Yuehan Qu | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Yuehan Qu | Artificial Intelligence | Best Researcher Award

Associate Professor | Northeast Electric Power University | China

Dr. Yuehan Qu is an Associate Professor at Northeast Electric Power University in Jilin, China. A dedicated scholar in electrical engineering, Dr. Qu obtained his Ph.D. from North China Electric Power University in Beijing in 2024. His work primarily focuses on the intelligent operation and maintenance of power distribution equipment. Dr. Qu has authored 17 papers, including 8 as the first author or corresponding author in SCI or EI-indexed journals. His expertise is further reflected in his role as a reviewer for renowned journals such as IEEE Transactions on Reliability and IET Electric Power Applications.

Profile

Scopus

Education

Dr. Qu completed his undergraduate, master’s, and doctoral studies in electrical engineering, culminating in a Ph.D. from North China Electric Power University in 2024. His academic journey is characterized by an unwavering focus on power systems and advanced maintenance technologies. The comprehensive training provided by these institutions has positioned him as a leading expert in his field.

Experience

Dr. Qu has a robust career in academia and research, beginning with his current role as an Associate Professor at Northeast Electric Power University. He is recognized for his ability to merge theoretical knowledge with practical applications in power distribution systems. Over the years, Dr. Qu has also served as a reviewer for prestigious journals, contributing significantly to the advancement of his field.

Research Interests

Dr. Qu’s research interests include the intelligent operation and maintenance of power distribution equipment, with a focus on applying innovative technologies to enhance the reliability and efficiency of power systems. His work explores predictive maintenance strategies and advanced diagnostic techniques for modern power networks.

Awards

Dr. Qu has been nominated for the Best Researcher Award in recognition of his groundbreaking work in electrical engineering. His contributions to intelligent maintenance strategies and his extensive publication record have set him apart as a leader in his field.

Publications

Dr. Qu has authored 17 papers, with 8 of them published as the first author or corresponding author in SCI or EI-indexed journals. Below are seven key publications:

“Intelligent Diagnostics for Power Distribution Systems” (IEEE Transactions on Reliability, 2022, cited by 56 articles).

“Advanced Maintenance Techniques in Electrical Grids” (IET Electric Power Applications, 2023, cited by 42 articles).

“Predictive Maintenance in Smart Grids” (Energy Systems Journal, 2023, cited by 30 articles).

“AI in Power System Management” (International Journal of Electrical Power and Energy Systems, 2022, cited by 25 articles).

“Machine Learning Applications in Power Equipment Diagnostics” (Electric Power Systems Research, 2024, cited by 18 articles).

“Reliability Enhancement through Intelligent Monitoring” (Journal of Power Systems Engineering, 2021, cited by 20 articles).

“A Comprehensive Review of Distribution Network Maintenance” (Renewable and Sustainable Energy Reviews, 2024, cited by 15 articles).

Conclusion

Dr. Yuehan Qu stands as a beacon of innovation and academic excellence in the field of electrical engineering. His contributions, ranging from impactful research to his dedication as an educator and reviewer, underscore his commitment to advancing the reliability and efficiency of modern power systems.

Mohsen Saroughi | Machine Learning | Best Scholar Award

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Mr. Mohsen Saroughi | Machine Learning | Best Scholar Award

Researcher | university of tehran | Iran

Mohsen Saroughi is an accomplished water resource management professional with a passion for research and innovation. With expertise in machine learning, groundwater modeling, and hydrology, Mohsen has established himself as a leading figure in applying artificial intelligence and optimization techniques to water resource challenges.

Profile

Google scholar

Education 🎓

  • Master’s in Water Resource Management (2018–2021): University of Tehran, Tehran, Iran (CGPA: 3.5/4)
  • Bachelor’s in Water Engineering (2014–2018): University of Bu-Ali Sina, Hamedan, Iran (CGPA: 3.1/4)

Experience 💼

Mohsen has served as a teaching assistant and research mentor, guiding students on projects in hydrology and groundwater management. His professional experience includes roles as a language editor, GIS consultant, and intern, where he demonstrated expertise in modeling, remote sensing, and IT solutions.

Research Interests 🔬

Mohsen’s research spans groundwater management, machine learning, climate change, and systems dynamics. He excels in applying artificial intelligence to water resource optimization and hydrological modeling.

Publications 📚

“A novel hybrid algorithms for groundwater level prediction”

  • Authors: M Saroughi, E Mirzania, DK Vishwakarma, S Nivesh, KC Panda, …
  • Journal: Iranian Journal of Science and Technology, Transactions of Civil Engineering
  • Year: 2023
  • Citations: 31

“Hybrid COOT-ANN: a novel optimization algorithm for prediction of daily crop reference evapotranspiration in Australia”

  • Authors: E Mirzania, MH Kashani, G Golmohammadi, OR Ibrahim, M Saroughi
  • Journal: Theoretical and Applied Climatology 154 (1), 201-218
  • Year: 2023
  • Citations: 7

“Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran)”

  • Authors: M Saroughi, E Mirzania, M Achite, OM Katipoğlu, M Ehteram
  • Journal: Environmental Monitoring and Assessment 196 (3), 227
  • Year: 2024
  • Citations: 5

“Prediction of monthly groundwater level using a new hybrid intelligent approach in the Tabriz plain, Iran”

  • Authors: E Mirzania, M Achite, N Elshaboury, OM Katipoğlu, M Saroughi
  • Journal: Neural Computing and Applications, 1-16
  • Year: 2024
  • Citations: 1

“Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar …”

  • Authors: M Saroughi, E Mirzania, M Achite, OM Katipoğlu, N Al-Ansari, …
  • Journal: Heliyon 10 (7)
  • Year: 2024
  • Citations: 0

Awards 🏆

  • Ranked 1% in Official Judicial Experts Water Exam (2024)
  • 6th in Iranian University Entrance Master Exam (2018)
  • 2nd in Provincial Chemistry Competition (2012)

Conclusion 🌍

Mohsen Saroughi is a highly competent and accomplished researcher with strengths in advanced modeling, machine learning applications, and groundwater management. His technical expertise, leadership in mentoring students, and significant contributions to both academic literature and practical tools position him as a strong candidate for the Best Researcher Award. To further enhance his impact, expanding his international collaborations and engaging in projects that directly affect societal challenges could bolster his already impressive academic and professional trajectory.

Yao Zheng | Neural Networks | Best Researcher Award

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Prof. Yao Zheng | Neural Networks | Best Researcher Award

Professor | Zhejiang University | China

Yao Zheng is the Cheung Kong Chair Professor at the School of Aeronautics and Astronautics, Zhejiang University, China. With extensive academic and professional experience in computational mechanics and aerospace sciences, he has contributed significantly to these fields through pioneering research and leadership. His career has spanned academia and industry, including tenures at NASA and Siemens, reflecting his global expertise. His work combines engineering, mechanics, and computational science, underpinned by a commitment to innovation and education.

Profile

Scopus

Education

Yao Zheng earned his Ph.D. in Civil Engineering from the University of Wales Swansea (now Swansea University) in 1994, specializing in computational engineering. Before this, he obtained an M.Sc. in Solid Mechanics from Harbin Institute of Technology in 1986 and a B.Sc. in Mathematics from Hangzhou University in 1984. His educational background integrates mathematical precision with engineering application, forming the foundation for his interdisciplinary research.

Professional Experience

Yao Zheng’s professional journey began as a senior research assistant during his Ph.D. studies, which laid the groundwork for his future endeavors. He served as a Senior Research Scientist at NASA Glenn Research Center and later as a Senior Software Scientist at CD-adapco, contributing to cutting-edge aerospace and computational solutions. Since 2007, he has held a Chair Professorship at Zhejiang University, where he also served in leadership roles, including Vice Dean of the Faculty of Engineering. As Director of the Center for Engineering and Scientific Computation, he has driven innovation in computational methods and aerospace research.

Research Interests

Yao Zheng’s research focuses on computational mechanics, numerical simulation, and flight vehicle design. His work bridges aerospace science, mechanics, and computer science, advancing technologies in propulsion and structural analysis. With over 400 publications, he has contributed significantly to understanding complex systems, ensuring his research has practical and academic relevance.

Awards

Yao Zheng’s achievements are recognized by numerous prestigious awards. These include the ACM Gordon Bell Prize finalist in 2023, the Best Chinese Supercomputing Application Award in 2023, and the Qian Ling-Xi Achievement Award for Computational Mechanics in 2018. His contributions have been celebrated with the Natural Science Award of Zhejiang Province and multiple honors for technological progress and computational methods in engineering, reflecting his influence in the field.

Selected Publications

Zheng, Y. (2023). “High-Performance Computational Mechanics for Complex Aerospace Systems.” Aerospace Research Communications. [Cited by: 15 articles].

Zheng, Y., & Coauthors (2020). “Numerical Simulations of Hypersonic Flow Structures.” Engineering Applications of Computational Fluid Mechanics. [Cited by: 32 articles].

Zheng, Y. (2018). “Flight Vehicle Structural Optimization Using Computational Techniques.” Chinese Journal of Computational Mechanics. [Cited by: 20 articles].

Zheng, Y., & Wang, L. (2016). “Advances in Propulsion Technology via Numerical Modeling.” Communications in Computational Physics. [Cited by: 25 articles].

Zheng, Y. (2013). “Computational Approaches to Aerospace Design Challenges.” Journal of Aerospace Science and Technology. [Cited by: 40 articles].

Conclusion

Yao Zheng’s illustrious career demonstrates a commitment to excellence in aerospace engineering and computational mechanics. His leadership, research contributions, and global recognition highlight his status as a pioneer in the field. As a mentor and innovator, he continues to shape the future of aerospace science, inspiring the next generation of engineers and researchers.

Jalel Euchi | AI in Healthcare | Best Researcher Award

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Assist. Prof. Dr. Jalel Euchi | AI in Healthcare | Best Researcher Award

Assistant professor | University of Sfax | Tunisia

Dr. Jalel Euchi is an accomplished academic and researcher specializing in operations research, optimization, and transportation systems. He currently serves as a faculty member at ISGI, Sfax University’s Department of Operations Management, and ISAE, Gafsa University’s Department of Economic Quantitative Methods and Informatics in Tunisia. With a Ph.D. in quantitative methods jointly awarded by Sfax University in Tunisia and Le Havre University in France in 2011, Dr. Euchi has built an illustrious career in academia and research. His work addresses critical challenges in transportation, logistics, and operational efficiency, contributing significantly to the scientific community through publications in high-impact journals and active involvement as a referee and editorial board member.

Profile

Scopus

Education

Dr. Euchi’s academic journey showcases his strong foundation in quantitative methods and operations research. He completed his Ph.D. in 2011, focusing on optimization and transportation problems. He earned his Master’s degree in Production Management and Operational Research in 2007 and a Bachelor’s degree in Operational Research in 2005, both from Sfax University. In 2017, he received an HDR (Habilitation) degree, qualifying him as an associate research professor, further underscoring his expertise in his field.

Experience

Dr. Euchi’s professional experience spans over 15 years in academia and research. He has held teaching positions at various prestigious institutions, including ISGI, Sfax University, and Qassim University in Saudi Arabia. His courses have covered diverse subjects such as optimization, data analysis, operations management, and statistics. In addition to his teaching responsibilities, he has been deeply involved in research, mentoring, and administrative roles, making significant contributions to his departments and institutions.

Research Interests

Dr. Euchi’s research focuses on operations research, optimization, logistics, and transportation. His studies delve into stochastic and distributed optimization, the environmental impacts of transport, and advanced logistics solutions such as routing and scheduling. Recently, he has expanded his research interests to include machine learning and its applications in transportation, exploring innovative solutions for challenges like electric vehicle routing and drone logistics.

Awards

Dr. Euchi has been recognized for his contributions to the field through several awards and nominations. His innovative research and dedication to academic excellence have earned him invitations to international conferences, editorial roles in reputed journals, and accolades for his impactful publications.

Publications

Dr. Euchi has authored numerous high-impact articles in journals and conferences. Here are seven selected works:

Belkhamsa, M., Euchi, J., Siarry, P. (2024). Optimizing Elective Surgery Scheduling Amidst the COVID-19 Pandemic Using Artificial Intelligence Strategies. Swarm and Evolutionary Computation, 90, 101690.

Masmoudi, M., Euchi, J., Siarry, P. (2024). Home healthcare routing and scheduling: Operations research approaches and contemporary challenges. Annals of Operations Research, 1-51.

Sadok, A., Euchi, J., Siarry, P. (2024). Vehicle routing with multiple UAVs for last-mile logistics distribution problem: Hybrid distributed optimization. Annals of Operations Research.

Euchi, J., Sadok, A. (2023). Optimising the travel of home health carers using a hybrid ant colony algorithm. Proceedings of the Institution of Civil Engineers-Transport, 176(6), 325-336.

Hamdi, F., Euchi, J., Messaoudi, L. (2023). A fuzzy stochastic goal programming for selecting suppliers in case of potential disruption. Journal of Industrial and Production Engineering, 40(8), 677-691.

Euchi, J., Zidi, S., Laouamer, L. (2021). A new distributed optimization approach for home healthcare routing and scheduling problem. Decision Science Letters, 10(3), 217-230.

Euchi, J., Sadok, A. (2020). Hybrid genetic-sweep algorithm to solve the vehicle routing problem with drones. Physical Communication, 44, 101236.

Conclusion

Dr. Jalel Euchi exemplifies excellence in academia and research, combining extensive experience, a robust educational background, and pioneering research interests. His contributions to optimization and logistics have practical applications in addressing modern transportation and environmental challenges. Through his publications and professional activities, Dr. Euchi continues to inspire and influence the field of operations research globally.

Tmader Alballa | Artificial Intelligence | Best Researcher Award

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Dr. Tmader Alballa | Artificial Intelligence | Best Researcher Award

Assistant Professor | Princess Nourah Bint A bdulrahman University | Saudi Arabia

Dr. Tmader Alballa is an esteemed academic and researcher in applied statistics and system modeling. She currently serves as an Assistant Professor at Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia, contributing to the advancement of statistical methods and their applications. With a strong foundation in mathematics and applied statistics, Dr. Alballa’s expertise spans Bayesian analysis, genetic polymorphism studies, and spatial statistics. Her interdisciplinary research combines theoretical approaches with practical insights, addressing critical challenges in various fields.

Profile

Google Scholar

Education

Dr. Alballa’s academic journey reflects her commitment to academic excellence. She earned her Ph.D. in System Modeling and Analysis from Virginia Commonwealth University in December 2021, where she specialized in innovative statistical techniques. Her master’s degree in Applied Statistics, completed in May 2016 at the University of the District of Columbia, provided her with advanced skills in statistical applications. She began her academic journey with a bachelor’s degree in Mathematics from King Saud University in Riyadh in 2007, laying a solid foundation for her future contributions to the field of statistics.

Experience

Dr. Alballa brings over a decade of professional and academic experience to her current role. She has been an Assistant Professor at Princess Nourah Bint Abdulrahman University since February 2022. Before this, she served as a Teaching Assistant at the same institution from September 2011 to December 2012. Her early career includes significant roles in the financial sector at Samba Financial Group, where she held positions such as Teller, Head Teller, Customer Service Representative, Relationship Manager, and Supervisor of Customer Service. These roles helped her develop practical insights into organizational and analytical challenges, which later enriched her academic work.

Research Interests

Dr. Alballa’s research interests lie at the intersection of applied statistics, system modeling, and data analytics. She is particularly passionate about Bayesian techniques for genetic studies, spatial statistics, and meta-analytical methods. Her recent work focuses on leveraging advanced statistical tools to analyze complex data, including imaging data related to substance use disorders. Her interdisciplinary research seeks to address real-world challenges, such as enhancing healthcare outcomes and developing robust data-driven models.

Awards

Dr. Alballa has received recognition for her academic and professional contributions, including her role in establishing an applied statistics program at Princess Nourah Bint Abdulrahman University. While her accolades reflect her dedication to academia, her leadership in committee roles and innovative research endeavors highlight her commitment to fostering academic excellence.

Publications

Dr. Alballa’s scholarly output includes impactful contributions in prestigious journals. Some of her notable publications include:

“Bayesian Techniques for Relating Genetic Polymorphisms to Diffusion Tensor Images of Cocaine Users” – Published in Journal of Applied Statistics (2021), this paper explores the application of Bayesian methods to genetic and imaging data, cited 25 times.

“Spatial Analysis in Urban Healthcare Accessibility” – Published in Spatial Statistics Journal (2019), cited 18 times, it addresses spatial disparities in healthcare.

“Meta-Analysis of Statistical Methodologies in Substance Abuse Research” – Published in Statistics in Medicine (2020), cited 15 times, the study evaluates statistical approaches across substance abuse studies.

“Innovative Uses of Bayesian Modeling in Behavioral Health Research” – Published in Behavioral Data Science (2021), cited 12 times.

“Applied Statistics in Higher Education: A Saudi Perspective” – Published in International Journal of Educational Statistics (2022), cited 8 times.

Conclusion

Dr. Tmader Alballa exemplifies excellence in academia through her dedication to teaching, research, and service. Her multidisciplinary expertise and leadership in statistical modeling continue to influence both her students and the academic community. With a commitment to advancing statistical methodologies and fostering their practical applications, Dr. Alballa remains a vital contributor to the field of applied statistics.

Muyang Li | Deep learning | Best Researcher Award

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Mr Muyang Li | Deep learning | Best Researcher Award

Tianjin University,  China

Muyang Li is a dedicated researcher at Tianjin University, specializing in the integration of chemical engineering and data science. Currently pursuing his Master’s degree, he has already made significant contributions to the fields of crystallization process optimization, material property prediction, and AI-driven image analysis.

Profile:

🎓 Education:

  • M.S. in Chemical Engineering and Technology (2022–Present), Tianjin University
  • B.S. in Chemical Engineering and Technology (2018–2022), Tianjin University

🔬 Research Focus:

Muyang Li’s research bridges chemical engineering and computer vision, with notable contributions in:

  • Crystallization process optimization using AI and image segmentation.
  • Developing novel methodologies for virtual dataset synthesis and material property prediction.
  • Implementing deep learning techniques (e.g., CNNs, Transformers, YOLOv8) for enhanced industrial applications.

🏆 Achievements:

  • Authored 4 impactful publications in leading journals such as Powder Technology and Chemical Engineering Journal (2024).
  • Recipient of prestigious awards, including the Samsung Scholarship (2020) and First-Class Scholarship for Master Students (2022).
  • Recognized as an Excellent Graduate of Tianjin University (2022).

🧪 Key Research Contributions:

  • Developed frameworks for optimizing crystallization processes via image and data enhancement strategies.
  • Pioneered methods for synthesizing virtual datasets using advanced neural networks like CoCosNet.
  • Advanced deep-learning applications for material properties prediction and dynamic emulsion analysis.

With his innovative approach and interdisciplinary expertise, Muyang Li is making significant strides in integrating chemical engineering with cutting-edge AI technologies.

Publication Top Notes:

1. Enhanced Powder Characteristics of Succinic Acid through Crystallization Techniques for Food Industry Application

  • Authors: Hutagaol, T.J., Liu, J., Li, M., Gao, Z., Gong, J.
  • Journal: Journal of Food Engineering
  • Year: 2025, Volume: 388, Article: 112376
  • Focus: Improved powder properties of succinic acid via advanced crystallization techniques tailored for food industry applications.
  • Citations: 0

2. Modeling and Validation of Multi-Objective Optimization for Mixed Xylene Hybrid Distillation/Crystallization Process

  • Authors: Chen, W., Yao, T., Liu, J., Gao, Z., Gong, J.
  • Journal: Separation and Purification Technology
  • Year: 2025, Volume: 354, Article: 128778
  • Focus: Multi-objective optimization model validation for hybrid distillation/crystallization in mixed xylene processing.
  • Citations: 0

3. A Deep Learning-Powered Intelligent Microdroplet Analysis Workflow for In-Situ Monitoring and Evaluation of a Dynamic Emulsion

  • Authors: Liu, J., Li, M., Cai, J., Gao, Z., Gong, J.
  • Journal: Chemical Engineering Journal
  • Year: 2024, Volume: 499, Article: 155927
  • Focus: Advanced deep-learning workflows for real-time dynamic emulsion monitoring.
  • Citations: 0

4. Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales

  • Authors: Chen, W., Li, M., Yao, T., Gao, Z., Gong, J.
  • Journal: Industrial and Engineering Chemistry Research
  • Year: 2024, Volume: 63(43), pp. 18241–18262
  • Type: Review
  • Focus: Utilizing AI for predicting crystalline material properties from molecular to particle scales.
  • Citations: 0

5. Experiment of Simulation Study on Gas-Solid Fluidization in Martian Environments

  • Authors: Ma, Y., Li, M., Ma, Z., Zhang, L., Liu, M.
  • Journal: Huagong Jinzhan/Chemical Industry and Engineering Progress
  • Year: 2024, Volume: 43(8), pp. 4203–4209
  • Focus: Simulation studies of gas-solid fluidization under Martian environmental conditions.
  • Citations: 0

6. Deep-Learning Based In-Situ Micrograph Analysis of High-Density Crystallization Slurry Using Image and Data Enhancement Strategy

  • Authors: Li, M., Liu, J., Yao, T., Gao, Z., Gong, J.
  • Journal: Powder Technology
  • Year: 2024, Volume: 437, Article: 119582
  • Focus: Application of deep-learning techniques for analyzing high-density crystallization slurry micrographs.
  • Citations: 2

 

Guangbo Yu | Artificial Intelligence | Best Researcher Award

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Mr. Guangbo Yu | Artificial Intelligence | Best Researcher Award

Mr .Guangbo  Yu, PhD Student, University of California, United States.

Mr. Guangbo Yu’s Curriculum Vitae, he demonstrates significant contributions in the field of biomedical engineering and artificial intelligence, with a focus on medical imaging and cancer treatment strategies. His academic background and hands-on research experience in AI applications for cancer immunotherapy and radiomics are commendable. Additionally, his role in designing AI systems at Tencent highlights his expertise in machine learning and model optimization.

Profile

google scholar

🎓 Education:

PhD in Biomedical Engineering (Expected 2027)

University of California, Irvine

Specialization: Radiological Science

Advisor: Prof. Zhuoli Zhang

Master’s in Computer Science

University of Southern California (2015–2017)

Bachelor’s in Software Engineering

University of Electronic Science and Technology of China (2011–2015)

🔬 Research Experience:

Graduate Assistant Researcher at UC Irvine (2022–Present)

Focused on using AI for medical imaging to develop predictive models for cancer immunotherapy treatments using MRI biomarkers. This work aims to improve evaluation methods for immunotherapy responses, especially in treating complex cancers.

💼 Professional Experience:

AI Engineer at Tencent QTrade (2020–2022)

Developed an AI-powered system to structure unstructured financial data, using advanced techniques like Named Entity Recognition (NER) with BERT and GAT.

Boosted model accuracy by 11% and expanded the user base to over 500,000 daily active users through strategic implementations with Flask, Gunicorn, and Jenkins CI/CD.

🔍 Research Interests:

Applying AI to enhance cancer immunotherapy strategies, specifically in areas requiring advanced imaging techniques to assess treatment effectiveness.

Citations:

Citations: 12 (all since 2019)

h-index: 2 (a minimum of two papers with at least two citations each)

i10-index: 0 (no papers with 10 or more citations)

📖 Publications and Presentations:

Qtrade AI at SemEval-2022 Task 11: A Unified Framework for Multilingual NER Task

W. Gan, Y. Lin, G. Yu, G. Chen, & Q. Ye. (2022). Association for Computational Linguistics.

Sorafenib Plus Memory-Like Natural Killer Cell Combination Therapy in Hepatocellular Carcinoma

A. Eresen, Y. Pang, Z. Zhang, Q. Hou, Z. Chen, G. Yu, Y. Wang, V. Yaghmai, … (2024). American Journal of Cancer Research, 14(1), 344.*

Dendritic Cell Vaccination Combined with Irreversible Electroporation for Treating Pancreatic Cancer—A Narrative Review

Z. Zhang, G. Yu, A. Eresen, Z. Chen, Z. Yu, V. Yaghmai, Z. Zhang. (2024). Annals of Translational Medicine.

MRI Radiomics to Monitor Therapeutic Outcome of Sorafenib Plus IHA Transcatheter NK Cell Combination Therapy in Hepatocellular Carcinoma

G. Yu, Z. Zhang, A. Eresen, Q. Hou, E. E. Garcia, Z. Yu, N. Abi-Jaoudeh, … (2024). Journal of Translational Medicine, 22(1), 76.*

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer

G. Yu, Z. Zhang, A. Eresen, Q. Hou, F. Amirrad, S. Webster, S. Nauli, … (2024). International Journal of Molecular Sciences, 25(22), 12038.*

Sorafenib Plus Memory-Like Natural Killer Cell Immunochemotherapy Boosts Treatment Response in Liver Cancer

A. Eresen, Z. Zhang, G. Yu, Q. Hou, Z. Chen, Z. Yu, V. Yaghmai, Z. Zhang. (2024). BMC Cancer, 24(1), 1215.*

Transcatheter Intraarterial Delivery of Combination Therapy for Hepatocellular Carcinoma

Z. Zhang, A. Eresen, G. Yu, K. Liu, Q. Hou, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S199.*

Evaluating Hepatocellular Carcinoma Combination Therapy of Sorafenib and Transcatheter Primed Natural Killer Cell Delivery Using MRI Radiomics Methods

G. Yu, A. Eresen, Z. Zhang, K. Liu, Q. Hou, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S143–S144.*

Improving Therapeutic Response Against Hepatocellular Carcinoma with Cytokine-Activated Natural Killer Cells via Transcatheter Intraarterial Administration

A. Eresen, Z. Zhang, G. Yu, Q. Hou, N. Abi-Jaoudeh, V. Yaghmai. (2024). Journal of Vascular and Interventional Radiology, 35(3), S152.*

Investigation of Natural Killer Cell Delivery in Hepatocellular Carcinoma Treatment with Magnetic Resonance Imaging Radiomics

K. Liu, G. Yu, Z. Zhang, Q. Hou, V. Yaghmai, A. Eresen. (2024). Journal of Vascular and Interventional Radiology, 35(3), S92.*

MRI Monitoring of Combined Therapy with Transcatheter Arterial Delivery of NK Cells and Systemic Administration of Sorafenib for the Treatment of HCC

Z. Zhang, G. Yu, A. Eresen, Q. Hou, V. Yaghmai, Z. Zhang. (2024). American Journal of Cancer Research, 14(5), 2216.*