Xin Zhao | Heat Transfer | Best Researcher Award

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Assi. Prof. Dr. Xin Zhao | Heat Transfer | Best Researcher Award

Assi. Prof. Dr. Xin Zhao | Inner Mongolia University | China

Assi. Prof. Dr. Xin Zhao is a researcher specializing in cold-region transportation engineering, with a strong focus on freezing theory, frost damage mechanisms, and anti-freezing technologies for tunnels, highways, and subgrade systems. His work advances the understanding of multi-factor coupled thermal–hydrological processes, frost-heave behavior, and risk-grading models in frozen soil environments. He has led multiple scientific projects, published high-impact SCI and EI papers, and developed a whole-chain technical framework integrating investigation, diagnosis, prediction, evaluation, and treatment of frost damage. His contributions include frost-damage databases, analytical and numerical models, and risk-control grading systems that support safe infrastructure development in frigid regions. With extensive patents, standards contributions, editorial roles, and interdisciplinary collaborations, he continues to enhance theoretical research and engineering applications in frozen-soil geotechnics. Assi. Prof. Dr. Xin Zhao work has gained growing academic influence, reflected in strong citation metrics across major indexing platforms

Profile: Orcid

Featured Publications:

Zhao, X., Zhang, H. W., Wang, X. Y., & Zheng, H. (2025). Analysis on the cooling effect of the thermosyphon buried in the subgrade of the permafrost regions of Inner Mongolia, China. Cold Regions Science and Technology, 238, 104546. (JCR Q1)

Zhao, X., Zhang, H. W., Wang, X. Y., & Zheng, H. (2025). A whole-chain technical system for multi-factor coupling mechanism, risk grading and control of frost damage in cold-region tunnels. Case Studies in Thermal Engineering, 76, 107382. (JCR Q1)

Zhao, X., Yang, X. H., Zhang, H. W., Lai, H. P., & Wang, X. Y. (2020). An analytical solution for frost heave force by the multifactor of coupled heat and moisture transfer in cold-region tunnels. Cold Regions Science and Technology, 175, 103077. (JCR Q1)

Zhao, X., Zhang, H. W., Lai, H. P., Yang, X. H., Wang, X. Y., & Zhao, X. L. (2020). Temperature field characteristics and influencing factors on frost depth of a highway tunnel in a cold region. Cold Regions Science and Technology, 179, 103141. (JCR Q1)

Zhao, X., & Yang, X. H. (2019). Experimental study on water inflow characteristics of tunnel in the fault fracture zone. Arabian Journal of Geoscience, 12, 399. (JCR Q3)

Peng Zhang | Single-Cell Analysis | Best Researcher Award

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Dr. Peng Zhang  | Single-Cell Analysis | Best Researcher Award

Dr. Peng Zhang | Shandong University |China

Dr. Peng Zhang is a multidisciplinary researcher whose work spans liver immunology, metabolic regulation, and computational biomedical science, with a focus on uncovering cellular and molecular mechanisms underlying liver diseases. His research integrates single-cell RNA sequencing, spatial transcriptomics, metabolic pathway analysis, and immunological profiling to explore conditions such as alcoholic-associated liver disease, cholestatic injury, NASH, liver cancer, ischemia–reperfusion injury, and metabolic dysfunction–associated steatotic liver disease. He has contributed to defining the roles of CD4⁺ T lymphocytes, liver endothelial cells, macrophage heterogeneity, and innate lymphoid cells in disease progression, revealing how metabolic reprogramming, sphingolipid pathways, and NAD homeostasis shape liver injury and regeneration. His work additionally bridges computational and mechanobiological modeling, providing insights into blood-flow-dependent inflammation, lipoprotein transport, and vascular biomechanics that inform cardiometabolic disease mechanisms. Through high-impact publications in immunology, hepatology, metabolism, and biomedical engineering, he has advanced understanding of liver microenvironmental crosstalk, immune–metabolic interactions, and molecular drivers of liver pathology. Overall, his research aims to translate mechanistic discoveries into therapeutic strategies by integrating systems biology, immunometabolism, and advanced computational analysis to better diagnose, prevent, and treat complex liver disorders.

Profile: Orcid

Zhang, P., Li, X., Liang, J., Zheng, Y., Tong, Y., Shen, J., Chen, Y., Han, P., Chu, S., Liu, R., Zheng, M., Zhai, Y., Tang, X., Zhang, C., Qu, H., Mi, P., Chai, J., Yuan, D., & Li, S. (2025). Chenodeoxycholic acid modulates cholestatic niche through FXR/Myc/P-selectin axis in liver endothelial cells. Nature Communications, 16(1), 2093.

Gao, C., Wang, S., Xie, X., Ramadori, P., Li, X., Liu, X., Ding, X., Liang, J., Xu, B., Feng, Y., Tan, X., Wang, H., Zhang, Y., Zhang, H., Zhang, T., Mi, P., Li, S., Zhang, C., Yuan, D., Heikenwalder, M., & Zhang, P. (2024). Single-cell profiling of intrahepatic immune cells reveals an expansion of tissue-resident cytotoxic CD4+ T lymphocyte subset associated with pathogenesis of alcoholic-associated liver diseases. Cellular and Molecular Gastroenterology and Hepatology, 19, 101411.

Shen, J., Li, Z., Liu, X., Zheng, M., Zhang, P., Chen, Y., Tian, Q., Tian, W., Kou, G., Cui, Y., Xu, B., Zhai, Y., Li, W., Guo, X., Qiu, J., Li, C., He, R., Li, L., Ma, C., Li, Y., Zuo, X., Yuan, D., & Li, S. (2024). Sensing of liver-derived nicotinamide by intestinal group 2 innate lymphoid cells links liver cirrhosis and ulcerative colitis susceptibility. Advanced Science, 11(23), e2404274.

Feng, Y., Wang, S., Xie, J., Ding, B., Wang, M., Zhang, P., Mi, P., Wang, C., Liu, R., Zhang, T., Yu, X., Yuan, D., & Zhang, C. (2023). Spatial transcriptomics reveals heterogeneity of macrophages in the tumor microenvironment of granulomatous slack skin. Journal of Pathology, 261(1), 105–119.

Wang, S., Chen, S., Sun, J., Han, P., Xu, B., Li, X., Zhong, Y., Xu, Z., Zhang, P., Mi, P., Zhang, C., Li, L., Zhang, H., Xia, Y., Li, S., Heikenwalder, M., & Yuan, D. (2023). m6A modification-tuned sphingolipid metabolism regulates postnatal liver development in male mice. Nature Metabolism, 5(6), 842–860.

 

Ai-Huei Chiou | Automated Inspection | Best Researcher Award

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Prof. Ai-Huei Chiou | Automated Inspection | Best Researcher Award

Prof. Ai-Huei Chiou | National Formosa University | Taiwan

Prof. Ai-Huei Chiou is a leading researcher whose work integrates artificial intelligence, Internet of Things (IoT), and sustainable green energy systems. Her research focuses on intelligent energy management, AI-assisted detection technologies, and renewable energy optimization, emphasizing high-efficiency, low-carbon, and environmentally adaptive solutions. She advances data-driven methods for smart grid performance, predictive maintenance, energy forecasting, and automated diagnostics, contributing to more resilient and sustainable energy infrastructures. Her interdisciplinary approach bridges computational modeling, environmental engineering, and real-time sensing, positioning her as an influential scholar in developing next-generation green technologies. With 479 citations, an h-index of 11, and 12 i10-index publications, as well as 252 citations, an h-index of 9, and 9 i10-index publications recorded across academic platforms, her work demonstrates strong scientific impact and continuous global relevance. Prof. Ai-Huei Chiou’ s contributions support the transition toward intelligent, AI-powered, and environmentally responsible energy systems.

Profile: Google Scholar

Featured Publications:

Chiou, A., & Huang, P. (2013). Optimization of micro milling electrical discharge machining of Inconel 718 by Grey-Taguchi method. Transactions of Nonferrous Metals Society of China, 23(3), 661–666.

Chiou, A., Chen, L. H., & Chen, S. K. (1991). Foodborne illness in Taiwan, 1981–1989, 452–453.

Chiou, A. H., Tsao, C. C., & Hsu, C. Y. (2015). A study of the machining characteristics of micro EDM milling and its improvement by electrode coating. The International Journal of Advanced Manufacturing Technology, 78(9), 1857–1864.

Twu, M. J., Chiou, A. H., Hu, C. C., Hsu, C. Y., & Kuo, C. G. (2015). Properties of TiO2 films deposited on flexible substrates using direct current magnetron sputtering and using high power impulse magnetron sputtering. Polymer Degradation and Stability, 117, 1–7.

Chiou, A. H., Chien, T. C., Su, C. K., Lin, J. F., & Hsu, C. Y. (2013). The effect of differently sized Ag catalysts on the fabrication of a silicon nanowire array using Ag-assisted electroless etching. Current Applied Physics, 13(4), 717–724.

Posen Lee | Computer Technology for Design and Simulation | Best Researcher Award

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Prof. Posen Lee | Computer Technology for Design and Simulation | Best Researcher Award

Prof. Posen Lee | I-Shou University | Taiwan

Prof. Posen Lee is an interdisciplinary scholar whose research advances psychiatric occupational therapy through the integration of psychometrics, artificial intelligence, and rehabilitation technology. His work focuses on developing and validating assessment tools for schizophrenia and other psychiatric disorders, applying AI-based motion and image analysis to rehabilitation practices, and conducting quantitative gait and balance studies in older adults and individuals with mental health conditions. He has contributed to the refinement of clinical communication assessment through the creation of a psychiatric OT-specific OSCE model and has led multiple government- and hospital-funded projects published in SCI and SSCI journals such as Biosensors, Bioengineering, Sensors, and the Asian Journal of Psychiatry. With 281 citations across 274 documents, 23 publications, and an h-index of 9, his scholarly work strengthens evidence-based mental health rehabilitation and promotes technology-enhanced, client-centered occupational therapy practice.

Profile: Scopus

Featured Publications :

Group Dynamics in Occupational Therapy: Applications and Innovations  Publisher.

Updated Group Dynamics in Occupational Therapy Publisher.

Occupational Therapy for Common Geriatric Disorders  Publisher.

Chia-Hung Lai | Machine Learning for Smart Manufacturing | Best Researcher Award

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Assoc. Prof. Dr. Chia-Hung Lai | Machine Learning for Smart Manufacturing | Best Researcher Award

Associate Professor | National Chin-Yi University of Technology | Taiwan

Chia-Hung Lai, Ph.D., is an interdisciplinary researcher whose work bridges intelligent automation, smart manufacturing, and advanced sensing technologies to enhance industrial reliability and technical education. His research integrates deep learning, machine vision, nondestructive testing, and engineering information security, with notable contributions to welding automation, gear defect detection, tool-breakage prediction, and secure engineering data transmission. He has developed innovative AI-driven diagnostic systems using convolutional neural networks, symmetrized dot patterns, discrete wavelet transforms, and current-sensing analytics, enabling high-precision detection of defects in manufacturing processes. His studies also explore VR/AR-based learning systems, reflecting his commitment to advancing industry-aligned technical education through immersive and intelligent technologies. In addition, he has contributed to environmentally sustainable engineering through deep learning approaches for monitoring emissions in industrial operations. His work in information security demonstrates a unique blend of engineering design and cybersecurity through novel applications of steganography in CAD environments. With multiple publications in SCIE-indexed journals and recognition through awards and competitive achievements, he has established a strong research footprint across automation, sensing, and applied AI. He actively contributes to the scholarly community through reviewing roles and by leading numerous industry–academic collaborative projects focused on intelligent systems, advanced diagnostics, and smart manufacturing innovation.

Profiles:  Scopus | Google Scholar

Featured Publications

Chien, Y.-C., Wu, T. T., Lai, C.-H., & Huang, Y.-M. (2022). Investigation of the influence of artificial intelligence markup language-based LINE ChatBot in contextual English learning. Frontiers in Psychology, 13, 785752.

Lai, C.-H., Liu, M.-C., Liu, C.-J., & Huang, Y.-M. (2016). Using positive visual stimuli to lighten the online learning experience through in-class questioning. International Review of Research in Open and Distributed Learning, 17(1), 23–41.

Huang, Y.-M., Liu, M.-C., Lai, C.-H., & Liu, C.-J. (2017). Using humorous images to lighten the learning experience through questioning in class. British Journal of Educational Technology, 48(3), 878–896.

Liu, C.-J., Huang, C.-F., Liu, M.-C., Chien, Y.-C., Lai, C.-H., & Huang, Y.-M. (2015). Does gender influence emotions resulting from positive applause feedback in self-assessment testing? Evidence from neuroscience. Journal of Educational Technology & Society, 18(1), 337–350.

Lai, C.-H., Wu, T. E., Huang, S.-H., & Huang, Y.-M. (2020). Developing a virtual learning tool for industrial high schools’ welding course. Procedia Computer Science, 172, 696–700.

Liu, M.-C., Lai, C.-H., Su, Y.-N., Huang, S.-H., Chien, Y.-C., & Huang, Y.-M., & Hwang, J. P. (2015). Learning with great care: The adoption of the multi-sensor technology in education. In Sensing technology: Current status and future trends III (pp. 223–242).

Lai, C.-H., & Yang, H.-C. (2016). Theoretical investigation of a planar rack cutter with variable diametral pitch. Arabian Journal for Science and Engineering, 41(5), 1585–1594.

Nadeer Gharaibeh | AI-based Medical Image Analysis | Best Researcher Award

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Dr. Nadeer Gharaibeh | AI-based Medical Image Analysis | Best Researcher Award

Master in Radiology | Huazhong University of Science and Technology | China

Dr. Nadeer Gharaibeh is an emerging radiology researcher whose work integrates advanced medical imaging, radiomics, and AI-assisted diagnostic technologies to enhance clinical interpretation and patient outcomes. His academic and clinical background spans radiology training, clinical imaging practice, and participation in multidisciplinary care, shaping a strong foundation in CT, MRI, radiomics analysis, and interventional imaging principles. With 3 citations across 3 indexed documents, 6 total research documents, and an h-index of 1, his early scholarly footprint reflects steady growth and increasing academic visibility. His research focuses on quantitative imaging, deep learning–based diagnostic enhancement, and the application of compositional MRI techniques for early disease detection. He has contributed to studies on musculoskeletal imaging, venous thrombosis assessment, knee joint instability detection using AI algorithms, and advanced MRI applications in spine pathology, reflecting his commitment to bridging imaging science with clinical relevance. Dr. Gharaibeh’s publications highlight diagnostic challenges, imaging biomarkers, and the potential of machine learning to refine radiologic evaluation. He has actively engaged in international radiology forums, imaging exchange programs, and academic collaborations, strengthening his global research perspective. Alongside his scientific work, he remains consistently involved in clinical projects, imaging workshops, and academic discussions, demonstrating strong analytical, communication, and teamwork capabilities. His broader contributions include community engagement and cultural initiatives, reflecting a well-rounded professional ethos grounded in service, leadership, and continuous learning. Overall, Dr. Gharaibeh’s research trajectory positions him as a dynamic contributor to the evolving fields of medical imaging, radiomics, and AI-driven radiology innovation.

Profile:  Scopus

Featured Publications

  • Gharaibeh, N. M., Fadoul, H. M., Al-Sarairah, A. H., & Li, X. (2025, July). Osteoid osteoma of the joint capsule: A case report highlighting diagnostic challenges and the role of advanced imaging.

  • Sun, D., Wu, G., Zhang, W., Gharaibeh, N. M., & Li, X. (2025, January). Visualizing preosteoarthritis: Updates on UTE-based compositional MRI and deep learning algorithms.

  • Li, T., Gharaibeh, N. M., Jia, S., & Wu, G. (2024, December). YOLOv8 algorithm-aided detection of patellar instability or dislocation on knee joint MRI images.

  • Wu, G., Wu, Y., Gharaibeh, N. M., & Li, X. (2024, August). Magnetic resonance evaluation of deep venous thrombosis of 338 discharged viral pneumonia patients.

  • Fadoul, H. M., Gharaibeh, N. M., Wu, G., & Li, X. (2024, February). The value of 3D SPACE MRI in differentiating between sequestrated lumbar disc herniation and tumors: Two cases and literature review.

 

Henry Barham | Surgery | Best Researcher Award

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Dr. Henry Barham | Surgery | Best Researcher Award

Rhinologist | Sinus and Nasal Specialists of Louisiana | United States

Henry Pipes Barham, MD, FARS is a distinguished rhinologist and skull base surgeon whose research advances the understanding and treatment of complex sinonasal and upper airway disorders. His scholarly work spans clinical investigation, molecular studies, and translational science, contributing valuable insights into pediatric and adult otolaryngologic conditions. Dr. Barham has published influential studies on curcumin’s therapeutic potential in head and neck squamous cell carcinoma through modulation of the Akt/mTOR pathway, expanding the scientific dialogue on targeted cancer therapies. His research also explores collaborative surgical strategies in pediatric thyroidectomy, rare inflammatory and congenital sinonasal disorders, and unusual airway manifestations associated with genetic syndromes. Notably, he has contributed to advancing knowledge on idiopathic sclerosing inflammation, congenital nasolacrimal duct anomalies, and complex paranasal sinus tumors, enriching diagnostic and therapeutic approaches in these challenging cases. Dr. Barham’s work in sensory cell biology, including investigations into solitary chemosensory cells and bitter taste receptor signaling in human sinonasal mucosa, has deepened understanding of airway immunological and chemosensory mechanisms. Beyond peer-reviewed publications, he has produced educational surgical content for the American Rhinologic Society, supporting global dissemination of advanced rhinologic techniques. Through a strong commitment to clinical excellence, innovation, and academic contribution, Dr. Barham continues to influence best practices in rhinology and skull base surgery.

Profile:  Orcid

Featured Publications

  • Clark, C. A., Rong, Y., Rong, X., Shah, S., Barham, H., & Nathan, C. O. (2009). Curcumin inhibits HNSCC by modulating the Akt/mTOR pathway. Oral Oncology, 3(1).

  • Wood, J. H., Partrick, D. A., Barham, H. P., Bensard, D. D., Travers, S. H., Bruny, J. L., & McIntyre, R. C., Jr. (2011). Pediatric thyroidectomy: A collaborative surgical approach. Journal of Pediatric Surgery, 46(5), 823–828.

  • Barham, H. P., Dishop, M. K., & Prager, J. D. (2012). Idiopathic sclerosing inflammation presenting as sinusitis. Allergy & Rhinology (Providence), 3(2), e101–e104.

  • Barham, H. P., Wudel, J. M., Enzenauer, R. W., & Chan, K. H. (2012). Congenital nasolacrimal duct cyst/dacryocystocele: An argument for a genetic basis. Allergy & Rhinology (Providence), 3(1), e46–e49.

  • Barham, H. P., Said, S., & Ramakrishnan, V. R. (2013). Colliding tumor of the paranasal sinus. Allergy & Rhinology (Providence), 4(1), e13–e16.

  • Barham, H. P., Cooper, S. E., Anderson, C. B., Tizzano, M., Kingdom, T. T., Finger, T. E., Kinnamon, S. C., & Ramakrishnan, V. R. (2013). Solitary chemosensory cells and bitter taste receptor signaling in human sinonasal mucosa. International Forum of Allergy & Rhinology, 3(6), 450–457.

 

Yunus Arzik | Animal Science | Excellence in Innovation Award

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Dr. Yunus Arzik | Animal Science | Excellence in Innovation Award

Assistant Professor | Aksaray University | Turkey

Dr. Yunus Arzik is a distinguished researcher at Aksaray University specializing in animal genetics and genomics, with a focus on genome-wide association studies (GWAS) for economically important traits and disease resistance in livestock. His research integrates quantitative genetics, genome analysis, and molecular biology to unravel the complex genetic mechanisms underlying growth, productivity, and health traits in sheep, cattle, and poultry. With 14 publications, over 130 citations from 95 documents, and an h-index of 7, Dr. Arzik has made notable contributions to advancing the understanding of genetic variation influencing animal performance and resilience. His recent works employ advanced genomic and transcriptomic approaches—such as RNA-Seq and small RNA-Seq—to identify candidate genes and regulatory pathways associated with traits like wool quality, parasite resistance, and metabolic responses in animals. By combining statistical genetics and machine learning-based GWAS models, his studies provide valuable insights into the genomic architecture of economically and biologically relevant traits, supporting the development of sustainable breeding strategies. His publications in leading journals such as Genes, Scientific Reports, International Journal of Molecular Sciences, and Veterinary Medicine and Science reflect the breadth and impact of his research. Dr. Arzik also contributes to the scientific community as a reviewer for international journals, reinforcing his commitment to advancing animal health, welfare, and productivity through innovative genomic research.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  • Yilmaz, O., Kizilaslan, M., Arzik, Y., Behrem, S., Ata, N., Karaca, O., Elmaci, C., et al. (2022). Genome‐wide association studies of preweaning growth and in vivo carcass composition traits in Esme sheep. Journal of Animal Breeding and Genetics, 139(1), 26–39.

  • Kizilaslan, M., Arzik, Y., White, S. N., Piel, L. M. W., & Cinar, M. U. (2022). Genetic parameters and genomic regions underlying growth and linear type traits in Akkaraman sheep. Genes, 13(8), 1414.

  • Arzik, Y., Kizilaslan, M., Behrem, S., White, S. N., Piel, L. M. W., & Cinar, M. U. (2023). Genome-wide scan of wool production traits in Akkaraman sheep. Genes, 14(3), 713.

  • Kizilaslan, M., Arzik, Y., Cinar, M. U., & Konca, Y. (2022). Genome-wise engineering of ruminant nutrition–nutrigenomics: Applications, challenges, and future perspectives – A review. Annals of Animal Science, 22(2), 511–521.

  • Kizilaslan, M., Arzik, Y., Behrem, S., White, S. N., & Cinar, M. U. (2024). Comparative genomic characterization of indigenous fat‐tailed Akkaraman sheep with local and transboundary sheep breeds. Food and Energy Security, 13(1), e508.

  • Arzik, Y., Kizilaslan, M., White, S. N., Piel, L. M. W., & Çınar, M. U. (2022). Genomic analysis of gastrointestinal parasite resistance in Akkaraman sheep. Genes, 13(12), 2177.

  • Arzik, Y., Kizilaslan, M., White, S. N., Piel, L. M. W., & Cinar, M. U. (2022). Estimates of genomic heritability and genome-wide association studies for blood parameters in Akkaraman sheep. Scientific Reports, 12(1), 18477.

  • Gul, S., Arzik, Y., Kizilaslan, M., Behrem, S., & Keskin, M. (2023). Heritability and environmental influence on pre-weaning traits in Kilis goats. Tropical Animal Health and Production, 55(2), 85.

Ilana Golub | Medicine | Best Researcher Award

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Dr. Ilana Golub | Medicine | Best Researcher Award

Resident Physician | University of California, Los Angeles | United States

Dr. Ilana S. Golub is a physician-scientist specializing in cardiovascular imaging, preventive cardiology, and academic internal medicine. Her research focuses on non-invasive cardiac imaging, particularly cardiac CT scanning, to advance the early detection and risk assessment of atherosclerotic cardiovascular disease. She has made significant contributions to understanding coronary calcium scoring, arterial calcification, and imaging biomarkers of subclinical atherosclerosis. Her first-author publication on “Major Global Coronary Artery Calcium Guidelines” was recognized among the American College of Cardiology’s Top 10 Contents, reflecting her influence on global clinical practice. Dr. Golub’s research portfolio includes 18 peer-reviewed publications, more than 35 published abstracts, and numerous national conference presentations. With 308 citations across 297 indexed documents and an h-index of 5, her scholarly impact spans both academic and clinical domains. In addition to her research, she has demonstrated outstanding leadership in mentorship, medical education, and community health through her work with the UCLA Mobile Clinic Project, integrating medical care with social work and public health outreach to serve unhoused populations. Her academic pursuits embody a strong commitment to advancing clinical excellence, research innovation, and compassionate, community-centered healthcare.

Profiles: Scopus | Orcid

Featured Publications

  • Golub, I. S., Lakshmanan, S., & Budoff, M. J. (2020). Myocardial crypt, diverticulum, or aneurysm? CTA as an adjudicator. International Journal of Cardiovascular Imaging, 36(10), 2061–2062.

  • Golub, I. S., Lakshmanan, S., Calicchio, F., & Budoff, M. J. (2020). Computed tomography angiogram: Diagnosing device placement failure. Journal of Cardiovascular Computed Tomography, 14(6), e163–e164.

  • Golub, I. S., Dahal, S., Calicchio, F., & Budoff, M. J. (2021). Novel use of coronary artery calcium scoring. Coronary Artery Disease, 32(1), 86–87.

  • Golub, I. S., Lakshmanan, S., Dahal, S., & Budoff, M. J. (2021). Utilizing coronary artery calcium to guide statin use. Atherosclerosis, 326, 17–24.

  • Golub, I. S., Dahal, S., Lakshmanan, S., & Budoff, M. (2021). Where is the stent? CTA assists angiography: A case of jailed LAD. Journal of Clinical Images and Medical Case Reports, 2. ISSN 2766-7820.

  • Golub, I. S., Lakshmanan, S., Dahal, S., Kristo, S., Schroeder, L., Termeie, O., Manubolu, V., Hussein, L., Verghese, D., Shafter, A. M., Casaburi, R., Budoff, M. J., & Roy, S. K. (2022). Aortic arch calcification and novel markers of subclinical atherosclerosis on lung CT: Methodology and reproducibility in the COPDgene study. Imaging in Medicine, 14(6). ISSN 1755-5191.

  • Golub, I. S., Sheppard, J. P., Lakshmanan, S., Dahal, S., Kinninger, A., Allison, M., Barr, G., McClelland, R., Blaha, M. J., Roy, S. K., & Budoff, M. J. (2022). Coronary artery and aortic arch calcification in ungated lung CT scans as predictors of ASCVD in the Multi-Ethnic Study of Atherosclerosis: Methods and reproducibility. Journal of Coronary Artery Disease, 28(4).

  • Golub, I. S., Termeie, O. G., Kristo, S., Schroeder, L. P., Lakshmanan, S., Shafter, A. M., Hussein, L., Verghese, D., Aldana-Bitar, J., Manubolu, V. S., & Budoff, M. J. (2023). Major global coronary artery calcium guidelines. JACC: Cardiovascular Imaging, 16(1), 98–117.

  • Golub, I. S., Benzing, T., Kianoush, S., Krishnan, S., Ichikawa, K., & Budoff, M. J. (2024). Hemodynamic significance of coronary anomalies: Computed tomography-based fractional flow reserve (CT-FFR) as an adjudicator. Coronary Artery Disease, 35(5), 440–441.

  • Golub, I. S., Misic, A., Schroeder, L. P., Aldana-Bitar, J., Krishnan, S., Kianoush, S., Benzing, T., Ichikawa, K., & Budoff, M. J. (2024). Calcific coronary lesions: Management, challenges, and a comprehensive review. AIMS Medical Science, 11(3), 292–317. 1

Yuming Jiang | Computational Oncology | Best Researcher Award

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Assist. Prof. Dr. Yuming Jiang | Computational Oncology | Best Researcher Award

Assistant Professor | Wake Forest University School of Medicine | United States

Dr. Yuming Jiang, MD, PhD, is a physician-scientist specializing in radiation oncology, artificial intelligence in cancer care, and precision oncology. His research integrates computational modeling, digital pathology, and radiomics to improve cancer diagnosis, prognosis prediction, and treatment response assessment. He has made pioneering contributions to the development of deep learning frameworks that noninvasively characterize the tumor microenvironment, predict immunotherapy response, and forecast recurrence and survival outcomes in gastrointestinal and other cancers. His high-impact publications in journals such as Nature Communications, The Lancet Digital Health, Annals of Oncology, and Journal of Clinical Oncology have significantly advanced the field of AI-driven oncology and personalized medicine. Dr. Jiang’s work emphasizes translational applications of biology-guided deep learning models to bridge clinical imaging, pathology, and genomics, offering novel insights into tumor biology and therapeutic decision-making. Beyond research, he actively contributes to the scientific community through editorial roles in leading journals including Frontiers in Oncology, Frontiers in Immunology, and npj Precision Oncology. With 2,944 citations across 2,326 documents, 78 publications, and an h-index of 29, Dr. Jiang’s scholarly impact reflects his leadership in computational oncology, fostering cross-disciplinary innovation between artificial intelligence, cancer biology, and clinical radiology to enhance patient outcomes and accelerate the integration of AI into precision cancer management.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

1. Wang, X., Jiang, Y., Yang, S., Wang, F., Zhang, X., Wang, W., Chen, Y., Wu, X., Xiang, J., Li, Y., Jiang, X., Yuan, W., Zhang, J., Yu, K., Ward, R., Hawkins, N., Jonnagaddala, J., Li, G., & Li, R. (2025). A foundation model for predicting prognosis and adjuvant therapy benefit from digital pathology in gastrointestinal cancers. Journal of Clinical Oncology, JCO-24-01501.

2. Jiang, Y., Zhang, Z., Wang, W., Huang, W., Chen, C., Xi, S., Ahmad, M. U., Ren, Y., Sang, S., Yuan, Q., Xu, Y., Xing, L., Poultsides, G. A., Li, G., & Li, R. (2023). Biology-guided deep learning predicts prognosis and cancer immunotherapy response. Nature Communications, 14, 5135.

3. Jiang, Y., Zhou, K., Sun, Z., Wang, H., Xie, J., Zhang, T., Sang, S., Islam, M. T., Wang, J.-Y., Chen, C., Yuan, Q., Xi, S., Li, T., Xu, Y., Xiong, W., Wang, W., Li, G., & Li, R. (2023). Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics. Cell Reports Medicine, 4, 101146.

4. Jiang, Y., Zhang, Z., Yuan, Q., Wang, W., Wang, H., Li, T., Huang, W., Xie, J., Chen, C., Sun, Z., Yu, J., Xu, Y., Poultsides, G. A., Xing, L., Zhou, Z., Li, G., & Li, R. (2022). Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer using multi-task deep learning: A retrospective study. The Lancet Digital Health, 4(5), e340–e350. )

5. Jiang, Y., Li, R., & Li, G. (2023). Artificial intelligence for clinical oncology: Current status and future outlook. Science Bulletin, (23), 00113–5. R. (2023). Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning. Radiotherapy and Oncology, 186, 109793.

7. Huang, W., Jiang, Y. (co-first), Xiong, W., Sun, Z., Chen, C., Yuan, Q., Zhou, K., Han, Z., Hu, Y., Yu, J., Zhou, Z., Wang, W., Xu, Y., & Li, G. (2022). Noninvasive imaging of the tumor immune microenvironment correlates with response to immunotherapy in gastric cancer. Nature Communications, 13, 5095.

8. Jiang, Y., Liang, X., Han, Z., Wang, W., Chen, C., Xu, Y., Zhou, Z., Poultsides, G. A., Li, G., & Li, R. (2021). Radiographical assessment of tumour stroma and treatment outcomes using deep learning: A retrospective, multicohort study. The Lancet Digital Health, 3(6), e371–e382.

9. Jiang, Y., Jin, C., Yu, H., Wu, J., Chen, C., Yuan, Q., Zhou, Z., Fisher, G. A. Jr., Li, G., & Li, R. (2021). Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: A multicenter, retrospective study. Annals of Surgery, 274(6), e1153–e1161.

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