Dr. Aiai Wang | Machine Learning | Best Researcher Award

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Dr. Aiai Wang | Machine Learning | Best Researcher Award

Doctoral student, University of Science and Technology Beijing, China

Ai-Ai Wang is a passionate and dedicated young researcher born in March 1998 in Langfang, Hebei Province, China. A proud member of the Communist Party of China (CPC), she is currently based at the University of Science and Technology Beijing (USTB), where she serves as the Secretary of the 16th Party Branch, 4 Zhaizhai. With a solid academic foundation in mining and civil engineering, Ai-Ai has excelled in both academic and research spheres, contributing significantly to digital and intelligent mining technologies. Her work emphasizes physical dynamics in tailings sand cementation and filling, showing strong potential for innovation in sustainable mining practices.

Publication Profile

Scopus

🎓Education Background:

Ai-Ai Wang completed her Bachelor of Science in Mining Engineering from North China University of Science and Technology in 2021. She further pursued her Master’s degree in Civil Engineering at the University of Science and Technology Beijing (2021.09–2024.06), affiliated with the School of Civil and Resource Engineering.

🛠️Professional Experience:

Alongside her academic journey, Ai-Ai has undertaken significant responsibilities, currently serving as Secretary of the Party Branch at USTB. Her leadership extends beyond administration into collaborative research projects, software development, and patent contributions under renowned mentors such as Prof. Cao Shuai. She has played vital roles in developing intelligent systems for mining operations, reinforcing her multidisciplinary strengths.

🏅Awards and Honors:

Ai-Ai Wang has been recognized extensively for her academic and research excellence. Notable accolades include the “Top Ten Academic Stars” at USTB (2023), a National Scholarship for Master’s Degree Students (2022), the prestigious Taishan Iron and Steel Scholarship (2023), and multiple First-Class Academic Scholarships from USTB. She was twice named an Outstanding Three-Good Graduate Student and honored by her school as an outstanding individual. Moreover, she has received scientific awards such as the First Prize from the China Gold Association and the Second Prize from the China Nonferrous Metals Industry for her impactful contributions to green and safe mining.

🔬Research Focus:

Ai-Ai Wang’s research is rooted in advanced techniques of tailings sand cementation, intelligent filling systems, and digital mining. She explores the structural stability of backfills, application of nanomaterials, and CT-based 3D modeling of internal structures. Her work blends civil engineering, environmental safety, and digital innovation, aiming to enhance sustainability and efficiency in modern mining. She also contributes to cutting-edge software systems and patented technologies for mining design and operation support.

📝Conclusion:

Ai-Ai Wang stands out as a promising engineer and researcher whose academic achievements, professional dedication, and innovative research in intelligent mining set a high standard for future civil and mining engineers. Her trajectory reflects not just technical mastery but a deep commitment to sustainable and smart engineering solutions in the mining industry.

📚Top Publications with Details

Effect of height to diameter ratio on dynamic characteristics of cemented tailings backfills with fiber reinforcement through impact loading – Construction and Building Materials, 2022
Cited by: 26 articles
Influence of types and contents of nano cellulose materials as reinforcement on stability performance of cementitious tailings backfill – Construction and Building Materials, 2022
Cited by: 20 articles
Quantitative analysis of pore characteristics of nanocellulose reinforced cementitious tailings fills using 3D reconstruction of CT images – Journal of Materials Research and Technology, 2023
Cited by: 12 articles

 

Rab Nawaz Bashir | Machine Learning | Best Researcher Award

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Dr. Rab Nawaz Bashir | Machine Learning | Best Researcher Award

Assistant Professor, COMSATS University, Pakistan

Dr. Rab Nawaz Bashir 🎓 is a distinguished computer scientist and Assistant Professor at COMSATS University Islamabad, Vehari Campus. He holds a Ph.D. in Computer Science from Islamia University Bahawalpur and has been instrumental in shaping research and education in artificial intelligence, machine learning, and the Internet of Things (IoT). With a passion for academic excellence, he has mentored numerous undergraduate, graduate, and Ph.D. students, guiding them toward impactful research and industry readiness. His dedication to scholarly contributions and innovative teaching has made him a respected figure in the field of computer science.

Publication Profile

Education 🎓

Dr. Bashir has a strong academic foundation, earning his Ph.D. in Computer Science (2021) from Islamia University Bahawalpur. Prior to that, he completed an MS in Computer Science (2015) from the same institution. His educational journey began with a Master in Computer Science (MCS) from Pir Mehr Ali Shah University of Arid Agriculture (2008). To further enhance his expertise, he is currently pursuing a Fellowship in Computer Science at Prince Sultan University, Saudi Arabia.

Experience 👨‍🏫

With over 15 years of experience, Dr. Bashir has significantly contributed to academia and research. Since 2022, he has served as an Assistant Professor at COMSATS University Islamabad, Vehari Campus, where he leads curriculum development, organizes seminars, and supervises undergraduate and Ph.D. research. Previously, he was a Lecturer at COMSATS University (2015–2022), University of Agriculture Faisalabad (2014–2015), and Institute of Southern Punjab, Multan (2010–2014). Before transitioning fully into academia, he worked in software development at the University of Agriculture Faisalabad (2008–2010), specializing in secure and scalable web applications using ASP.NET and SQL Server.

Awards and Honors 🏆

Dr. Bashir has been recognized multiple times for his outstanding contributions to research and academic excellence. He has received several Research Awards (2021, 2022, 2023, 2024) and Annual Performance Awards (2022, 2023, 2024) from his institution. Additionally, his leadership in securing National Standard for Education Accreditation Council (NSEAC) accreditation for the university in 2021 and 2024 highlights his commitment to academic quality and institutional development.

Research Focus 🔬

Dr. Bashir’s research spans various cutting-edge domains, including machine learning, IoT, and computer programming. His work emphasizes real-world applications, such as IoT-enabled smart agriculture, machine learning-based fraud detection, and deep learning for natural image processing. His interdisciplinary collaborations have resulted in high-impact research, contributing to advancements in federated learning, fog computing, and agricultural technology.

Conclusion 🌟

Dr. Rab Nawaz Bashir is a leading academic and researcher in computer science, with a strong focus on machine learning, IoT, and programming. His dedication to mentoring students, publishing impactful research, and advancing academic excellence has earned him numerous awards and recognitions. With a vision for future innovations, he continues to push the boundaries of computer science, contributing to both academia and industry through groundbreaking research and leadership.

Publications 📚

A Novel 1-Dimensional Cosine Chaotic Equation and Digital Image Encryption Technique (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3447889

Federated Learning (FL) Model of Wind Power Prediction (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3415781

IoT-Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning Machine (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3379327

Mushroom Species Classification in Natural Habitats Using Convolutional Neural Networks (CNN) (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3502543

Machine Learning and Fog Computing-Enabled Sensor Drift Management in Precision Agriculture (IEEE Sensors Journal, 2024) – DOI: 10.1109/JSEN.2024.3451662

Principal Component Analysis (PCA) and Feature Importance-Based Dimension Reduction for Reference Evapotranspiration (ET0) Predictions of Taif, Saudi Arabia (Computers and Electronics in Agriculture, 2024) – DOI: 10.1016/j.compag.2024.109036

Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3310576

Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network  (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3301504

Stacked Ensemble Model for Tropical Cyclone Path Prediction (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3292907

Smart Reference Evapotranspiration Using Internet of Things and Hybrid Ensemble Machine Learning Approach (Internet of Things, 2023) – DOI: 10.1016/j.iot.2023.100962

Mr. Ali Beikmohammadi | Machine Learning | Best Researcher Award

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Mr. Ali Beikmohammadi | Machine Learning | Best Researcher Award

PhD Researcher, Stockholm University, Sweden

👨‍💻 Ali Beikmohammadi is a dedicated researcher in Reinforcement Learning, Deep Learning, and Federated Learning. Currently pursuing his Ph.D. in Computer and Systems Sciences at Stockholm University, Sweden, he has made remarkable contributions to AI research, publishing 15+ papers in top-tier conferences and journals. With a strong foundation in stochastic optimization, telecommunications, and cyber-physical systems, Ali has worked on various industry projects and supervised 30+ Master’s students. His expertise extends to high-performance computing, AI applications in healthcare, and distributed learning, making him a highly influential figure in AI research. 🚀

Publication Profile

Education

🎓 Ali holds a Ph.D. in Computer and Systems Sciences (2021–Present) from Stockholm University, Sweden, where he focuses on sample-efficient reinforcement learning and AI-driven optimization. He earned an M.Sc. in Electrical Engineering (Digital Electronic Systems) (2017–2019) from Amirkabir University of Technology, Iran, specializing in deep learning for plant classification. His B.Sc. in Electrical Engineering (Electronics) (2013–2017) from Bu-Ali Sina University, Iran, involved research on license plate recognition using computer vision. 📚

Experience

💡 With extensive research and industry collaborations, Ali has supervised 30+ Master’s students at Stockholm University and Karolinska Institutet, applying AI to healthcare, recommendation systems, forecasting, and network optimization. He has also instructed 91 students in Health Informatics courses, focusing on time-series analysis, deep learning, and reinforcement learning. His industry collaborations include Scania CV AB, Hitachi Energy, and the University of California, where he played key roles in algorithm design, pipeline development, and AI-driven performance optimization. 🤖

Awards and Honors

🏆 Ali’s exceptional contributions to AI and engineering have earned him prestigious scholarships such as the Lars Hierta Memorial Foundation Scholarship (2025) and the Rhodins, Elisabeth, and Herman Memory Scholarship (2024). He is a member of the Iran National Elites Foundation and has received the Outstanding Paper Award at the 5th ICSPIS’19 Conference. His academic excellence is further highlighted by ranking 1st in GPA during his B.Sc. and M.Sc. studies. 🌟

Research Focus

🔬 Ali’s research revolves around Reinforcement Learning, Deep Learning, and Federated Learning, with a strong emphasis on stochastic optimization, telecommunications, and cyber-physical systems. His recent work explores teacher-assisted reinforcement learning, federated learning without data similarity constraints, and cost-sensitive AI models for industrial applications. His contributions aim to enhance AI’s efficiency, scalability, and applicability across domains like healthcare, robotics, and automation. ⚙️

Conclusion

🌍 Ali Beikmohammadi is an accomplished AI researcher, educator, and industry collaborator pushing the frontiers of Reinforcement Learning, Deep Learning, and Federated Learning. With multiple high-impact publications, prestigious awards, and hands-on experience in AI-driven solutions, he continues to bridge the gap between academic research and real-world AI applications. His passion for cutting-edge AI innovations positions him as a leading voice in modern AI research. 🚀✨

Publications

Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Upsampling under Varying Imbalance Levels

TA-Explore: Teacher-assisted exploration for facilitating fast reinforcement learning – Published at International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2023).  Paper Link

Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms – Artificial General Intelligence Conference (2023).  Paper Link

Human-inspired framework to accelerate reinforcement learning – arXiv (2023).  Paper Link

Compressed federated reinforcement learning with a generative model – ECML-PKDD (2024).  Paper Link

On the Convergence of Federated Learning Algorithms without Data Similarity – IEEE Transactions on Big Data (2024).  Paper Link

Parallel Momentum Methods Under Biased Gradient Estimations – IEEE Transactions on Control of Network Systems (2025).  Paper Link

A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data – arXiv (2024).  Paper Link

Ehsan Mansouri | Machine learning | Best Researcher Award

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Mr. Ehsan Mansouri | Machine learning | Best Researcher Award

Researcher, Inha University, South Korea

🧑‍💻 Ehsan Mansouri is a passionate software engineer and researcher specializing in cloud computing, machine learning, and data science. With a strong background in software engineering, he is committed to pushing the boundaries of technology to solve complex problems. Ehsan is currently a researcher at the Industrial Science and Technology Research Institute, Inha University, South Korea, where he explores innovative solutions in optimization and human-computer interaction.

Publication Profile

Google Scholar

Strengths for the Award

  1. Strong Educational Background: Ehsan Mansouri holds a Master’s degree in Software Engineering, specializing in cloud computing and optimization algorithms. His thesis focused on improving server efficiency in cloud environments, demonstrating his deep understanding of cutting-edge technology.
  2. Diverse Research Interests and Experience: His research spans multiple areas, including Machine Learning, Data Science, Cloud Computing, and Human-Computer Interaction, reflecting a broad and interdisciplinary approach. This is complemented by his experience as a researcher at Inha University and software engineer roles in both academic and private sectors.
  3. Publication Record: Mansouri has an impressive record of peer-reviewed publications, with multiple articles in reputable journals like Buildings, Journal of Forecasting, and Steel and Composite Structures. His works cover a range of topics from machine learning applications to software development for thermal analysis, illustrating his versatility as a researcher.
  4. Technical Proficiency: Proficient in various programming languages and machine learning frameworks, Mansouri has the technical skills required for developing innovative solutions. His expertise in data processing and visualization further enhances his research capabilities.
  5. Active Research Projects: Mansouri is actively involved in current research projects, such as developing machine learning models for predicting shear capacity of composite beams, highlighting his ongoing contributions to the field.

Areas for Improvement

  1. Focus on Core Research Strengths: While Mansouri has a broad range of research interests, a more concentrated focus on a specific niche area might help solidify his standing as an expert in that domain. This would enhance his recognition and impact in a highly specialized field.
  2. Increased Collaboration and Networking: Engaging in more international collaborations and expanding his network beyond his current institutions could amplify his research visibility and impact. This could include presenting at more international conferences and participating in cross-institutional research projects.
  3. Further Development of Communication Skills: Although Mansouri has achieved a high TOEFL score, enhancing his speaking skills (currently at 20/30) could improve his ability to present research findings effectively and engage with the global academic community more fluently.

 

Education

🎓 Master of Science in Software Engineering from Azad University of Birjand, Iran, where Ehsan developed a new data replication algorithm in cloud computing using particle swarm optimization. He also holds a Bachelor of Science in Software Engineering from the University of Birjand, Iran. His early education was at the National Organization for Development of Exceptional Talents (NODET) in Birjand, Iran.

Experience

💼 Ehsan has held various positions, including Researcher at Inha University, South Korea, and Software Engineer Expert at Birjand University of Medical Sciences and Butia’s Intelligent Sense of Communication in Iran. His experience ranges from software engineering to implementing innovative research projects in academia and private sectors.

Research Focus

🔍 Ehsan’s research interests include Machine Learning, Data Science, Cloud Computing, Human-Computer Interaction, Optimization, Data Grid, and Time Series Analysis. He is driven by a passion for creating efficient, scalable, and intelligent systems that enhance user experience and computational performance.

Awards and Honors

🏆 Ehsan Mansouri has achieved notable recognition in his field for his innovative work in cloud computing and data replication strategies, contributing significantly to enhancing server efficiency and optimization techniques in computational environments.

Publication Top Notes

📚 Ehsan has published several impactful papers in leading journals, including the Journal of Forecasting, Buildings, Steel and Composite Structures, and the Journal of Sustainability. His research contributions have been recognized and cited widely by peers in the field.

Ferreira, F.P.V., Jeong, S.H., Mansouri, E., Shamass, R., Tsavdaridis, K., Martins, C.H., De Nardin, S. (2024). Five Machine Learning Models Predicting the Global Shear Capacity of Composite Cellular Beams with Hollow-Core Units. Buildings. Link. Cited by: [2 articles].

Adnan, R.M., Mostafa, R.R., Dai, H.L., Mansouri, E., Kisi, O., Zounemat‐Kermani, M. (2024). Comparison of Improved Relevance Vector Machines for Streamflow Predictions. Journal of Forecasting, 43(1). Link. Cited by: [1 article].

Jeong, S.H., Mansouri, E., Ralston, N., Hu, J.W. (2024). An Advanced Software Interface to Make OpenSees for Thermal Analysis of Structures More User-Friendly. Steel and Composite Structures, 51(2). Link. Cited by: [3 articles].

Sabzekar, M., Mansouri, E., Deldari, A. (2023). A Data Replication Algorithm for Improving Server Efficiency in Cloud Computing Using PSO and Fuzzy Systems. Computer and Knowledge Engineering, 6(12), 1-14. Link. Cited by: [5 articles].

Mansouri, E., Manfredi, M., Hu, J.W. (2022). Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning. Journal of Sustainability, 14(20), 12990. Link. Cited by: [4 articles].

Conclusion

Ehsan Mansouri is a strong candidate for the Research for Best Researcher Award, given his robust educational background, diverse research interests, impressive publication record, and technical expertise. To further strengthen his candidacy, focusing on core research areas, expanding international collaborations, and refining communication skills would be beneficial. Overall, Mansouri demonstrates significant potential and contributions to the field of computer science and software engineering.