Aizhen Ren | Machine learning | Excellence in Innovation Award

Published on by Engineering Scientist

Prof. Aizhen Ren | Machine learning
| Excellence in Innovation Award

College of Science, Inner Mongolia Agricultural University | China

Prof. Aizhen Ren research work centers on machine learning, deep learning, statistical inference, and their applications in economics, finance, and computational biology. A significant portion of the research contributes to the development and mathematical validation of advanced bootstrap techniques, including the speedy double bootstrap method, which enhances the statistical reliability of phylogenetic tree estimation and provides third-order accurate unbiased p-values. These methods have been applied to evolutionary analyses of horse breeds, supporting biological and genomic investigations with high-precision statistical tools. In the financial domain, the research explores machine-learning-based trend prediction models, such as multiscale bootstrap-corrected random forest voting systems used to forecast stock index movement with improved accuracy and inference reliability. Additional work includes the construction of financial risk early-warning models for listed companies using multiple machine learning approaches, reflecting an interdisciplinary blend of statistics, computing, and economics. Contributions also extend to consumption behavior analysis employing regression-based models, as well as deep learning ensemble frameworks integrating empirical mode decomposition and temporal convolutional networks for time-series prediction tasks. The released R package SDBP operationalizes the novel bootstrap methodology, enabling researchers to compute unbiased p-values efficiently. Overall, the research advances methodological innovation and practical applications across data-intensive scientific domains.

 Profile: Orcid

Featured Publications

Ren, A., Duan, Y., & Liu, J. (2025). Multiscale bootstrap correction for random forest voting: A statistical inference approach to stock index trend prediction. Mathematics, 13(22), 3601. https://doi.org/10.3390/math13223601

Ren, A., Ishida, T., & Akiyama, Y. (2020). Mathematical proof of the third order accuracy of the speedy double bootstrap method. Communications in Statistics – Theory and Methods, 49(16), 3950–3964. https://doi.org/10.1080/03610926.2019.1594295

Ren, A., Ishida, T., & Akiyama, Y. (2013). Assessing statistical reliability of phylogenetic trees via a speedy double bootstrap method. Molecular Phylogenetics and Evolution, 67(2), 429–435. https://doi.org/10.1016/j.ympev.2013.02.011

Jamshaid Ul Rahman | Artificial Intelligence | Editorial Board Member

Published on by Engineering Scientist

Dr. Jamshaid Ul Rahman | Artificial Intelligence
| Editorial Board Member

Abdus Salam School of Mathematical Sciences, GC University Lahore | Pakistan

Dr. Jamshaid Ul Rahman’s research spans artificial intelligence, deep learning, mathematical modeling, and scientific computing, with a strong emphasis on applying advanced neural architectures to complex problems in molecular modeling, epidemiology, biomechanics, and computational physics. His work integrates graph neural networks, wavelet neural networks, periodic neural networks, and deep optimization frameworks to improve prediction accuracy and interpretability in molecular property analysis, virus transmission modeling, and dynamic system simulation. He has contributed significantly to AI-driven analysis of infectious diseases such as Marburg, Ebola, and Hepatitis C through nonlinear modeling, hybrid optimization, and neural simulation techniques. His research also advances computational mechanics, including deep learning-based vibration analysis in tapered beams, micro-electromechanical systems, and nonlinear oscillators. In cheminformatics, he has enhanced molecular property prediction using quantized GNNs, optimized activation functions, and improved training strategies for message-passing networks. His contributions extend to biophysical modeling, where neural networks simulate glycolysis, biochemical oscillators, and biological system dynamics with high fidelity. He has also explored angular softmax variations, Laplacian smoothing, and stochastic gradient methodologies in deep convolutional networks, advancing core theoretical aspects of machine learning. Across more than fifty publications, his work consistently integrates mathematical rigor with cutting-edge AI to address interdisciplinary scientific and engineering challenges.

 Profile:  Google Scholar

Featured Publications

Rahman, J. U., Noureen, I., Mannan, A., & Uwitije, R. (2025). PhyFold: Environment aware physics informed neural network for protein folding dynamics.

Ul Rahamn, J., Iqbal, M. A., Rasool, A., & Uwitije, R. (2025). p-GIN: A graph isomorphism network based on p-Laplacian operator to enhance molecular property prediction. Discover Applied Sciences, 7(11), 1251.

Shoket, N., Ul Rahman, J., & Zafar, N. (2025). Mathematical modeling and computational investigation of the COVID-19 epidemic using wavelet neural networks and coupled optimization. SN Soft Computing, 1–18.

Rahman, J. U., Ali, H., & Rassol, A. (2025). A comprehensive analysis of optimizers in message passing neural networks for molecular property prediction task. Computational Biology and Chemistry, 108556

Cyrille Feybesse | Data Science | Best Researcher Award

Published on by Engineering Scientist

Dr. Cyrille Feybesse | Data Science | Best Researcher Award

Dr. Cyrille Feybesse | Guillaume Regnier Hospital | France

Dr. Cyrille Feybesse, a French psychologist and Maître de Conférences (MCF, 2022), is a researcher and clinical psychologist specializing in love, creativity, and cross-cultural psychology. He earned his Ph.D. in Psychology (2015) at Université Paris Descartes under the supervision of Professors Geneviève Coudin and Todd Lubart, with additional mentorship from Professor Elaine Hatfield (University of Hawai‘i). His academic background spans clinical, health, and social psychology, with training in France, Portugal, and Brazil. Dr. Feybesse has held postdoctoral fellowships at Université Paris Descartes and the University of Porto, collaborating with the Portuguese Foundation for Science and Technology (FCT) on pioneering research into passionate love and creativity. Since 2022, he has been conducting clinical and research activities at the Centre Hospitalier Guillaume Régnier, Rennes, focusing on child psychiatry, creativity, and high-potential intelligence. He has authored over 14 publications, contributed to multiple book chapters, and presented at numerous international conferences. His work has been cited more than 350 times (Scopus Author ID: 57191835134; ORCID: 0000-0001-7795-568X) with an h-index of 6. Multilingual in English, Portuguese, and Spanish, Dr. Feybesse also serves as Assistant Editor for Interpersona and reviewer for leading journals including Sexuality & Culture and European Psychologist, advancing research at the intersection of love, creativity, and human development.

 Profile: Scopus | Orcid  | Researchgate 

Featured Publications

Feybesse, C., Forthmann, B., Neto, F., Holling, H., & Hatfield, E. (2025). Measuring love around the world: A cross-cultural reliability generalization. Sexuality & Culture. Advance online publication.

Feybesse, C. (2024). Social representation of passionate love among Brazilian and French youngsters. Trends in Psychology. Advance online publication.

Feybesse, C., Fu, S., Lubart, T., Rasa, L., Ossom, C., Cavasino, V., Jacob, J., & Lemonnier, T. (2020). Social representation of fair price among professional photographers. PLOS ONE, 15(12), e0243547.

Feybesse, C. (2018). Assessing passionate love: Italian validation of the PLS (reduced version). Sexual and Relationship Therapy, 33(2), 221–229.

Feybesse, C. (2016). Sensory values in romantic attraction in four European countries: Gender and cross-cultural comparison. Journal of Cross-Cultural Research, 50(2), 109–123.

Rui Zhang | Intelligent Manufacturing | Best Researcher Award

Published on by Engineering Scientist

Dr. Rui Zhang | Intelligent Manufacturing
| Best Researcher Award

Doctor at Northwestern Polytechnical University | China

Dr. Rui Zhang is a dedicated Ph.D. candidate at the School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China. His research lies at the intersection of intelligent manufacturing and data-driven quality control in CNC machining. Dr. Zhang has actively contributed to national research projects funded by the National Natural Science Foundation of China and Shaanxi Province. His innovative approaches have been published in respected journals such as Journal of Manufacturing Processes and Precision Engineering. He has also secured two patents on machining technologies. With a passion for improving manufacturing efficiency and precision, Dr. Zhang continues to push boundaries in predictive modeling and optimization techniques. His work stands as a testament to the integration of machine learning into modern manufacturing practices.

Professional Profile 

Scopus

ORCID

Suitability for the Best Researcher Award

Dr. Rui Zhang exemplifies excellence in manufacturing research, applying advanced data-driven and machine learning techniques to solve real-world problems in precision engineering. His pioneering work on multi-process machining error prediction for thin-walled blades has significantly reduced manufacturing deviations and improved operational efficiency. Dr. Zhang’s contributions have led to two national patents and several publications in leading SCI-indexed journals, reflecting both academic rigor and industrial relevance. His involvement in major national science and technology projects further underscores his capability to lead research with broad impact. As an emerging expert in intelligent manufacturing, Dr. Zhang’s dedication to high-quality, innovative research aligns perfectly with the values recognized by the Best Researcher Award.

Education 

Dr. Rui Zhang is currently pursuing his Ph.D. in Mechanical Engineering at Northwestern Polytechnical University (NPU), Xi’an, one of China’s leading research universities in engineering and technology. Through his doctoral studies, he has focused on intelligent manufacturing, predictive modeling, and process optimization in CNC machining. His academic training at NPU includes rigorous coursework in control theory, mechanical design, and machine learning. Rui’s education is complemented by active involvement in national research projects, which provided him with hands-on experience in applying theoretical concepts to practical industrial challenges. His academic background forms a strong foundation for his research endeavors and highlights his technical competence in both traditional and emerging areas of manufacturing engineering.

Work Experience 

Dr. Rui Zhang has acquired extensive experience through participation in multiple high-profile national research initiatives, including three projects under the National Natural Science Foundation of China and one under the Natural Science Basic Research Program of Shaanxi. His hands-on contributions to the National Science and Technology Major Project of China further demonstrate his ability to address complex industrial problems. Dr. Zhang has also authored papers in prestigious journals and is the inventor of two patented technologies related to precision machining. His practical experience spans developing machine learning models for error prediction, optimizing multi-process machining parameters, and implementing intelligent control strategies. These experiences position him as a young researcher capable of bridging the gap between theory and industrial application.

Awards and Honors

While Dr. Rui Zhang is still in the early stages of his academic career, his contributions have already earned recognition through competitive funding from major national research programs. He is the named recipient of support from three grants by the National Natural Science Foundation of China and one from the Shaanxi Province research fund. His research achievements have resulted in the publication of two Chinese invention patents, a significant mark of innovation and practical impact. Furthermore, he has published in high-impact journals recognized globally. These accomplishments reflect his rising stature in the field of intelligent manufacturing and make him a promising candidate for future academic and industry accolades, including the prestigious Best Researcher Award.

Research Focus 

Dr. Rui Zhang’s research focuses on intelligent manufacturing with an emphasis on CNC machining quality prediction and optimization. He integrates machine learning techniques to develop predictive models for machining errors, particularly for complex components like thin-walled blades. His recent work includes establishing a multi-process error prediction model and applying intelligent optimization algorithms to coordinate machining parameters, achieving improved accuracy and efficiency. His goal is to advance the use of AI in manufacturing, enabling real-time quality control and adaptive process improvement. By addressing key issues such as dimensional accuracy and error propagation in manufacturing processes, Dr. Zhang’s research contributes significantly to the development of high-precision, data-driven manufacturing systems.

Publication Top Notes

  1. Embedding Graph Auto-Encoder for Graph Clustering
    Year: 2022

  2. Graph Convolution RPCA with Adaptive Graph
    Year: 2022

  3. Manifold Neural Network with Non-Gradient Optimization
    Year: 2022

  4. Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning
    Year: 2022

  5. Unsupervised Graph Embedding via Adaptive Graph Learning
    Year: 2022

  6. Robust Kernel Principal Component Analysis with Optimal Mean
    Year: 2022

  7. Adaptive Graph Auto-Encoder for General Data Clustering
    Year: 2021

Conclusion

In conclusion, Dr. Rui Zhang exhibits the technical depth, innovative thinking, and applied focus that align well with the spirit of the Best Researcher Award. His contributions to intelligent manufacturing, particularly the fusion of AI with machining quality control, are timely and relevant. While there is scope for enhancing visibility and professional engagement, his research trajectory clearly signals future leadership in smart manufacturing technologies. Recognizing Dr. Zhang with this award would not only validate his efforts but also encourage further advancement in data-driven, efficient manufacturing systems.

Saqib Qamar | Artificial Intelligence | Best Researcher Award

Published on by Engineering Scientist

 Dr. Saqib Qamar |Artificial Intelligence
| Best Researcher Award

Assistant Professor at Sohar University, India.

Dr. Saqib Qamar is a dedicated Assistant Professor at Sohar University, Oman, specializing in computer science with expertise in medical image analysis and deep learning. With a Ph.D. from Huazhong University of Science and Technology, China, and postdoctoral research from Sweden’s top institutes—KTH and Umea University—he has demonstrated strong research capabilities through high-quality publications and international collaboration. His academic career spans teaching, curriculum development, and industry experience. Known for his work ethic, innovation, and research productivity, Dr. Qamar has a profound commitment to student success and scientific excellence. His scholarly contributions and interdisciplinary engagement make him a compelling candidate for the Best Researcher Award.

🌍 Professional Profile:

ORCID

Google Scholar

🏆 Suitability for the Best Researcher Award :

Dr. Saqib Qamar’s academic journey reflects consistent excellence and impactful contributions to computer science, particularly in medical image analysis using AI. With experience in global research environments and a record of peer-reviewed publications, he bridges theory with real-world applications. His Ph.D. work on 3D CNNs for brain MRI segmentation was both innovative and practically relevant. As a postdoctoral fellow at KTH and Umea University, he engaged in collaborative, cutting-edge research. He also actively mentors students and contributes to academic discourse. His awards, research leadership, and ongoing projects demonstrate a trajectory of influence, making him highly suitable for the Best Researcher Award.

🎓 Education :

Dr. Saqib Qamar earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2015–2019), with a dissertation focused on 3D CNN models for brain MRI segmentation. His doctoral research integrated deep learning with medical imaging and received the HUST Excellence Award. He completed his Master’s in Computer Science at Aligarh Muslim University, India (2010–2013), where he studied AI, programming, and software engineering, and was awarded a Merit Cum Means scholarship. His academic foundation was laid through a B.Sc. (Hons.) in Statistics from the same university (2007–2010), where he focused on probability, statistics, and linear algebra, graduating with First Division.

🏢 Work Experience :

Dr. Saqib Qamar is currently an Assistant Professor at Sohar University, Oman (2025–present), where he teaches and conducts research in AI and medical image computing. He was previously a postdoctoral fellow at KTH Royal Institute of Technology (2024–2025) and Umea University (2022–2024), Sweden, focusing on machine learning and deep learning applications. At Umea, he also taught deep learning courses. Earlier, he served as Assistant Professor at Madanapalle Institute of Technology and Science, India (2019–2021), where he taught programming and ML subjects. Prior to academia, he worked as a Database Developer at nServices, Delhi (2013–2015), specializing in Oracle-based data systems and programming.

🏅Awards and Honors

Dr. Saqib Qamar has been recognized for his academic performance and research excellence throughout his career. He received the HUST Excellence Award during his Ph.D. at Huazhong University of Science and Technology, acknowledging his exceptional work in medical image segmentation using deep learning. He was also awarded the Merit Cum Means Scholarship by the Government of India during his Master’s studies. His postdoctoral research in Sweden was supported through prestigious international fellowships, and he has contributed to multiple international projects. Additionally, he has received appreciation for teaching excellence and academic service in both India and Oman. His record of honors reflects his dedication to advancing AI and medical informatics research globally.

🔬 Research Focus :

Dr. Saqib Qamar’s research focuses on medical image analysis, deep learning, and 3D convolutional neural networks. His doctoral work centered on brain MRI segmentation, proposing efficient and parallelized CNN architectures. He has expanded his expertise during postdoctoral stints in Sweden, exploring advanced AI techniques in healthcare imaging, neural cell recognition, and explainable AI. His work integrates datasets from MRI and CT scans with machine learning algorithms to enhance diagnostic capabilities. He is also interested in real-time data processing, parallel computing, and interpretable AI models. With an aim to bridge clinical needs with computational innovation, Dr. Qamar’s research contributes significantly to the domains of health informatics and intelligent medical systems.

📊 Publication Top Notes:

📘 Techniques of data mining in healthcare: a review
🗓️ Year: 2015 | ✍️ P Ahmad, S Qamar, SQA Rizvi | 📖 International Journal of Computer Applications | 🔢 Cited by: 174 📊

🧠 A variant form of 3D-UNet for infant brain segmentation
🗓️ Year: 2020 | ✍️ S Qamar, H Jin, R Zheng, P Ahmad, M Usama | 📰 Future Generation Computer Systems | 🔢 Cited by: 132 🧬

🦴 CT-based automatic spine segmentation using patch-based deep learning
🗓️ Year: 2023 | ✍️ SF Qadri, H Lin, L Shen, M Ahmad, S Qadri, S Khan, M Khan, SS Zareen, S Qamar | 🧾 International Journal of Intelligent Systems | 🔢 Cited by: 77 🧠

🧠 Context aware 3D UNet for brain tumor segmentation
🗓️ Year: 2020 | ✍️ P Ahmad, S Qamar, L Shen, A Saeed | 📘 MICCAI Brainlesion Workshop | 🔢 Cited by: 57 🧪

🧬 MH UNet: A multi-scale hierarchical based architecture for medical image segmentation
🗓️ Year: 2021 | ✍️ P Ahmad, H Jin, R Alroobaea, S Qamar, R Zheng, F Alnajjar, F Aboudi | 📰 IEEE Access | 🔢 Cited by: 51 🔬

🧴 Dense encoder-decoder–based architecture for skin lesion segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 🧠 Cognitive Computation | 🔢 Cited by: 50 🧪

🧠 HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 📘 Brainlesion: Glioma, MS, Stroke and TBI Workshop | 🔢 Cited by: 46 💡

Hao Zhang | Artificial Intelligence | Best Researcher Award

Published on by Engineering Scientist

Dr. Hao Zhang | Artificial Intelligence
| Best Researcher Award

Associate professor at Carnegie Mellon University, United States.

Hao Zhang is a Research Associate at Carnegie Mellon University (CMU), conducting postdoctoral research at the Safe AI Lab under Prof. Ding Zhao. He also serves as the Associate Director of the ETAIC Research Lab at the University of Texas at Arlington, led by Prof. Eric Tseng (NAE Member). He holds a Ph.D. from Tsinghua University, co-advised by Prof. Zhi Wang and Prof. Shengbo Eben Li. With over 35 SCI/EI publications and 17 patents, his research advances multi-agent reinforcement learning and closed-loop LLMs for real-world AI deployment in autonomous vehicles, robotics, and smart energy systems. He collaborates globally with academic and industrial leaders such as BYD, SAIC, Dongfeng Motor, and UCL, making impactful contributions to intelligent mobility.

🌍 Professional Profile:

ORCID

Google Scholar 

Scopus 

🏆 Suitability for the Best Researcher Award :

Dr. Hao Zhang is an exceptional candidate for the Best Researcher Award due to his groundbreaking work at the intersection of artificial intelligence and real-world applications. His achievements in scalable AI for micro-mobility and autonomous vehicles have led to industrial deployments across leading automotive manufacturers. With a strong publication record, global collaborations, and 17 patents, he exemplifies innovation, impact, and leadership. He bridges theory and practice, pushing the boundaries of safe and trustworthy AI agents. His dual appointments at CMU and UTA and contribution to both academia and industry reflect his versatile excellence. Dr. Zhang’s work not only enhances technological advancement but also fosters a responsible and intelligent future for mobility and energy systems.

🎓 Education :

Hao Zhang received his Ph.D. in Mechanical Engineering from Tsinghua University, one of China’s most prestigious institutions, where he was co-advised by renowned scholars Prof. Zhi Wang and Prof. Shengbo Eben Li. During his Ph.D., he focused on reinforcement learning and its applications to intelligent vehicle systems. Prior to that, he completed his undergraduate and master’s studies with distinction, developing a strong foundation in robotics, automation, and control systems. His education also included collaborative learning experiences with industry, which laid the groundwork for his multidisciplinary approach to research. Currently, he is expanding his expertise through postdoctoral research at Carnegie Mellon University, contributing to the development of safe AI systems under the mentorship of Prof. Ding Zhao.

🏢 Work Experience :

Dr. Zhang has a rich portfolio of academic and industrial experience. As a Research Associate at Carnegie Mellon University, he works at the forefront of AI safety, while simultaneously serving as Associate Director at the ETAIC Lab at UTA. He has led or participated in five major government-funded research projects and four OEM-sponsored industry projects. His efforts have directly supported intelligent system development for companies such as BYD Auto, SAIC Motor, and Dongfeng. His engineering solutions have real-world applications in autonomous driving, energy management, and mobile robotics. His academic roles, coupled with his industrial consultancy, enable him to effectively translate research into practice. Dr. Zhang’s interdisciplinary experience sets him apart as a leader in applied AI and automation.

🏅Awards and Honors

Hao Zhang’s research excellence has earned him notable recognition across academia and industry. He has published over 35 SCI/EI-indexed journal articles, authored a technical book (ISBN: 9780443329845), and holds 17 patents related to intelligent control and autonomous systems. His work has been cited nearly 500 times, demonstrating significant influence. His research contributions have been integrated into industrial platforms at BYD and Dongfeng, marking a rare crossover between lab and large-scale deployment. Although he is still early in his postdoctoral career, his consistent innovation and impact have made him a rising leader in AI-powered mobility. His contributions position him for prestigious honors such as the Best Researcher Award and similar recognitions for scientific leadership.

🔬 Research Focus :

Dr. Zhang’s research focuses on scalable and trustworthy AI for autonomous systems and smart energy applications. His core expertise includes multi-agent reinforcement learning, closed-loop large language models (LLMs), and intelligent motion control. He develops AI algorithms that can be safely deployed in micro-mobility devices (assistive and mobile robots), connected vehicles, and distributed energy platforms. His work contributes to both algorithmic innovation and real-world adoption, ensuring AI agents are reliable, interpretable, and responsive to dynamic environments. He is particularly interested in bridging theory with practice by collaborating with top-tier institutions and OEMs. Dr. Zhang’s interdisciplinary approach merges robotics, automotive systems, control engineering, and deep learning to create adaptive, secure, and energy-efficient intelligent agents.

📊 Publication Top Notes:

📘 Impact of ammonia addition on knock resistance and combustion performance in a gasoline engine with high compression ratio
📅 Year: 2023 | 📊 Cited by: 75 | 🛠️ Energy efficiency, combustion

📘 Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine
📅 Year: 2021 | 📊 Cited by: 42 | ⚡ Hybrid vehicles, control systems

📘 Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles
📅 Year: 2023 | 📊 Cited by: 40 | 📡 Connected vehicles, optimization

📘 Experimental study on combustion and emission characteristics of ethanol-gasoline blends in a high compression ratio SI engine
📅 Year: 2023 | 📊 Cited by: 36 | 🔬 Fuel science, engine performance

📘 Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information
📅 Year: 2022 | 📊 Cited by: 34 | 🤖 AI control, mobility systems

📘 Integrated thermal and energy management of connected hybrid electric vehicles using deep reinforcement learning
📅 Year: 2023 | 📊 Cited by: 30 | 🧠 Deep learning, hybrid energy systems