Winston Dunn | Artificial Intelligence |  Best Researcher Award

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Dr. Winston Dunn | Artificial Intelligence |  Best Researcher Award

The University Of Kansas Medical Center | United States

Author Profiles

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Orcid ID

Early Academic Pursuits

Dr. Winston Dunn began his academic journey with a Bachelor of Science in Biochemistry from the University of British Columbia. He pursued his medical degree at Ross University School of Medicine and later completed advanced training at The Chicago Medical School. His strong foundation in internal medicine was built through residency at the Mayo Graduate School of Medicine, followed by specialized fellowships in Gastroenterology at the University of California, San Diego, and in Transplant Hepatology at Mayo Clinic. Parallel to his clinical training, he enriched his expertise with a Master’s in Public Health, highlighting his commitment to blending clinical practice with population health perspectives.

Professional Endeavors

Dr. Dunn’s academic career began as an Instructor in Medicine at the Mayo Clinic College of Medicine (2009–2010). He then joined the University of Kansas Medical Center as an Assistant Professor (2010–2016), rising to Associate Professor (2016–2025), and currently serves as Professor in the Division of Gastroenterology. His steady progression reflects consistent excellence in teaching, research, and clinical leadership. Additionally, his professional licensure across multiple states and board certifications in Internal Medicine, Gastroenterology, and Transplant Hepatology highlight his depth of expertise.

Contributions and Research Focus

Dr. Dunn’s research is primarily centered on liver diseases, with a special focus on nonalcoholic steatohepatitis (NASH), metabolic dysfunction-associated steatohepatitis (MASH), cirrhosis, and outcomes after hepatitis C treatment. He has been the principal investigator in numerous Phase II and Phase III clinical trials, collaborating with leading pharmaceutical companies and institutions. His studies explore genetic predictors of liver disease outcomes, non-invasive biomarkers, and novel therapeutics aimed at reversing fibrosis and improving survival. He also contributed to improving models of care, diagnosis, and patient referral systems in hepatology.

Impact and Influence

Dr. Dunn’s work has had significant clinical implications, advancing knowledge in liver transplantation, NASH therapeutics, and hepatology practice. His role in high-impact trials, including those supported by Gilead Sciences, Novo Nordisk, and Madrigal Pharmaceuticals, positions him as a leader in shaping the future of liver disease management. His active service on the AASLD Alcoholic Liver Disease Steering Committee and Education Subcommittee further demonstrates his influence on clinical guidelines, education, and policy within the hepatology community.

Academic Citations and Recognition

Dr. Dunn’s contributions have been recognized through multiple awards, including the Sheila Sherlock Clinical and Translational Research Award (2012), the AASLD Advanced Transplant Hepatology Fellowship Award (2009), and induction as a Fellow of the American Association for the Study of Liver Diseases (FAASLD) in 2023. His research has generated numerous publications and citations, reflecting his impact on both scientific literature and clinical practice.

Legacy and Future Contributions

With an established record of academic excellence, groundbreaking research, and mentorship, Dr. Dunn’s legacy lies in advancing hepatology through both innovative research and translational impact. His ongoing leadership in pivotal trials will likely redefine therapeutic standards for NASH and related liver conditions. As a Professor at the University of Kansas Medical Center, he is poised to continue mentoring future hepatologists while contributing to international collaborations that influence the global fight against liver disease.

Conclusion

In summary, Dr. Winston Dunn exemplifies the qualities of a distinguished academic physician-scientist. His rigorous training, progressive academic career, and pioneering research in hepatology underscore his suitability for honors such as the Best Researcher Award. With a career dedicated to bridging clinical care, research innovation, and education, Dr. Dunn stands as a role model whose work continues to shape the future of liver disease management and patient outcomes worldwide.

Notable Publications

“Artificial Intelligence for Predictive Diagnostics, Prognosis, and Decision Support in MASLD, Hepatocellular Carcinoma, and Digital Pathology" 

  • Author: Nicholas Dunn; Nipun Verma; Winston Dunn
  • Journal: Journal of Clinical and Experimental Hepatology
  • Year: 2025

"Prevalence and Predictors of Suspected Metabolic Dysfunction‐Associated Steatotic Liver Disease in Adolescents in the United States" 

  • Author: Sheila L. Noon; Lauren F. Chun; Tin Bo Nicholas Lam; Nhat Quang N. Thai; Winston Dunn; Jeffrey B. Schwimmer‏
  • Journal: Alimentary Pharmacology & Therapeutics
  • Year: 2025

"Comparison Between Dynamic Models for Predicting Response to Corticosteroids in Alcohol‐Associated Hepatitis: A Global Cohort Study" 

  • Author: Francisco Idalsoaga; Luis Antonio Díaz; Leonardo Guizzetti; Winston Dunn; Heer Mehta; Jorge Arnold; Gustavo Ayares; Rokhsana Mortuza; Gurpreet Mahli; Alvi H. Islam et al.
  • Journal: Alimentary Pharmacology & Therapeutics
  • Year: 2025

"Moderate alcohol-associated hepatitis: A real-world multicenter study" 

  • Author: Francisco Idalsoaga; Luis Antonio Díaz; Winston Dunn; Heer Mehta; Karen Muñoz; Vicente Caldentey; Jorge Arnold; Gustavo Ayares; Rokhsana Mortuza; Shiv K. Sarin et al.
  • Journal: Hepatology Communications
  • Year: 2025

"An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study" 

  • Author: Winston Dunn; Yanming Li; Ashwani K. Singal; Douglas A. Simonetto; Luis A. Díaz; Francisco Idalsoaga; Gustavo Ayares; Jorge Arnold; María Ayala-Valverde; Diego Perez et al.
  • Journal: Hepatology
  • Year: 2024

Tzu-Chien Wang | AI | Best Researcher Award

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Assist. Prof. Dr. Tzu-Chien Wang | AI | Best Researcher Award

Tzu-Chien Wang at Department of Computer Science and Information Management Soochow University, Taiwan

Dr. Tzu-Chien Wang is an Assistant Professor in the Department of Computer Science and Information Management at Soochow University. He specializes in artificial intelligence, data mining, decision support systems, and process improvement techniques. With a strong background in machine learning, natural language processing, and predictive modeling, he has contributed significantly to both academia and industry by developing proof-of-concept models for operational processes.

Professional Profile:

Orcid

Google Scholar

Education Background

Dr. Tzu-Chien Wang earned his Ph.D. in Business Administration from National Taiwan University, where he specialized in data-driven decision-making, artificial intelligence applications, and business intelligence. His doctoral research focused on leveraging machine learning, data mining, and optimization techniques to enhance decision support systems and operational efficiency. His academic training has provided him with a strong foundation in predictive modeling, natural language processing, and process improvement methodologies, which he has effectively applied in both research and industry settings.

Professional Development

Dr. Wang has a diverse professional background, spanning academia, industry, and research institutions. Before joining Soochow University in 2025, he served as an Assistant Professor at Mackay Junior College of Medicine, Nursing, and Management. He also held managerial roles in data development at VisualSoft Information System Co., Ltd. and worked as a Senior Data Analyst at Fubon Life Insurance Co., Ltd. Additionally, he contributed as an Assistant Research Fellow at the Commerce Development Research Institute, focusing on international digital commerce.

Research Focus

His research interests include artificial intelligence, data mining, decision support systems, natural language processing, optimization, clustering, classification, and predictive model building. He is particularly engaged in developing AI-driven solutions for business intelligence, healthcare applications, and digital transformation.

Author Metrics:

Dr. Wang has published extensively in AI, data analytics, and business intelligence. His research contributions can be found on Google Scholar, reflecting his impact on data science and AI applications.

Awards and Honors:

  • High-Age Health Smart Medical Care Industry-Academia Alliance, National Science and Technology Council, Taiwan (2025–2028)

  • AI+BI Agile Development Data Platform Project, Ministry of Economic Affairs, Taiwan (2022)

  • Consumer Data-Driven Precision R&D and Manufacturing (C2M) Promotion Project, Bureau of Energy, Taiwan (2021)

Publication Top Notes

1. Deep Learning-Based Prediction and Revenue Optimization for Online Platform User Journeys

  • Author: T.C. Wang
  • Journal: Quantitative Finance and Economics (2024)
  • Type: Research Article
  • Citations: 6
  • Summary: This study utilizes deep learning techniques to predict user behavior and optimize revenue generation on online platforms, improving personalized recommendations and business strategies.

2. An Integrated Data-Driven Procedure for Product Specification Recommendation Optimization with LDA-LightGBM and QFD

  • Authors: T.C. Wang, R.S. Guo, C. Chen
  • Journal: Sustainability (2023)
  • Type: Research Article
  • Citations: 5
  • Summary: This research presents a hybrid framework combining Latent Dirichlet Allocation (LDA), LightGBM, and Quality Function Deployment (QFD) to optimize product specification recommendations, improving efficiency in sustainable manufacturing.

3. Integrating Latent Dirichlet Allocation and Gradient Boosting Tree Methodology for Insurance Product Development Recommendation

  • Authors: W.Y. Chen, T.C. Wang, R.S. Guo, C. Chen
  • Conference: Proceedings of the 9th International Conference on Big Data Analytics (ICBDA) (2024)
  • Type: Conference Paper
  • Citations: 1
  • Summary: This paper integrates LDA and Gradient Boosting Trees to refine insurance product development recommendations, offering a data-driven approach for personalized insurance solutions.

4. Data Mining Methods to Support C2M Product-Service Systems Design and Recommendation System Based on User Value

  • Authors: T.C. Wang, R.S. Guo, C. Chen
  • Conference: 2022 Portland International Conference on Management of Engineering and Technology (PICMET)
  • Type: Conference Paper
  • Citations: 1
  • Summary: This study explores data mining techniques to enhance Consumer-to-Manufacturer (C2M) product-service system design, optimizing recommendation systems based on user value analysis.

5. Customer Demand Evaluation Method

  • Author: T.C. Wang
  • Patent: TW Patent TW202,414,306 A (2024)
  • Type: Patent
  • Summary: This patent presents a novel method for evaluating customer demand using AI-driven analytics, enhancing precision in product development and market segmentation.

Conclusion

Dr. Tzu-Chien Wang is a strong candidate for the Best Researcher Award, given his expertise in AI, machine learning, and business intelligence, along with his demonstrated contributions to academia and industry. His innovative research, patents, and funded projects underscore his impact. By expanding global collaborations, diversifying his research themes, and increasing engagement in AI policy and ethics, he can further solidify his standing as a leading researcher in artificial intelligence

An Zeng | Machine Learning | Best Researcher Award

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Prof. An Zeng | Machine Learning | Best Researcher Award

Professor at Guangdong University of Technology, China📖

Professor Zeng An is a distinguished researcher with extensive expertise in machine learning, data mining technologies, and their applications in medicine. Her work has significantly contributed to the advancement of deep learning, neural networks, probabilistic models, rough set theory, genetic algorithms, and other optimization methods. Since her postdoctoral research at the National Research Council of Canada and Dalhousie University (2008–2011) under the guidance of Professor Kenneth Rockwood, Professor Xiaowei Song, and Professor Arnold Mitnitski, she has been dedicated to applying these computational techniques to clinical research on Alzheimer’s Disease (AD).

Profile

Scopus Profile

Education Background🎓

Professor Zeng An completed her postdoctoral research at the National Research Council of Canada, collaborating with leading experts in medical AI applications. She holds a Ph.D. in Computer Science with a focus on machine learning and data mining techniques for medical applications. Her academic journey also includes a master’s and a bachelor’s degree in computer science or related fields (specific institutions and years can be added if available).

Professional Experience🌱

With a career spanning academia and research, Professor Zeng An has held key positions in leading universities and research institutions. During her postdoctoral tenure (2008–2011), she worked at Dalhousie University’s Faculty of Computer Science and Faculty of Medicine, contributing to AI-driven clinical research on neurodegenerative diseases. She has since continued her work in academia, conducting research on advanced machine learning techniques, medical data analysis, and clinical decision support systems.

Research Interests🔬

Professor Zeng An’s research focuses on developing intelligent algorithms for medical applications, particularly in Alzheimer’s Disease diagnostics and prediction. She specializes in deep learning, neural networks, probabilistic models, genetic algorithms, and optimization techniques. Her work extends to clinical data mining, patient risk assessment, and AI-driven medical decision-making, significantly impacting precision medicine.

Author Metrics

Professor Zeng An has a strong publication record in high-impact journals and conferences related to machine learning, AI in healthcare, and medical informatics. Her work has received substantial citations, reflecting her influence in the field. Key metrics such as H-index, i10-index, and total citations further highlight her academic contributions (specific numbers can be added if available).

Awards & Honors

Throughout her career, Professor Zeng An has received prestigious awards and recognitions for her contributions to AI and medical research. Her collaborations with renowned scientists in AI-driven healthcare innovations have led to groundbreaking advancements in the field. She continues to be a leading figure in interdisciplinary research, bridging computer science and medicine for improved healthcare outcomes.

Publications Top Notes 📄

1. Reinforcement Learning-Based Method for Type B Aortic Dissection Localization

  • Authors: Zeng An, Xianyang Lin, Jingliang Zhao, Baoyao Yang, Xin Liu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: This study presents a reinforcement learning-based approach for accurately localizing Type B aortic dissection, improving diagnostic precision in medical imaging.

2. Progressive Deep Snake for Instance Boundary Extraction in Medical Images (Open Access)

  • Authors: Zixuan Tang, Bin Chen, Zeng An, Mengyuan Liu, Shen Zhao
  • Journal: Expert Systems with Applications, 2024
  • Citations: 2
  • Summary: The research introduces a progressive deep snake model to enhance boundary extraction in medical images, facilitating precise segmentation for clinical applications.

3. Multi-Scale Quaternion CNN and BiGRU with Cross Self-Attention Feature Fusion for Fault Diagnosis of Bearing

  • Authors: Huanbai Liu, Fanlong Zhang, Yin Tan, Shenghong Luo, Zeng An
  • Journal: Measurement Science and Technology, 2024
  • Citations: 1
  • Summary: This paper develops a multi-scale quaternion CNN and BiGRU model integrating cross self-attention feature fusion to enhance the accuracy of bearing fault diagnosis in industrial applications.

4. An Ensemble Model for Assisting Early Alzheimer’s Disease Diagnosis Based on Structural Magnetic Resonance Imaging with Dual-Time-Point Fusion

  • Authors: Zeng An, Jianbin Wang, Dan Pan, Wenge Chen, Juhua Wu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: The study proposes an ensemble model utilizing dual-time-point fusion of MRI scans to improve early detection and diagnosis of Alzheimer’s Disease.

5. FedDUS: Lung Tumor Segmentation on CT Images Through Federated Semi-Supervised Learning with Dynamic Update Strategy

  • Authors: Dan Wang, Chu Han, Zhen Zhang, Zhenwei Shi, Zaiyi Liu
  • Journal: Computer Methods and Programs in Biomedicine, 2024
  • Summary: This research introduces a federated semi-supervised learning framework with a dynamic update strategy for effective lung tumor segmentation in CT imaging.

Conclusion

Professor An Zeng is a highly qualified candidate for the Best Researcher Award, given her outstanding contributions to AI in medicine, deep learning, and computational diagnostics. Her strong publication record, international research experience, and interdisciplinary approach make her an excellent nominee. While expanding clinical collaborations and citation impact would further enhance her profile, her cutting-edge research already positions her as a leader in medical AI applications.

Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

Mohammad Reza Nikpour at University of Mohaghegh Ardabili, Iran📖

Dr. Mohammad Reza Nikpour is an esteemed scholar in Water Engineering, currently serving as a faculty member at the University of Mohaghegh Ardabili, Iran. His expertise lies in hydrodynamics, river engineering, and water resource management, with extensive contributions to computational modeling and environmental sustainability.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

  • Ph.D. in Water Engineering, University of Mohaghegh Ardabili, Iran
  • M.Sc. in Water Engineering, University of Mohaghegh Ardabili, Iran
  • B.Sc. in Water Engineering, University of Mohaghegh Ardabili, Iran

Professional Experience🌱

Dr. Nikpour has been actively involved in academic research and teaching at the University of Mohaghegh Ardabili. His work focuses on computational hydrodynamics, groundwater quality assessment, and flood prediction modeling. He has collaborated with international researchers and contributed to innovative water management solutions through data-driven models.

Research Interests🔬

Her research interests include:

  • Hydrodynamics and River Engineering
  • Groundwater Quality Assessment
  • Soft Computing and AI Applications in Water Resource Management
  • Flood Prediction and Climate Change Impact Studies

Author Metrics

Dr. Mohammad Reza Nikpour has established a strong academic presence with numerous publications in high-impact journals, including River Research and Applications, Journal of Cleaner Production, and Stochastic Environmental Research and Risk Assessment. His research contributions have been widely recognized, earning him a growing citation count on Google Scholar and an impressive h-index on Scopus (to be verified). As a highly cited researcher in water engineering, his work has significantly influenced hydrodynamics, groundwater quality assessment, and computational water resource management. His ORCID ID is 0000-0003-4332-0525, and his research continues to shape innovative solutions in environmental sustainability and AI-driven water system modeling.

Awards and Honors
  • Recognized for outstanding contributions in hydrodynamic modeling and water resource sustainability.
  • Published multiple high-impact research papers in top-tier journals such as River Research and Applications, Journal of Cleaner Production, and Stochastic Environmental Research and Risk Assessment.
  • Recipient of research grants and funding for pioneering studies in environmental and computational water management.
Publications Top Notes 📄

1. Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques

  • Authors: H. Sanikhani, O. Kisi, M.R. Nikpour, Y. Dinpashoh
  • Journal: Water Resources Management
  • Volume: 26
  • Pages: 4347-4365
  • Year: 2012
  • Citations: 70
  • Summary: This study applies adaptive neuro-fuzzy inference system (ANFIS) models to estimate daily pan evaporation, comparing their accuracy and efficiency in hydrological forecasting.

2. Experimental and numerical simulation of water hammer

  • Authors: M.R. Nikpour, A.H. Nazemi, A.H. Dalir, F. Shoja, P. Varjavand
  • Journal: Arabian Journal for Science and Engineering
  • Volume: 39
  • Pages: 2669-2675
  • Year: 2014
  • Citations: 48
  • Summary: This paper investigates water hammer phenomena using both experimental methods and numerical simulations, providing insights into fluid dynamics and pipeline safety.

3. Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables

  • Authors: F. Esmaeilbeiki, M.R. Nikpour, V.K. Singh, O. Kisi, P. Sihag, H. Sanikhani
  • Journal: Journal of Cleaner Production
  • Volume: 276
  • Article: 124206
  • Year: 2020
  • Citations: 31
  • Summary: This research explores soft computing techniques, such as machine learning, for the spatial analysis of groundwater quality, enhancing environmental monitoring and sustainability.

4. Hydrodynamics of river-channel confluence: toward modeling separation zone using GEP, MARS, M5 Tree, and DENFIS techniques

  • Authors: O. Kisi, P. Khosravinia, M.R. Nikpour, H. Sanikhani
  • Journal: Stochastic Environmental Research and Risk Assessment
  • Volume: 33 (4-6)
  • Pages: 1089-1107
  • Year: 2019
  • Citations: 28
  • Summary: The study applies various data-driven models, including gene expression programming (GEP) and M5 Tree, to model separation zones in river confluences, improving hydrodynamic predictions.

5. Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections

  • Authors: P. Khosravinia, M.R. Nikpour, O. Kisi, Z.M. Yaseen
  • Journal: Computers and Electronics in Agriculture
  • Volume: 170
  • Article: 105283
  • Year: 2020
  • Citations: 16
  • Summary: This paper investigates the use of advanced data mining techniques to predict discharge and end depth in trapezoidal channels, optimizing water resource management and agricultural planning.

Conclusion

Dr. Mohammad Reza Nikpour is an exceptional researcher in AI-driven water resource management, making him a strong candidate for the Best Researcher Award. His pioneering work in soft computing and AI applications for hydrology and environmental sustainability sets him apart in his field. Expanding into deep learning, increasing industry collaborations, and engaging in AI conferences could further solidify his leadership in AI for water engineering.