Prof. Dr Chih-Yung Tsai | Machine learning | Best Researcher Award |

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Prof. Dr Chih-Yung Tsai | Machine learning | Best Researcher Award

Professor, at University of Taipei, Taiwan.

Prof. Dr. Chih-Yung Tsai is a distinguished academic and researcher specializing in electrical engineering and nanotechnology. He is currently a professor at the Department of Electrical Engineering, National Taiwan University (NTU). Dr. Tsai’s work has significantly advanced the fields of nanolithography, integrated circuit design, and advanced manufacturing processes

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🎓 Education 

Dr. Tsai earned his Bachelor of Science and Master of Science degrees from National Taiwan University. He then pursued his Ph.D. in Aeronautics and Astronautics, with a minor in Electrical Engineering, at Stanford University, completing it in 2002.

💼 Experience 

From 2002 to 2005, Dr. Tsai served as a Senior Process Engineer in lithography at Intel Corporation. During this time, he worked on performance monitoring and improvement of 193-nm microlithography scanners for Intel’s 90-nm process technology. In 2005, he joined the faculty of NTU’s Department of Electrical Engineering, where he established and directed several research laboratories, including the Nanoscale Design and Fabrication Systems Lab (NDFSL), Particle Beam Precision Patterning and Imaging Lab (PBPPIL), and High-Performance Servo Systems Lab (HPSSL).

🔬 Research Interests 

Dr. Tsai’s research centers on the design and application of advanced control, simulation, signal processing, optimization, and machine learning techniques to solve nanolithography and nanotechnology-related problems. His current focus includes the design and fabrication techniques of exploratory nanoscale integrated circuits at the IRDS 10 nm half-pitch node and beyond. He is also involved in aerospace-related research activities and has conducted research on control system design automation, automotive and aerospace electronics, consumer electronics, and biomedical equipment design.

🏆 Awards 

Dr. Tsai has been recognized for his contributions to the field with several honors. He has been invited by The Japan Society of Applied Physics to serve as Program Committee Section Sub Head in International Microprocesses and Nanotechnology since 2007. He has also been invited to present on innovative applications of helium ion beam nanofabrication and techniques for EUV mask defect detection technology research and development at various international workshops and seminars.

📚 Top Noted Publications 

Title: Applying the theory of planned behavior to explore the independent travelers’ behavior
Author(s): CY Tsai
Citations: 109
Index: African Journal of Business Management 4 (2), 221
Year of Publication: 2010

Title: Using the technology acceptance model to analyze ease of use of a mobile communication system
Author(s): CY Tsai, CC Wang, MT Lu
Citations: 79
Index: Social Behavior and Personality: an international journal 39 (1), 65-69
Year of Publication: 2011

Title: The impact of tour guides’ physical attractiveness, sense of humor, and seniority on guide attention and efficiency
Author(s): CY Tsai, MT Wang, HT Tseng
Citations: 55
Index: Journal of Travel & Tourism Marketing 33 (6), 824-836
Year of Publication: 2016

Title: An analysis of usage intentions for mobile travel guide systems
Author(s): CY Tsai
Citations: 48
Index: African Journal of Business Management 4 (14), 2962
Year of Publication: 2010

Title: Learning under time pressure: Learners who think positively achieve superior learning outcomes from creative teaching methods using picture books
Author(s): CY Tsai, YH Chang, CL Lo
Citations: 39
Index: Thinking Skills and Creativity 27, 55-63
Year of Publication: 2018

Conclusion

Prof. Dr. Chih-Yung Tsai’s illustrious career in electrical engineering and nanotechnology has significantly advanced the understanding and application of nanolithography and integrated circuit design. His dedication to research, education, and innovation continues to inspire and shape the future of technology.

Prof. Dr ALEN JUGOVIC | TECHNOLOGY | Best Researcher Award |

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Prof. Dr. ALEN JUGOVIC | TECHNOLOGY | Best Researcher Award

FULL PROFESSOR , at University of Rijeka, Faculty of Maritime Studies,Croatia

Prof. Dr. Alen Jugović is a distinguished Croatian academic and maritime economist with a profound impact in the field of port systems, maritime transport, and logistics. With over two decades of experience in higher education, research, and strategic project leadership, he is a full professor at the University of Rijeka, Faculty of Maritime Studies, where he also served as Dean from 2016 to 2022. His leadership and academic vision have significantly advanced maritime education and research infrastructure both in Croatia and across the Adriatic region.

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Education

Prof. Jugović holds a Ph.D. in Maritime Economics, with his doctoral work focused on economics of infrastructure projects in ports and port systems development. His education blends strong theoretical foundations with practical insight into the maritime transport sector. He has further honed his expertise through continuous collaboration with leading academic institutions and participation in inter-university doctoral programs.

Experience

Prof. Jugović has a rich professional background that combines academic excellence with institutional leadership. From 2016 to 2022, he served as Dean of the Faculty of Maritime Studies, overseeing academic programs, research initiatives, and administrative reforms. Previously, he held the position of Vice Dean for Finance (2013–2016). He has taught key courses such as Maritime Economics, Port Business, Passenger Traffic, and Entrepreneurship, and has been a guest lecturer at universities across Croatia. He has led or participated in over 90 strategic and expert projects, including EU-funded IPA cross-border cooperation programs like PROMARES, MIMOSA, and FRAMESPORT.

In addition, he is the founder of the Maritime Science and Technology Research Hub (MASTER Hub), a cutting-edge facility hosting labs in virtual reality, refrigeration technology, materials, and cognitive neuroscience. He also established educational centers and foundations supporting student excellence and social equity.

Research interest

Prof. Jugović’s research revolves around maritime economics, port business management, passenger maritime transport, and entrepreneurship in logistics. He is especially recognized for his work on infrastructure development, multimodal transport systems, and innovation in port operations. His interdisciplinary approach bridges economic theory with operational and policy-driven maritime logistics, making his work highly applicable to both academia and industry.

Awards

In 2021, he was honored with a national award from the Ministry of Sea, Traffic, and Infrastructure for his outstanding contribution to the development and international reputation of Croatian maritime transport. This recognition reflects his longstanding influence and thought leadership in shaping the future of the shipping and port industries.

Publications

“Seaports and Economic Growth: Panel Data Analysis of EU Port Regions”
Authors: G. Mudronja, A. Jugović, D. Škalamera-Alilović
Published in Journal of Marine Science and Engineering, Vol. 8(12), 2020
Citations: 81

“MenadĹľement pomorskoputniÄŤkih luka”
Authors: B. Kesić, A. Jugović
Published in 2006
Citations: 76

“Factors Influencing the Formation of Freight Rates on Maritime Shipping Markets”
Authors: A. Jugović, N. Komadina, A. Perić Hadžić
Published in Pomorstvo, Vol. 29(1), 2015
Citations: 74

“Sustainable Development Model for Nautical Tourism Ports”
Authors: A. Jugović, M. Kovačić, A. Hadžić
Published in Tourism and Hospitality Management, Vol. 17(2), 2011
Citations: 72

“A Study on Cyber Security Threats in a Shipboard Integrated Navigational System”
Authors: B. Svilicic, I. Rudan, A. Jugović, D. Zec
Published in Journal of Marine Science and Engineering, Vol. 7(10), 2019
Citations: 66

Conclusion

Prof. Dr. Alen Jugović stands out as a leading academic figure in maritime economics and port systems management. His combined expertise in research, education, and institutional leadership has helped shape national policies, modernize educational infrastructure, and contribute meaningfully to international collaborations. With a rare blend of theoretical insight, practical knowledge, and innovation leadership, he is a deserving candidate for any prestigious academic or research excellence award in the fields of transport economics, maritime studies, and sustainable infrastructure development.

Mr Farshad Sadeghpour | Engineering | Best Researcher Award |

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Mr.Farshad Sadeghpour | Engineering | Best Researcher Award | 

Researcher, at Petroleum University of Technology (PUT) ,Iran

Mr. Farshad Sadeghpour is a highly motivated and multidisciplinary Petroleum Engineer and Data Scientist with a strong academic foundation and hands-on industry experience. He specializes in applying artificial intelligence, machine learning, and geomechanical modeling to tackle real-world challenges in petroleum exploration and reservoir engineering. With an impressive track record of publications, research collaborations, and award-winning projects, Mr. Sadeghpour stands out as an innovative young professional pushing the frontiers of modern petroleum engineering.

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Education 🎓

Mr. Sadeghpour holds a Master of Science in Petroleum Engineering (Exploration) from the Petroleum University of Technology, Abadan, Iran (2019–2022), where he graduated with a stellar GPA of 18.82/20. He earned his Bachelor of Science in Petroleum Engineering (Exploration) from the Islamic Azad University, Science and Research Branch, Tehran, Iran (2015–2019), achieving an exceptional GPA of 19.14/20. His academic journey reflects deep technical knowledge, diligence, and consistent excellence.

Experience 👩‍

Mr. Sadeghpour has worked across leading organizations in Iran’s energy sector. His roles include Petroleum Engineer, Petrophysicist, and Data Scientist at institutions such as the Research Institute of Petroleum Industry (RIPI), Petro Vision Pasargad (PVP), Computer Aided Process Engineering (CAPE), and the National Iranian South Oil Company (NISOC). His work involved RCAL, SCAL, EOR laboratory operations, geomechanical modeling, machine learning implementation, and reservoir data analysis—reflecting a strong blend of fieldwork, laboratory experience, and data-driven insights.

Research Interests 🔬

Farshad’s research is at the intersection of petroleum engineering, data science, and geomechanics. He focuses on using machine learning, deep learning, and AI-based models to solve complex reservoir problems such as mud loss prediction, permeability estimation, and COâ‚‚ storage assessment. His work emphasizes both theoretical modeling and practical industry applications, often conducted in collaboration with organizations such as NISOC, RIPI, and the National Iranian Oil Company. His master’s thesis and several projects revolve around neural networks, genetic algorithms, and petrophysical characterization, showing his innovative edge.

Awards 🏆

Farshad’s dedication to excellence was recognized internationally when he secured Third Prize in the EAGE Laurie Dake Challenge 2022 held in Madrid, Spain—a highly competitive event for petroleum engineering students worldwide. He has also been involved in significant national-level research projects with reputed institutions, showcasing his contributions to both academic and industrial progress.

Top Noted Publications đź“š

Farshad has co-authored and led multiple high-impact publications in Q1 journals, including:

Energy (2025): Machine learning for COâ‚‚ storage feasibility.

Journal of Petroleum Exploration and Production Technology (2025): New petrophysical-mathematical approach for RQI and FZI.

Geoenergy Science and Engineering (2024): Upscaling methods for elastic modulus prediction.

Journal of Rock Mechanics and Geotechnical Engineering (2024): Stress effects on fracture development in the Asmari reservoir.

Multiple papers under review in Marine and Petroleum Geology, International Journal of Coal Geology, and others.

Conclusion

In conclusion, Farshad Sadeghpour exemplifies the profile of a next-generation energy researcher—technically brilliant, research-oriented, and industry-relevant. His interdisciplinary expertise, publication record, award-winning work, and innovative mindset make him an outstanding candidate for prestigious recognitions such as the Best Researcher Award. His contributions are not only academically significant but also strategically aligned with the global shift toward smart and sustainable energy solutions.

Assist. Prof. Dr Saeid Afshari | Artificial intelligence | Best Researcher Award |

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Assist. Prof. Dr Saeid Afshari | Artificial intelligence | Best Researcher Award | 

Faculty , at University of Isfahan , Iran.

Dr. Saeid Afshari is an Assistant Professor of Computer Engineering at the University of Isfahan, Iran. With extensive expertise in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Human-Computer Interaction (HCI), he has significantly contributed to both academia and industry. His research spans AI-driven solutions for healthcare, security, organizational management, and multimedia applications. An experienced IT project manager, he has successfully led the development of various smart software systems, AI-based analytics platforms, and e-Government services. Dr. Afshari is also a dedicated educator, mentoring numerous students in AI, data science, and software engineering.

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Education 🎓

Ph.D. in Computer Engineering – Artificial Intelligence (2015) | University of Isfahan, Iran

Thesis: Online Quality of Experience Assessment System for Interactive Applications in Computer Networks

M.Sc. in Computer Engineering – Artificial Intelligence (2005) | University of Isfahan, Iran

Thesis: Using Agents in Workflow Management System of Distributed Organizations

B.Sc. in Hardware Engineering (2000) | University of Isfahan, Iran

Professional Experience đź’Ľ

Dr. Afshari has extensive experience in managing and developing AI-powered IT projects. He has led multiple organizational management software, AI-based sports and athlete management platforms, and e-Government solutions. As an educator, he has taught a range of subjects, including AI, data science, statistical pattern recognition, and programming in Python, C++, and MATLAB. His supervision of numerous master’s theses on AI applications highlights his mentorship and contribution to academic research.

Research Interests 🌍

Dr. Afshari’s research explores AI-driven problem-solving, deep learning applications, and human-computer interaction. His work includes machine learning-based image processing for medical diagnostics (cancer detection, skin tissue analysis), AI-powered workflow automation, smart organizational management, and multimedia quality assessment. He has also worked on neural network-based human activity recognition, deep learning models for license plate recognition, and machine translation systems. His interdisciplinary approach integrates AI with healthcare, business management, and security applications.

Awards & Honors 🏆

Recognized for significant contributions in AI-driven software development and project leadership

Successfully implemented over 150 AI-based software applications in academia and industry

Leading researcher in QoE (Quality of Experience) assessment for interactive applications

Top Noted Publications đź“š

Load Balancing of Servers in Software-Defined Internet of Multimedia Things Using the Long Short-Term Memory Prediction Algorithm

Authors: S. Imanpour, A. Montazerolghaem, S. Afshari

Conference: 10th International Conference on Web Research (ICWR)

Citations: 7

Year: 2024

QoE Assessment of Interactive Applications in Computer Networks

Authors: S. Afshari, N. Movahhedinia

Journal: Multimedia Tools and Applications, Volume 75, Pages 903-918

Citations: 7

Year: 2016

Non-Intrusive Online Quality of Experience Assessment for Voice Communications

Authors: S. Afshari, N. Movahhedinia

Journal: Wireless Personal Communications, Volume 79, Pages 2155-2170

Citations: 3

Year: 2014

Optimizing Server Load Distribution in Multimedia IoT Environments Through LSTM-Based Predictive Algorithms

Authors: A. R. Montazerolghaem, S. Imanpour, S. Afshari

Journal: International Journal of Web Research

Year: 2025

Projecting Road Traffic Fatalities in Australia: Insights for Targeted Safety Interventions

Authors: A. Soltani, S. Afshari, M. A. Amiri

Journal: Injury

Year: 2025

Conclusion

Dr. Saeid Afshari is a strong candidate for the Best Researcher Award, particularly for his contributions to AI, machine learning, and deep learning applications. To further enhance his competitiveness, he could focus on increasing high-impact publications, international collaborations, patents, and research funding. If the award prioritizes real-world AI applications, software development, and mentorship, he would be an excellent nominee.

Dr Han Gao | Artificial Intellegence | Best Researcher Award |

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Dr. Han Gao | Artificial Intellegence | Best Researcher Award

postdoctoral fellow, at Harvard University, United States.

Dr. Han Gao is a dedicated researcher specializing in scientific deep learning, computational mechanics, and generative models for spatiotemporal physics. With a strong background in machine learning-driven physics simulations, he has contributed significantly to advancing numerical modeling and data-driven solutions for complex physical systems. His work bridges the gap between deep learning and traditional computational fluid dynamics, with applications in turbulence modeling, inverse problems, and reduced-order modeling.

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Education 🎓

Dr. Gao earned his Ph.D. in Aerospace and Mechanical Engineering from the University of Notre Dame (2018–2023), where he focused on scientific deep learning for forward and inverse modeling of spatiotemporal physics. He also holds a Master’s degree in Mechanical Engineering & Materials Science from Washington University in St. Louis (2016–2018), with research on numerical simulations of jet impingement and rotor blade effects. His academic journey began with a Bachelor’s degree in Mechanical Engineering from Shanghai University of Electric Power (2012–2016).

Professional Experience đź’Ľ

Dr. Gao is currently a Postdoctoral & Teaching Fellow at Harvard University (2023–present), where he continues his research in deep learning-driven physics simulations while mentoring students. Previously, he served as a Research & Teaching Assistant at the University of Notre Dame (2018–2023) and Washington University in St. Louis (2016–2018). Additionally, he gained industry experience as a Research Intern at Google Research (2022), where he worked on advanced AI-driven physics simulations.

Research Interests 🌍

Dr. Gao’s research revolves around the integration of deep learning techniques with physics-based modeling, particularly in solving partial differential equations (PDEs), turbulence modeling, generative models, and reduced-order modeling. He has developed novel physics-informed neural networks (PINNs), Bayesian generative models, and machine-learning frameworks for high-dimensional complex systems. His work is widely applicable in computational fluid dynamics (CFD), climate modeling, aerodynamics, and engineering simulations.

Awards & Honors 🏆

Dr. Gao has been recognized for his outstanding contributions to computational mechanics and machine learning applications in physics. His publications in top-tier journals, including Nature Communications, and prestigious machine learning conferences such as NeurIPS, ICML, and ICLR, reflect his impact in the field. He has also received competitive research opportunities, including a Google Research internship, showcasing his industry relevance.

Top Noted Publications đź“š

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
L. Sun, H. Gao, S. Pan, J.-X. Wang – 916 citations – Computer Methods in Applied Mechanics and Engineering, 2020

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
H. Gao, L. Sun, J.-X. Wang – 567 citations – Journal of Computational Physics, 2021

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
H. Gao, M. J. Zahr, J.-X. Wang – 240 citations – Computer Methods in Applied Mechanics and Engineering, 2022

Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
H. Gao, L. Sun, J.-X. Wang – 214 citations – Physics of Fluids, 2021

Predicting physics in mesh-reduced space with temporal attention
X. Han, H. Gao, T. Pfaff, J.-X. Wang, L.-P. Liu – 106 citations – ICLR, 2022

Conclusion

Dr. Han Gao is a highly promising researcher with a strong publication record, interdisciplinary expertise, and experience at prestigious institutions. His contributions to scientific deep learning and computational mechanics make him a strong contender for the Best Researcher Award. To further solidify his case, he could focus on gaining more individual recognitions, expanding his leadership roles, and demonstrating the real-world impact of his research.