface”

Authors: Ali, A., Xia, Y., Navid, Q., Aldakheel, E.A., Khafaga, D.

Year: 2024

Journal: PeerJ Computer Science

Citations: 2

“Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies”

Authors: Ghani, M.A.N.U., She, K., Rauf, M.A., Aldakheel, E.A., Khafaga, D.S.

Year: 2024

Journal: Computers, Materials and Continua

Citations: 0

“Detection and Identification of Plant Leaf Diseases using YOLOv4”

Authors: Aldakheel, E.A., Zakariah, M., Alabdalall, A.H.

Year: 2024

Journal: Frontiers in Plant Science

Citations: 1

“Performance of the Walrus Optimizer for Solving an Economic Load Dispatch Problem”

Authors: Said, M., Houssein, E.H., Aldakheel, E.A., Khafaga, D.S., Ismaeel, A.A.K.

Year: 2024

Journal: AIMS Mathematics

Citations: 2

“Efficient Analysis of Large-Size Bio-Signals Based on Orthogonal Generalized Laguerre Moments of Fractional Orders and Schwarz–Rutishauser Algorithm”

Authors: Aldakheel, E.A., Khafaga, D.S., Fathi, I.S., Hosny, K.M., Hassan, G.

Year: 2023

Journal: Fractal and Fractional

Citations: 1

“Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem”

Authors: Ismaeel, A.A.K., Houssein, E.H., Khafaga, D.S., AbdElrazek, A.S., Said, M.

Year: 2023

Journal: Mathematics

Citations: 17

“CyberHero: An Adaptive Serious Game to Promote Cybersecurity Awareness”

Authors: Hodhod, R., Hardage, H., Abbas, S., Aldakheel, E.A.

Year: 2023

Journal: Electronics (Switzerland)

Citations: 3

Carrasquilla, M.D.L., Sun, M., Long, T., Huang, L., & Zheng, Y. (2024). Seismic anisotropy of granitic rocks from a fracture stimulation well at Utah FORGE using ultrasonic measurements. Geothermics, 123, 103129.

Carrasquilla, M.D.L., Parsons, J., Long, T., Zheng, Y., & Han, D.-H. (2023). Ultrasonic measurements of elastic anisotropy of granitic rocks for enhanced geothermal reservoirs. SEG Technical Program Expanded Abstracts, 2023-August, 79–83.

Carrasquilla, M.D.L., Costa, M.D.F.B., Souza, I.J.S., Amanajás, C.E., & Nunes, L.R.A. (2022). Geological, geophysical and mathematical analysis of synthetic bulk density logs around the world – Part II – The use of non-linear regression on empirical parameters estimation. Journal of Applied Geophysics, 206, 104838.

Carrasquilla, M.D.L., Carvalho, C.P., Costa, M.D.F.B., Amanajás, C.E., & Rautino, L. (2022). Geological, geophysical and mathematical analysis of synthetic bulk density logs around the world – Part I – The use of linear regression on empirical parameters estimation. Journal of Applied Geophysics, 204, 104733.