Tatiana Solovey | Deep Learning | Best Researcher Award

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Prof. Tatiana Solovey | Deep Learning | Best Researcher Award

Polish Geological Institute | Poland

Dr. Tatiana Solovey is a Polish hydrogeologist and Associate Professor at the Polish Geological Institute – National Research Institute. With over two decades of academic and research experience, she has specialized in groundwater hydrology, environmental geology, and sustainable water resource management. She began her career as an Assistant Lecturer and later Assistant Professor at Chernivtsi National University, Ukraine, before moving to Poland, where she advanced from Senior Researcher to Head of the Department of Hydrogeology. Her international collaborations span internships and research stays in Latvia, Estonia, Norway, Ukraine, and the United States. A dedicated educator and mentor, she has taught hydrogeology, environmental monitoring, and water resource assessment, while also supervising young researchers in European-funded projects. Dr. Solovey is widely recognized for her contributions to transboundary groundwater management and the use of satellite data for hydrological monitoring. She also serves as editor for several leading geoscience journals.

Professional Profile

Scopus

Education

Tatiana Solovey holds advanced degrees in geography and Earth sciences with a specialization in hydrology. She earned her M.Sc. in Geography with a focus on Hydrology from Chernivtsi National University, Ukraine, followed by a Ph.D. in Earth and Environmental Sciences from the same university. Building on this foundation, she achieved her habilitation in Earth and Environmental Sciences (Hydrology) at Taras Shevchenko National University of Kyiv, a credential later nostrified at Nicolaus Copernicus University in Toruń, Poland. Throughout her academic journey, she enriched her expertise through international research internships, including at Taras Shevchenko National University of Kyiv, the University of Latvia, the Estonian Geological Survey, the Geological Survey of Norway, and the San Diego Supercomputer Center. These academic and research experiences shaped her as a leading expert in groundwater sustainability, transboundary aquifers, and hydro-environmental monitoring.

Experience

Dr. Solovey’s professional career reflects a steady progression in academia and research. She began as an Assistant Lecturer and later Assistant Professor at Chernivtsi National University, She then joined the Institute of Technology and Life Sciences, Falenty, Poland, where she served as Senior Researcher she has been affiliated with the Polish Geological Institute – National Research Institute, where her roles have included Senior Researcher, Assistant Professor, and Associate Professor. She has also held leadership positions, such as Head of the Department of Hydrogeology and Deputy Head of the Department of Hydrogeology and Environmental Geology. In addition to her administrative and teaching responsibilities, she has actively contributed to European and international research collaborations and delivered invited lectures across Poland, Ukraine, and international scientific forums. She continues to mentor young researchers, lead hydrology-focused projects, and strengthen international cooperation in water resource sustainability.

Awards and Honors

Dr. Solovey’s research focuses on hydrogeology, groundwater resources, and transboundary water systems. She investigates the hydrological and hydrochemical regimes of wetlands, groundwater exchange processes in transboundary aquifers, and the effects of climate change on water resources. A significant aspect of her work is the integration of remote sensing and GRACE satellite data to monitor groundwater level fluctuations and storage changes. Her studies aim to improve sustainable groundwater management, particularly in cross-border basins such as the Bug River Basin shared by Poland, Ukraine, and Belarus. She also explores hydrogeological models for aquifers, groundwater pollution hazards, and climate-induced water resource variability. By combining field hydrology, geospatial monitoring, and environmental modeling, her work bridges science and policy, offering solutions for water security and environmental resilience. Her research has a strong applied dimension, supporting sustainable development and international cooperation in managing shared water resources across borders.

Research Focus

Dr. Solovey’s distinguished career is marked by academic recognition, memberships, and leadership roles in prominent scientific organizations. She has been an Expert of the Integrated Monitoring of the Natural Environment Commission at the Polish Ministry of the Environment and a member of the Geological Committee of the Polish Academy of Sciences. She is also an active member of several professional associations, including the Ukrainian Geographical Society, the Polish Geological Society, EuroGeoSurveys Working Group on Geohazards, and the International Association of Hydrogeologists. Her expertise has been acknowledged internationally through invited lectures and conference presentations at UNESCO ISARM, the International Association of Hydrogeologists Congress, and the EGU General Assembly. Beyond scientific recognition, she holds key editorial roles as Editor of Geological Quarterly and Przegląd Geologiczny, Deputy Editor-in-Chief of Meteorology, Hydrology, Environmental Monitoring, and Editor of Geology and Geochemistry of Combustible Minerals, reflecting her outstanding contribution to Earth sciences.

Publication Top Notes 

Groundwater pollution risks assessment in Ukraine-Poland transboundary aquifers
Year: 2024

Assessment of the Effectiveness of GRACE Observations in Monitoring Groundwater Storage in Poland
Year: 2025

Conclusion

Tatiana Solovey’s impressive research experience, leadership roles, and editorial contributions make her a strong candidate for the Best Researcher Award. With further development of interdisciplinary research, global impact, and research translation, Solovey could solidify her position as a leading researcher in hydrogeology and environmental geology.

Dr. Zhiwei Zuo | Machine Learning | Best Researcher Award Lecturer

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Dr. Zhiwei Zuo | Machine Learning | Best Researcher Award

Lecturer | Hunan University | China

Dr. Zhiwei Zuo is a researcher specializing in machine learning, artificial intelligence, and machine unlearning. He earned his Ph.D. in Computer Science from Hunan University, China, under the supervision of Prof. Zhuo Tang, where his research explored machine unlearning, adversarial robustness, and efficient deep learning methods. He also gained international research experience as a visiting student at Nanyang Technological University, Singapore, under the mentorship of Prof. Anwitaman Datta, further expanding his expertise in trustworthy AI. Dr. Zuo is currently a lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University, where he continues to focus on designing algorithms that address data privacy, security, and robustness challenges in artificial intelligence systems. He has published in prestigious journals and conferences such as IEEE Transactions on Knowledge and Data Engineering, ICASSP, and Information Sciences. His work contributes to advancing trustworthy AI while ensuring ethical and responsible deployment of machine learning technologies.

Professional Profile

Scopus

Education

Dr. Zhiwei Zuo pursued his academic journey across several prestigious institutions. He completed his Ph.D. in Computer Science at Hunan University focusing on machine learning, adversarial robustness, and machine unlearning, under the supervision of Prof. Zhuo Tang. During his doctoral studies, he broadened his international exposure as a visiting student at Nanyang Technological University, Singapore where he collaborated with Prof. Anwitaman Datta at the School of Computer Science and Engineering, working on machine unlearning algorithms and data privacy in AI systems. Prior to his doctoral research, he earned his Bachelor’s degree in Computer Science from Central China Normal University  which laid the foundation for his interest in artificial intelligence and secure computing. Building on these academic milestones, he now serves as a Lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University where he integrates his strong educational background with active research and teaching.

Experience

Dr. Zuo’s professional and research experience spans academia and international collaboration in computer science. Currently, he is a Lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University, where he engages in teaching and research on artificial intelligence and its applications in education and security. His doctoral research at Hunan University provided him with extensive experience in algorithm development, adversarial machine learning, and machine unlearning frameworks. As a visiting student at Nanyang Technological University, Singapore, he collaborated with Prof. Anwitaman Datta on advancing fine-grained approaches to machine unlearning, combining theoretical insights with practical applications. Dr. Zuo has also contributed to multiple interdisciplinary projects, focusing on robust classifiers, text adversarial attacks, and efficient algorithms for high-performance computing. His teaching and mentorship roles further reflect his dedication to cultivating the next generation of AI researchers. His career demonstrates a blend of innovative research, teaching excellence, and international collaboration.

Research Focus

Dr. Zuo’s research focuses on machine unlearning, privacy-preserving artificial intelligence, adversarial robustness, and trustworthy machine learning systems. His work seeks to address one of the emerging challenges in AI—how to efficiently remove specific data or knowledge from trained models without retraining them entirely. He has developed fine-grained parameter perturbation methods and incremental learning frameworks to advance machine unlearning. His research also explores adversarial robustness, designing models that can withstand adversarial text and image attacks, and developing generative classifiers resistant to transfer attacks. Additionally, he has contributed to efficient high-performance algorithms for Bayesian text classification in distributed environments. His interdisciplinary approach combines theory, algorithm design, and practical implementation to ensure machine learning models remain reliable, secure, and ethically aligned. Currently, his research bridges AI and education, focusing on the safe deployment of machine learning systems in sensitive domains, while addressing privacy, fairness, and accountability in artificial intelligence.

Awards and Honors

Dr. Zuo has received recognition for his academic excellence, innovative research, and contributions to the field of artificial intelligence. His publications in top-tier venues such as IEEE Transactions on Knowledge and Data Engineering, ICASSP, and Information Sciences have been well received in the research community. As a doctoral student, he earned research scholarships and support for his outstanding performance and contributions at Hunan University. His visiting research tenure at Nanyang Technological University was also supported by competitive funding, reflecting the significance of his work in machine unlearning. Additionally, his contributions to adversarial robustness and parallel algorithms have been acknowledged through conference presentations and collaborative projects. Dr. Zuo has participated in international conferences, where his work received positive recognition for originality and practical relevance. His career highlights include balancing strong theoretical research with applied solutions in secure AI systems, establishing him as a promising researcher in trustworthy and privacy-preserving AI.

Publication Top Notes 

A distributed skewed stream processing system based on scoring high-frequency key perception

Year: 2025

Conclusion

Zhiwei Zuo’s impressive research experience, innovative research, and interdisciplinary collaboration make them a strong candidate for the Best Researcher Award. With further development of their publication record, global impact, and research translation, Zuo could solidify their position as a leading researcher in machine learning.

Christian Caamaño Carrillo | Deep Learning | Best Researcher Award

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Dr. Christian Caamaño Carrillo | Deep Learning | Best Researcher Award

Docente Depto | Universidad del Bío-Bío | Chile

Dr. Christian Caamaño Carrillo is a Chilean statistician specializing in spatial statistics, semiparametric models, time series, and distribution theory. Currently serving as an Assistant Professor at the Department of Statistics, Universidad del Bío-Bío, Dr. Christian Caamaño Carrillo has built an extensive academic career combining advanced statistical theory with practical applications in environmental and economic data modeling. They hold a Ph.D. in Statistics from the Universidad de Valparaíso, where their research focused on modeling and estimating non-Gaussian random fields. With a strong background in both teaching and research,Dr. Christian Caamaño Carrillo has contributed to the training of future statisticians at undergraduate and graduate levels, delivering courses in geostatistics, linear models, and predictive modeling. Their work has been published in international journals, reflecting an ongoing commitment to methodological innovation and interdisciplinary collaboration. Dr. Christian Caamaño Carrillo continues to advance statistical methods for real-world data, particularly in environmental and spatial applications.

Professional Profile

Orcid

Scholar

Education

Dr. Christian Caamaño Carrillo earned their Ph.D. in Statistics from the Institute of Statistics, Universidad de Valparaíso, Chile, defending their thesis on the “Modeling and estimation of some non-Gaussian random fields” in May under the supervision of Dr. Moreno Bevilacqua and Dr. Carlo Gaetan. They completed an M.Sc. in Mathematics with a specialization in Statistics at the Universidad del Bío-Bío, with a thesis on estimating the Chilean Quarterly GDP Series, advised by Dr. Sergio Contreras. Prior to this, they qualified as a Statistical Engineer at the same institution in, with a thesis on panel data analysis applied to corporate strategies. Their academic journey began with a Bachelor’s degree in Statistics from Universidad del Bío-Bío. This robust educational background has provided them with expertise in statistical modeling, time series analysis, and spatial statistics, forming the foundation, research, and consulting activities.

Experience

Dr. Christian Caamaño Carrillo has been an Assistant Professor at the Department of Statistics, Universidad del Bío-Bío since August, where they teach and supervise both undergraduate and graduate students. From, they served as a Part-time Lecturer in the same department, delivering a wide range of courses in probability, statistical inference, and geostatistics. In parallel, they worked as a Part-time Lecturer at the Department of Mathematics and Applied Physics, Universidad Católica de la Santísima Concepción, focusing on foundational courses in statistics and probability. Their teaching portfolio spans undergraduate courses such as Linear Models, Random Variables, and Statistical Computing, as well as graduate-level instruction in Geostatistical Methods, Semiparametric Models, and Predictive Modeling. They have also contributed to specialized programs at Universidad Adolfo Ibáñez and Universidad de Valparaíso. Alongside their teaching, Dr. Christian Caamaño Carrillo maintains an active research agenda in spatial statistics and environmental data analysis.

Research Focus

Dr. Christian Caamaño Carrillo focuses on developing and applying advanced statistical methods to solve complex real-world problems. Their main research areas include spatial statistics, where they work on modeling spatial and spatio-temporal processes; semiparametric models, which offer flexible approaches for data with both structured and unstructured components; time series analysis, particularly in economic and environmental contexts; and distribution theory, addressing the properties and applications of probability distributions beyond standard Gaussian assumptions. A notable part of their work involves modeling environmental and geostatistical data using robust techniques that handle skewness and heavy-tailed behavior, such as skew-t processes. They are also engaged in methodological innovations for composite likelihood estimation and nearest-neighbor approaches in large spatial datasets. Through interdisciplinary collaborations, Dr. Christian Caamaño Carrillo applies these methods to areas such as environmental monitoring, mineral deposit modeling, and economic indicator estimation, bridging theory and practice in statistical science.

Awards and Honors

Dr. Christian Caamaño Carrillo has earned recognition in the academic community through sustained contributions to spatial statistics and applied statistical modeling. Their doctoral research on non-Gaussian random fields has been cited as a significant methodological advancement in environmental and geostatistical applications. As a faculty member, they have played a key role in developing and teaching specialized statistical courses, shaping the next generation of statisticians in Chile. They have been invited to collaborate with national and international researchers, leading to peer-reviewed publications in respected journals such as Environmetrics. Through graduate thesis supervision and involvement in interdisciplinary projects, Dr. Christian Caamaño Carrillo has contributed to advancing statistical applications in environmental sciences, mining, and economics. While formal awards were not listed, their academic trajectory demonstrates consistent professional excellence and recognition through publications, collaborations, and contributions to statistical education and methodology.

Publication Top Notes

Conclusion

Caamaño-Carrillo is a qualified and accomplished researcher, with a strong academic background, research experience, and teaching expertise. Their research areas are relevant and important in the field of statistics, and their publication record demonstrates their potential for making significant contributions to their field. With continued research and publication efforts, C. Caamaño-Carrillo has the potential to make a meaningful impact in their field and is a strong candidate for the Best Researcher Award.

Prof. Dr. Jasenka Gajdoš Kljusurić | Data Science and Deep Learning | Best Researcher Award

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Prof. Dr. Jasenka Gajdoš Kljusurić | Data Science and Deep Learning | Best Researcher Award

Prof, Faculty of Food Technology and Biotechnology at University of Zagreb, Croatia

Sylvain S. Guillou is a Full Professor of Fluid Mechanics at the University of Caen Normandy, France. He is the Director of the Applied Science Laboratory LUSAC and has over 176 publications, 38,900 reads, and 1,692 citations. His research focuses on computational physics, fluid dynamics, and geophysics, particularly in tidal turbines and marine renewable energies ¹.

Profile

orcid

🎓 Education

– *HDR – Fluid Mechanics*, University of Caen (2004-2005)- Ph.D. in Applied Mathematics – Mechanics, University of Paris Pierre & Marie Curie (1993-1996)- (link unavailable) in Dynamics of Fluids – Numerical Modeling, Ecole Centrale de Nantes (1992-1993)

👨‍🔬 Experience

– *Full Professor*, University of Caen Normandy (2017-present)- *Associate Professor*, University of Caen Normandy (2005-2017)- *Assistant Professor*, University of Caen Normandy (1999-2005)- *Post-doctoral Researcher*, University of Caen (1996-1997)

🔍 Research Interest

– *Computational Physics*: Numerical simulations of complex fluid flows- *Fluid Dynamics*: Turbulence, sediment transport, and environmental fluid mechanics- *Geophysics*: Marine renewable energies, tidal turbines, and offshore wind energies

Awards and Honors 🏆

Although specific awards and honors are not detailed, Guillou’s editorial roles and conference organization demonstrate his recognition in the field ¹ ²: – *Associate Editor*, Energies, La Houille Blanche, and International Journal for Sediment Research- *Organizer*, International Conference on Estuaries and Coasts (ICEC-2018) and other conferences

📚 Publications 

– Numerical modeling of the effect of tidal stream turbines on the hydrodynamics and the sediment transport–Application to the Alderney Race (Raz Blanchard), France 🌊
– Modelling turbulence with an Actuator Disk representing a tidal turbine 🌟
– A two-phase numerical model for suspended-sediment transport in estuaries 🌴
– Wake field study of tidal turbines under realistic flow conditions 💨
– Tidal farm analysis using an analytical model for the flow velocity prediction in the wake of a tidal turbine with small diameter to depth ratio 🌊

Conclusion

Sylvain S. Guillou’s impressive research record, leadership roles, and editorial activities make him an excellent candidate for the Best Researcher Award. His contributions to computational physics, fluid dynamics, and geophysics have significantly advanced our understanding of these fields. With some potential for interdisciplinary collaborations and exploring emerging topics, Guillou is well-suited to receive this award ¹ ².

Prof. JinAn XU | Deep Learning | Best Researcher Award

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Prof. JinAn XU | Deep Learning | Best Researcher Award

The Head of Research Institute of Large Scale Data and NLP, Beijing Jiaotong University, China

Prof. JinAn Xu is a renowned researcher in the field of Natural Language Processing (NLP), Machine Translation (MT), and Large Language Models (LLMs). With a strong background in computer science, Prof. Xu has published numerous papers in top-tier conferences and journals. Currently, Prof. Xu is working at Beijing Jiaotong University as a professor.

Profile

scholar

🎓 Education

Ph.D. from Hokkaido University, Japan (2001-2006) 📚 Undergraduate degree from North Jiaotong University (1988-1992)

👨‍🔬 Experience

– Professor, Beijing Jiaotong University (2018-present) 👨‍🏫– Associate Professor, Beijing Jiaotong University (2009-2018) 📚– Researcher, NEC Research Center, NLP LAB (2006-2009) 🔬– Engineer, The Fourth Survey and Design Institute of the Ministry of Railway (1992-1999

🔍 Research Interest

– Natural Language Processing (NLP) 🤖– Machine Translation (MT) 🌎– Large Language Models (LLMs) 📈– Knowledge Graphs (KG)

🏆Awards and Honors

– CCF Outstanding Member 🌟

📚 Publications

1. “A Variational Hierarchical Model for Neural Cross-Lingual Summarization” 📄
2. “Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation” 🤖
3. “MSCTD: A Multimodal Sentiment Chat Translation Dataset” 💬
4. “Scheduled Multitask Learning for Neural Chat Translation” 📱
5. “Saliency as Evidence: Event Detection with Trigger Saliency Attribution” 🔍

Conclusion

Prof. JinAn Xu is a highly accomplished researcher with a strong publication record, research impact, and diverse research interests. Their leadership and experience make them an excellent candidate for the Best Researcher Award. With some potential areas for improvement, Prof. Xu’s achievements and contributions make them a strong contender for this award.

Dr. Giuseppe D’Albis | Intelligenza Artificiale | Excellence in Research Award

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Dr. Giuseppe D’Albis | Intelligenza Artificiale | Excellence in Research Award

Resident in Oral Surgery, University of Bari Aldo Moro, Italy

Giuseppe D’Albis is a dedicated dental professional with a strong academic background. Born on October 27, 1991, he has pursued various specializations in dentistry, including oral surgery, prosthodontics, and implantology. Currently, he is a resident in Oral Surgery at Bari Aldo Moro University. Giuseppe has participated in numerous scientific courses and conferences, showcasing his commitment to continuous learning and professional development.

Professional Profile

ORCID

🎓 Education

Giuseppe D’Albis has an impressive educational background in dentistry. He earned his Degree in Dentistry and Dental Prosthetics from the European University of Madrid. He then pursued multiple second-level master’s degrees in specialized fields, including Prosthodontics and New Technologies, Osseointegrated Implantology, Integrated Clinical Approach in Periodontology and Implantology, and Oral and Emergency Dental Surgery. His academic pursuits demonstrate his dedication to advancing his knowledge and skills in dentistry.

👩‍🏫 Experience

As a resident in Oral Surgery, Giuseppe D’Albis has gained valuable clinical experience in diagnosing and treating various oral health issues. He has participated in numerous training courses and conferences, staying up-to-date with the latest techniques and advancements in dentistry. His experience in different areas of dentistry, including prosthodontics, implantology, and oral surgery, makes him a well-rounded professional.

🏆 Awards and Honors

Although specific awards and honors are not mentioned in the provided CV, Giuseppe D’Albis’s participation in various scientific courses and conferences demonstrates his commitment to excellence in dentistry. His involvement in continuous learning and professional development showcases his dedication to providing high-quality patient care.

🔬 Research Interests

Giuseppe D’Albis’s research focus appears to be in the areas of oral surgery, prosthodontics, and implantology. His master’s thesis on “Intraoral Transmission of Bacteria and Its Relationship to Periimplantitis” suggests an interest in investigating the causes and consequences of periimplantitis. His participation in conferences and training courses related to these topics further highlights his research focus.

📚Top Noted Publications

1. Utilization of Platelet-Rich Plasma in Oral Surgery: A Systematic Review of the Literature 📚
2. Adjunctive Effects of Diode Laser in Surgical Periodontal Therapy: A Narrative Review of the Literature 💡
3. Odontogenic Myxoma Associated to Unerupted Mandibular Molar in a Pediatric Patient: A New Case Description with Comprehensive Literature Analysis 👦
4. Diagnostic Challenges of Traumatic Ulcerative Granuloma with Stromal Eosinophilia in the Hard Palate 🔍
5. Immediate Loading Implants in Fixed Partial Dentures 💯
6. The Role and Applications of Artificial Intelligence in Dental Implant Planning: A Systematic Review 🤖
7. Periodontal Health and Its Relationship with Psychological Stress: A Cross-Sectional Study 🤯
8. Single-implant-supported zirconia fixed partial denture with a mesial cantilever extension: a case report 💼
9. Augmented Reality-Assisted Surgical Exposure of an Impacted Tooth: A Pilot Study 🔥
10. Implant-supported zirconia fixed partial dentures cantilevered in the lateral-posterior area: A 4-year clinical results 📊
11. Use of hyaluronic acid for regeneration of maxillofacial bones 💊
12. SINGLE IMPLANT-SUPPORTED TWO-UNIT IN THE POSTERIOR AREA: CASE REPORT AND LITERATURE REVIEW 📄
13. Orientation of digital casts according to the face-bow arbitrary plan 🎨
14. Tunnel access for ridge augmentation: A review 📖
15. The Role and Applications of Artificial Intelligence in Dental Implant Planning (Working paper) 🤖

Conclusion

Giuseppe D’Albis demonstrates potential as a researcher in the field of dentistry, with a strong educational background and clinical experience. While there are areas for improvement, his participation in scientific courses and conferences showcases his commitment to continuous learning. With focused efforts on publishing research and exploring interdisciplinary collaborations, he could become a strong candidate for the Best Researcher Award.

Dr. Seyed Abolfazl Aghili | machine learning and deep learning | Best Review Paper Award

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Dr. Seyed Abolfazl Aghili | machine learning and deep learning | Best Review Paper Award

lecturer, Siran university of science and technology, Iran

Seyed Abolfazl Aghili is a civil engineer and researcher with expertise in construction engineering and management. He holds a Ph.D. in Civil Engineering from Iran University of Science and Technology (IUST). His research focuses on machine learning, resiliency, and building information modeling (BIM). Dr. Aghili has published several papers in reputable journals and has presented his work at international conferences. He is fluent in Persian and English and has skills in various software, including Python, MS Project, and Autodesk AutoCAD.

Profile

orcid

Education 🎓

Ph.D. in Civil Engineering, Construction Engineering and Management, Iran University of Science and Technology (IUST), 2019-2024 (link unavailable) in Civil Engineering, Construction Engineering and Management, Iran University of Science and Technology (IUST), 2013-2015 (link unavailable) in Civil Engineering, Isfahan University of Technology (IUT), 2009-2013

Experience 💼 

Researcher, Iran University of Science and Technology (IUST), 2019-2024  Graduate Research Assistant, Iran University of Science and Technology (IUST), 2013-2015  Undergraduate Research Assistant, Isfahan University of Technology (IUT), 2009-2013

Awards and Honors🏆

Ranked 5th among 2200 participants in Nationwide University Entrance Exam for Ph.D. program in Iran, 2019 Ranked 2nd among all construction management students in Iran University Science and Technology, 2013-2015 Ranked 220th among 32,663 participants (Top 1%) in Nationwide University Entrance Exam for (link unavailable) program in Iran, 2013

Research Focus

Machine learning and deep learning methods  Resiliency  Building Information Modeling (BIM)  Human Resource Management (HRM)  Decision Making Systems for Project Managers

Publications 📚

1. Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review 🤖
2. Data-driven approach to fault detection for hospital HVAC system 📊
3. Feasibility Study of Using BIM in Construction Site Decision Making in Iran 🏗️
4. Review of digital imaging technology in safety management in the construction industry 📸
5. The role of insurance companies in managing the crisis after earthquake 🌪️
6. The need for a new approach to pre-crisis and post-crisis management of earthquake 🌊

Conclusion

Seyed Abolfazl Aghili is an exceptional researcher with a strong academic background, interdisciplinary research experience, and a notable publication record. His teaching and mentoring experience, as well as his technical skills, demonstrate his commitment to education and research. While there are areas for improvement, Dr. Aghili’s strengths make him a strong candidate for the Best Researcher Award.

Zicheng Xin | intelligentialization | Best Researcher Award

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Dr. Zicheng Xin | intelligentialization | Best Researcher Award

postdoctor, University of Science and Technology Beijing, China

Zicheng Xin is a renowned researcher and visiting professor at the Korea Invention Academy. He is affiliated with the University of Science and Technology Beijing (USTB) and has made significant contributions to the field of metallurgical engineering. His research focuses on metallurgical process engineering, intelligence, and simulation.

Profile

scopus

Education 🎓

Ph.D. in Metallurgical Engineering, State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing (USTB) (2018-2022)

Experience 🧪

Visiting Professor, Korea Invention Academy (current)  Researcher, State Key Laboratory of Advanced Metallurgy, USTB (current)

Awards & Honors🏆

“Multiscale modeling and collaborative manufacturing for steelmaking plants”, the 10th World Scientist Grand Award — Golden Scientist Grand Award (Second Place, International Federation of Inventors’ Associations, 2023) “Multiscale modeling and collaborative manufacturing for steelmaking plants”, the 10th World Scientist Grand Award— Science & Technology Grand

Research Focus 🔍

Metallurgical process engineering and intelligence  Simulation and optimization of metallurgical process

Publications📚

1. Analysis of multi-zone reaction mechanisms in BOF steelmaking and comprehensive simulation [J]. Materials, 2025, 18(5): 1038. – Zicheng Xin, Qing Liu, Jiangshan Zhang, et al.
2. Modeling of LF refining process: a review [J]. Journal of Iron and Steel Research International, 2024, 31(2): 289-317. – Zicheng Xin, Jiangshan Zhang, Kaixiang Peng, et al.
3. Explainable machine learning model for predicting molten steel temperature in LF refining process [J]. International Journal of Minerals, Metallurgy and Materials, 2024, 31(12): 2657-2669. – Zicheng Xin, Jiangshan Zhang, Kaixiang Peng, et al.
4. Predicting temperature of molten steel in LF refining process using IF-ZCA-DNN model [J]. Metallurgical and Materials Transactions B, 2023, 54(3): 1181-1194. – Zicheng Xin, Jiangshan Zhang, Junguo Zhang, et al.
5. Predicting the alloying element yield in a ladle furnace using principal component analysis [J]. … – Zicheng Xin, Jiangshan Zhang, Yu Jin, et al.

Conclusion

Zicheng Xin’s academic excellence, research focus, and international recognition make him a strong candidate for the Best Researcher Award. While there are areas for improvement, his strengths and achievements demonstrate his potential to make significant contributions to the field of metallurgy.

Nahid Entezarian | Machine Interaction | Best Researcher Award

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Ms. Nahid Entezarian | Machine Interaction | Best Researcher Award

Author, University of Mashhad, Mashhad, Iran

Nahid Entezarian is a Ph.D. candidate in Information Technology Management at Ferdowsi University of Mashhad. Her research interests include text mining, data mining, NeuroIS, artificial intelligence, machine learning, and research methodology in information systems.

Profile

scholar

Education 🎓

Nahid Entezarian is currently pursuing her Ph.D. in Information Technology Management at Ferdowsi University of Mashhad, specializing in Smart Business. Her academic background has provided a solid foundation for her research and professional endeavors.

Experience 🧪

Unfortunately, the provided text does not mention specific work experience or professional roles held by Nahid Entezarian.

Awards & Honors �

Unfortunately, the provided text does not mention specific awards or honors received by Nahid Entezarian.

Research Focus 🔍

1. Text Mining: Investigating the application of text mining techniques in various domains.
2. Data Mining: Exploring the use of data mining methods for knowledge discovery.
3. NeuroIS: Examining the intersection of neuroscience and information systems.
4. Artificial Intelligence: Investigating the application of AI in various domains.
5. Machine Learning: Developing and applying machine learning algorithms for data analysis.

Publications📚

1. An investigation extent and factors influencing the users’ perception of database interface based on Nielsen model 📊
2. GUIDELINES FOR USER INTERFACE DESIGN BASED ON USERS’BEHAVIORS, EXPECTATIONS AND PERCEPTIONS 📈
3. Topic Modeling on System Thinking Themes Using Latent Dirichlet Allocation, Non-Negative Matrix Factorization and BER Topic 🤖
4. NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis 🧠
5. The Application of Artificial Intelligence in Smart Cities: A Systematic Review with Methodi Ordinatio 🌆
6. Systems Thinking in the Circular Economy: An Integrative Literature Review ♻️
7. The impact of knowledge management and Industry 4.0 technologies in organizations: a meta-synthesis approach 📈
8. Topic Modeling Emerging Trends for Business Intelligence in Marketing: With Text Mining and Latent Dirichlet Allocation 📊
9. Topic Modeling Emerging Trends for Business Intelligence in Marketing: With Text Mining and Latent Dirichlet Allocation 📊
10. Introducing and Evaluation of Rogers’s Diffusion Innovation Theory 📈

Conclusion 🏆

Nahid Entezarian’s impressive academic and research experience, research output, interdisciplinary research approach, and collaborations make her an outstanding candidate for the Best Researcher Award. While there are areas for improvement, her strengths and achievements demonstrate her potential to make a significant impact in her field.

Aaron Brunk | Applied and Numerical Analysis | Best Researcher Award

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Dr. Aaron Brunk | Applied and Numerical Analysis | Best Researcher Award

Dr. Johannes-Gutenberg University, Germany

Dr. Aaron Brunk is a Post-Doc Research Fellow at Johannes Gutenberg-University Mainz, specializing in numerical mathematics under Prof. Dr. Maria M. Lukácová-Medvid’ová. He focuses on thermodynamically consistent fluid modeling, parabolic cross-diffusion system analysis, and structure-preserving method construction. Dr. Brunk completed his PhD with magna cum laude in 2022, studying viscoelastic phase separation. His work includes multiple DFG projects, with roles ranging from PhD student to Principal Investigator. He is an active academic contributor, organizing seminars and workshops, presenting at international conferences, and engaging in research stays and academic self-administration. His current research projects involve variational quantitative phase-field modeling and spinodal decomposition of polymer-solvent systems.

 

Professional Profiles:

Googlescholar

🎓 Education

Nov. 2017 – Feb. 2022: Ph.D. in Mathematics (Dr. rer. nat.), Johannes Gutenberg-University Mainz, GermanyDissertation: Viscoelastic phase separation: Well-posedness and numerical analysisDisputation: 11.02.2022Degree: Magna cum laudeSupervisor: Prof. Dr. Mária M. Lukáčová-Medvid’ováOct. 2015 – Nov. 2017: M.Sc. in Mathematics, Johannes Gutenberg-University Mainz, GermanyThesis: Numerische Behandlung von zeitgebrochenen DiffusionsgleichungenSupervisor: Prof. Dr. Thorsten RaaschOct. 2012 – Oct. 2015: B.Sc. in Mathematics, Johannes Gutenberg-University Mainz, GermanyThesis: Mathematische Modellierung von PhosphorylierungssystemenSupervisor: Prof. Dr. Alan Rendall

🎓 Professional Experience

Feb. 2022 – Present: Post-Doc Research Fellow, Institute of Mathematics, Johannes Gutenberg-University Mainz, GermanyGroup: Numerical MathematicsSupervisor: Prof. Dr. Mária M. Lukáčová-Medvid’ováActivities:🧪 Modelling of thermodynamically consistent complex fluids📊 Analysis of parabolic cross-diffusion systems🔧 Construction of structure-preserving methods for cross-diffusion systems👨‍🏫 Assistant in various tutorials and seminars📚 Independent lecturingNov. 2017 – Feb. 2022: Research Assistant, Institute of Mathematics, Johannes Gutenberg-University Mainz, GermanyGroup: Numerical MathematicsSupervisor: Prof. Dr. Mária M. Lukáčová-Medvid’ováActivities:🧪 Modelling and analysis of viscoelastic phase separation👨‍🏫 Assistant in various tutorials and seminars

📚 Third Party Projects

Sep. 2023 – Aug. 2026: German Research Foundation (DFG) – Principal InvestigatorProject: Variational quantitative phase-field modeling and simulation of powder bed fusion additive manufacturing within the DFG Priority Programme 2256Collaborator: B.-X. Xu, Technical University Darmstadt, Material ScienceFunded Ph.D. positionFeb. 2022 – Feb. 2026: German Research Foundation (DFG) – Postdoctoral ResearcherProject: Spinodal decomposition of polymer-solvent systems within the TRR 146 Multiscale Simulation Methods for Soft Matter SystemsPrincipal Investigators: M. Lukáčová-Medvid’ová, B. DünwegNov. 2017 – Feb. 2022: German Research Foundation (DFG) – Ph.D. studentProject: Spinodal decomposition of polymer-solvent systems within the TRR 146 Multiscale Simulation Methods for Soft Matter SystemsPrincipal Investigators: M. Lukáčová-Medvid’ová, B. Dünweg, H. Egger

✍️Publications Top Note :

Analysis of a Viscoelastic Phase Separation Model

Authors: A Brunk, B Dünweg, H Egger, O Habrich, M Lukáčová-Medvid’ová, …

Journal: Journal of Physics: Condensed Matter 33 (23), 234002, 2021

Citations: 19

Global Existence of Weak Solutions to Viscoelastic Phase Separation Part: I. Regular Case

Authors: A Brunk, M Lukáčová-Medvid’ová

Journal: Nonlinearity 35 (7), 3417, 2022

Citations: 14

Modelling Cell-Cell Collision and Adhesion with the Filament Based Lamellipodium Model

Authors: N Sfakianakis, D Peurichard, A Brunk, C Schmeiser

Journal: arXiv preprint arXiv:1809.07852, 2018

Citations: 10

Global Existence of Weak Solutions to Viscoelastic Phase Separation: Part II. Degenerate Case

Authors: A Brunk, M Lukáčová-Medvid’ová

Journal: Nonlinearity 35 (7), 3459, 2022

Citations: 9

Systematic Derivation of Hydrodynamic Equations for Viscoelastic Phase Separation

Authors: D Spiller, A Brunk, O Habrich, H Egger, M Lukáčová-Medvid’ová, …

Journal: Journal of Physics: Condensed Matter 33 (36), 364001, 2021

Citations: 9

Existence, Regularity and Weak-Strong Uniqueness for the Three-Dimensional Peterlin Viscoelastic Model

Authors: A Brunk, Y Lu, M Lukacova-Medvidova

Journal: arXiv preprint arXiv:2102.02422, 2021

Citations: 9

Chemotaxis and Haptotaxis on Cellular Level

Authors: A Brunk, N Kolbe, N Sfakianakis

Journal: Theory, Numerics and Applications of Hyperbolic Problems I: Aachen, Germany, …

Citations: 4

On Existence, Uniqueness and Stability of Solutions to Cahn–Hilliard/Allen–Cahn Systems with Cross-Kinetic Coupling

Authors: A Brunk, H Egger, TD Oyedeji, Y Yang, BX Xu

Journal: Nonlinear Analysis: Real World Applications 77, 104051, 2024

Citations: 3

Stability and Discretization Error Analysis for the Cahn–Hilliard System via Relative Energy Estimates

Authors: A Brunk, H Egger, O Habrich, M Lukáčová-Medviďová

Journal: ESAIM: Mathematical Modelling and Numerical Analysis 57 (3), 1297-1322, 2023

Citations: 3

Existence and Weak-Strong Uniqueness for Global Weak Solutions for the Viscoelastic Phase Separation Model in Three Space Dimensions

Authors: A Brunk

Journal: arXiv preprint arXiv:2208.01374, 2022

Citations: 3

Relative Energy and Weak–Strong Uniqueness of a Two‐Phase Viscoelastic Phase Separation Model

Authors: A Brunk, M Lukáčová‐Medvid’ová

Journal: ZAMM‐Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte …, 2023

Citations: 2

Viscoelastic Phase Separation: Well-Posedness and Numerical Analysis

Authors: A Brunk

Journal: Dissertation, Mainz, Johannes Gutenberg-Universität Mainz, 2022

Citations: 2

Relative Energy Estimates for the Cahn-Hilliard Equation with Concentration Dependent Mobility

Authors: A Brunk, H Egger, O Habrich, M Lukacova-Medvidova

Journal: arXiv preprint arXiv:2102.05704, 2021

Citations: 2

Stability, Convergence, and Sensitivity Analysis of the FBLM and the Corresponding FEM

Authors: N Sfakianakis, A Brunk

Journal: Bulletin of Mathematical Biology 80, 2789-2827, 2018

Citations: 2

Fundamentals of the Oldroyd-B Model Revisited: Tensorial vs. Vectorial Theory

Authors: A Brunk, J Chaudhuri, M Lukacova-Medvidova, B Duenweg

Journal: arXiv preprint arXiv:2308.01326, 2023

Citations: 1

On Uniqueness and Stable Estimation of Multiple Parameters in the Cahn–Hilliard Equation

Authors: A Brunk, H Egger, O Habrich

Journal: Inverse Problems 39 (6), 065002, 2023

Citations: 1

A Second-Order Fully-Balanced Structure-Preserving Variational Discretization Scheme for the Cahn-Hilliard Navier-Stokes System

Authors: A Brunk, H Egger, O Habrich, M Lukacova-Medvidova

Journal: arXiv preprint arXiv:2209.03849, 2022

Citations: 1

Structure-Preserving Approximation of the Cahn-Hilliard-Biot System

Authors: A Brunk, M Fritz

Journal: arXiv preprint arXiv:2407.12349, 2024

Error Analysis for a Viscoelastic Phase Separation Model

Authors: A Brunk, H Egger, O Habrich, M Lukacova-Medvidova

Journal: arXiv preprint arXiv:2407.01803, 2024

Nonisothermal Cahn-Hilliard Navier-Stokes System

Authors: A Brunk, D Schumann

Journal: arXiv preprint arXiv:2405.13936, 2024