Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

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Prof. Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

SUNY Upstate Medical University | United States

Prof. Bardia Rodd is an Associate Professor at SUNY Upstate Medical University and Associate Director of AI Innovation at the AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center. He completed dual PhDs in Electrical & Computer Engineering from Université Laval and in Computer Science from the University of Malaya, alongside a postdoctoral fellowship at the University of Pennsylvania Perelman School of Medicine, focusing on precision medicine, artificial intelligence, and image-guided systems. His academic career spans faculty positions at SUNY Upstate, the University of Maryland, and Laval University, with extensive experience in biocomputational engineering, machine learning, medical image analysis, and data-driven healthcare solutions. He has led numerous grants, including initiatives in AI in education, biomedical research, and curriculum development. Prof. Rodd has supervised multiple graduate and postgraduate students and contributed to open educational resources, advancing accessible teaching in machine learning and bioengineering. He is an active member of professional societies including IEEE, SPIE, and the Association of Pathology Informatics, serving on multiple technical committees and leadership roles. His research interests include AI-driven biomedical imaging, predictive modeling, and health equity applications. Prof. Rodd has authored a textbook on machine learning for data analysis and continues to integrate research, teaching, and innovation to advance computational medicine and AI education.

Profile : Scopus

Featured Publications

Siezen, H., Awasthi, N., Rad, M. S., Ma, L., & Rodd, B. (2025). “Low-rank distribution embedding of dynamic thermographic data for breast cancer detection.” Journal of Thermal Biology, 104303.

Usamentiaga, R., Fidanza, A., Yousefi, B., Iacutone, G., Logroscino, G., & Sfarra, S. (2025). “Advancing knee injury prevention and anomaly detection in rugby players through automated processing of infrared thermography: A novel biothermodynamics approach.” Thermal Science and Engineering Progress, 103782.

Rad, M. S., Huang, J. V., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R., et al. (2025). “Deep learning for digital pathology: A critical overview of methodological framework.” Journal of Pathology Informatics, 100514.

Yousefi, B., Khansari, M., Trask, R., Tallon, P., Carino, C., Afrasiyabi, A., Kundra, V., Ma, L., Ren, L., Farahani, K., & Hershman, M. (2025). “Measuring subtle HD data representation and multimodal imaging phenotype embedding for precision medicine.” IEEE Transactions on Instrumentation and Measurement, TIM-24-01821.

Cao, Y., Sutera, P., Mendes, W. S., Yousefi, B., Hrinivich, T., Deek, M., et al. (2024). “Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.” Radiotherapy and Oncology, 199, 110443.

Khalid Alanazi | Engineering | Research Excellence Award

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Dr. Khalid Alanazi | Engineering | Research Excellence Award

Department of Mechanical Engineering/University of Tabuk | Saudi Arabia

Dr. Khalid Alanazi is an Assistant Professor of Mechanical Engineering at the University of Tabuk, specializing in energy systems, fluid mechanics, and material erosion studies. He earned his B.Sc. in Mechanical Engineering with distinction from the University of Tabuk, followed by M.Sc. and Ph.D. degrees from the University of Tulsa, where he conducted extensive research on solid particle erosion, oil-water emulsions, and computational fluid dynamics. Over his academic career, Dr. Alanazi has contributed to both teaching and curriculum development, instructing courses ranging from Engineering Mechanics and Fluid Mechanics to Turbomachinery and Refrigeration Systems, while supervising senior design projects and field training programs. His research primarily focuses on solar-powered desalination technologies, oil-water emulsion behavior under variable conditions, and the erosion resistance of metals in gas-solid flows. Dr. Alanazi has published numerous papers in high-impact journals and presented at international conferences, reflecting his commitment to advancing both fundamental and applied mechanical engineering. Recognized for his academic excellence, he has received multiple awards including the Prince Fahad bin Sultan Award for Academic Excellence and the Chapman Graduate Scholar Presentation Awards. His work continues to bridge experimental, computational, and applied approaches, contributing to sustainable engineering solutions and industrial applications worldwide.

Profile : Google Scholar

Featured Publications

Alanazi, K., Mohan, R. S., Kolla, S. S., & Shoham, O. (2024). “Effects of water salinity and temperature on oil-in-water emulsions stabilized by a nonionic surfactant.” Journal of Molecular Liquids, 414, 126067.

Alanazi, K., Mohan, R., Kolla, S. S., & Shoham, O. (2023). “Experimental study of monovalent salt and hydrochloric acid solution effects on the stability of blank oil-water dispersion in batch separators.” ASME International Mechanical Engineering Congress and Exposition, 87660.

Alanazi, K., Mohan, R., Kolla, S. S., & Shoham, O. (2023). “Effect of water cut and temperature on the stability of emulsifier-free oil-water dispersion in batch separators at various stirrer speeds.” ASME International Mechanical Engineering Congress and Exposition, 87660.

AbdelMeguid, H., Alanazi, K., Al-Shammari, A. F., Al-Twaim, O., Aljuhani, A., et al. (2025). “Sequential thermal and optical upgrades for passive solar stills: Toward sustainable desalination in arid climates.” Clean Energy and Sustainability, 3(4), 10017.

Alanazi, K., Mohan, R., Kolla, S. S., & Shoham, O. (2025). “Stability analysis of pure oil-water emulsion in gravity-based separators.” Journal of Fluids Engineering, 147(1), 011401.

Xiaochen Xie | Quantitative Research | Research Excellence Award

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Dr. Xiaochen Xie | Quantitative Research | Research Excellence Award

Renmin University of China | China

Dr. Xiaochen Xie is an Assistant Professor at the School of Finance, Renmin University of China, with concurrent research associate positions at the China Financial Policy Research Center, the Institute of Public Finance and Taxation, and the Institute of Digital Economics and Taxation. He holds a Ph.D. in Economics from Pennsylvania State University, an M.S. in Economics from the University of Wisconsin–Madison, and dual B.E. in Finance and B.S. in Statistics from Peking University. His research spans international trade, industrial organization, public economics, and urban economics, with a focus on firm entry, pricing strategies, and the economic impacts of policy interventions. Dr. Xie has published in leading journals, including the Journal of Economic Geography, and has multiple working papers under review in top-tier journals. He has received awards such as Outstanding Young Scholar at Renmin University and recognition for outstanding papers at the NCER-CCER Chinese Economy Conference. Dr. Xie has extensive teaching experience in undergraduate and graduate courses on public finance, quantitative empirical economics, and structural methods. His research integrates empirical and structural approaches to inform trade, fiscal, and industrial policies. Dr. Xie’s work contributes to understanding the intersection of market structure, policy design, and economic welfare, influencing both academic research and practical policy-making.

Profiles : Orcid | Google Scholar

Featured Publications

Chen, Y., Huang, T., & Xie, X. (2025). “Place-based policies: First-mover advantage and persistence.” Journal of Economic Geography, lbaf001.

Lu, W., & Xie, X. (2024). “Trade, markups, and consumer welfare: Evidence from the global smartphone industry.” Available at SSRN 4804811.

Xie, X. (2021). Two essays on firm entry and pricing. The Pennsylvania State University.

Weitao Yue | Engineering | Research Excellence Award

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Mr. Weitao Yue | Engineering | Research Excellence Award

China University of Mining and Technology | China

Mr. Weitao Yue is a Ph.D. candidate at the China University of Mining and Technology, specializing in Safety Science and Engineering with a focus on coal and rock dynamic disaster prevention and control. His research is dedicated to addressing critical challenges in coal mine safety, emphasizing the investigation of underlying mechanisms, monitoring and early warning systems, and the development of innovative control technologies for coal and rock dynamic disasters. He has actively contributed to multiple national-level major research projects, including the National Natural Science Foundation of China (NSFC) projects, National Key Research and Development Program projects, and National Major Scientific Instrument Development projects, where he played pivotal roles in theoretical innovation, experimental research, and data analysis. Mr. Yue has published seven high-impact SCI papers as first or corresponding author in top-tier journals such as International Journal of Mining Science and Technology, Engineering Geology, Rock Mechanics and Rock Engineering, Measurement, Journal of Central South University, and Physics of Fluids. Notably, two of his papers have been recognized as ESI Highly Cited, ranking among the top 1% most cited globally, reflecting his significant academic influence. His research provides theoretical foundations and technical solutions to enhance coal mine safety, contributing substantially to the advancement of mining engineering and disaster prevention technologies.

Profile : Orcid

Featured Publications

Feng, X., Yue, W., Cao, Z., Zhou, S., Cao, X., & Wang, E. (2026). “Multi-scale DEM simulation and experimental validation of water-saturated coal failure under variable-angle shear loading: Revealing hydrochemical coupling damage mechanisms.” Measurement. https://doi.org/10.1016/j.measurement.2025.119812

Feng, X., Yue, W., Zhao, X., Wang, D., Liu, Q., & Ding, Z. (2025). “Evolution law and risk analysis of fault-slip burst in coal mine based on microseismic monitoring.” Environmental Earth Sciences. https://doi.org/10.1007/s12665-024-12080-5

Vaishali Sharma | Neuroscience Data Analysis | Best Researcher Award

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Dr. Vaishali Sharma | Neuroscience Data Analysis | Best Researcher Award

Post Graduate Institue of Medical Education and Research (PGIMER) | India

Dr. Vaishali Sharma is a Senior Research Fellow at the Department of Neurology, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, specializing in neurobiology, neuroinflammation, and sleep disorders. She earned her Ph.D. in Neurology (2020–2025) from PGIMER, focusing on the role of aquaporins, heat shock proteins, and neuroinflammatory markers in idiopathic intracranial hypertension (IIH) and their correlation with clinical parameters. She also holds a master’s degree in Zoology and a bachelor’s in Medical Science. Her research encompasses molecular biomarkers, blood-brain barrier disruption, circadian rhythm disturbances, and the evaluation of sleep and headache disorders, combining clinical, translational, and systematic review approaches. Dr. Sharma has contributed to multiple peer-reviewed publications and conference presentations, investigating psychiatric and sleep abnormalities, migraine care, and IIH pathophysiology. She has been recognized with several awards and fellowships, including CSIR and ICMR travel grants, best poster awards, and national and international research fellowships. Her professional activities extend to academic leadership and organizing conferences in neuro-ophthalmology and neurology. Dr. Sharma’s work advances the understanding of neuroinflammatory mechanisms, sleep disorders, and headache pathophysiology, providing insights for improved diagnosis, treatment, and quality of life for patients with neurological disorders.

Profile : Google Scholar

Featured Publications

Choudhary, N., Kumar, A., Sharma, V., Kaur, K., Kharbanda, P. S., Baishya, J., et al. (2024). “Effectiveness of CBT for reducing depression and anxiety in people with epilepsy: A systematic review and meta-analysis of randomized controlled trials.” Epilepsy & Behavior, 151, 109608.

Sharma, V., Chakravarty, K., Ray, S., Lal, V., & Takkar, A., Kharbanda, P. (2021). “Evaluation of prevalence and severity of obstructive sleep apnea using overnight-polysomnography in patients with idiopathic intracranial hypertension.” Journal of the Neurological Sciences, 429.

Sharma, V., Chakravarty, K., & Ray, S. (2023). “Questionnaire-based evaluation of sleep abnormality in patients with primary headache disorder: A cross-sectional study from tertiary care centre.” Journal of the Neurological Sciences, 455.

Sharma, S. R. V., & Chakravarty, K. (2023). “Questionnaire-based assessment of sleep abnormalities in patients with migraine: A cross-sectional study with a comparison group.” Annals of Indian Academy of Neurology, 26, S186.

Sharma, A. T. K., Chakravarty, K., Lal, V., & Kharbanda, P. S., et al. (2022). “Sleep and headache characteristics in patients with idiopathic intracranial hypertension (IIH) – An observational study.” Annals of Indian Academy of Neurology, 25, S296–S297.

Arun Kumar | Mathematical Modeling | Best Researcher Award

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Dr. Arun Kumar | Mathematical Modeling | Best Researcher Award

IIT Mandi | India

Dr. Arun Kumar is a Research Associate (Postdoctoral) at the Indian Institute of Technology Mandi, specializing in mathematical and computational modeling of complex biological, ecological, and epidemiological systems. He earned his Ph.D. in Mathematics from Banaras Hindu University in 2023, following an M.Sc. in Mathematics from the same institution and a B.Sc. from CCS University, Meerut. His research focuses on nonlinear dynamics, bifurcation theory, delay differential equations, reaction–diffusion systems, Turing patterns, and the integration of deep learning and physics-informed neural networks (PINNs) for solving partial differential equations. Dr. Kumar has published extensively in high-impact journals on topics including SIR/SIRS epidemic models, cross-diffusion models, predator–prey dynamics, and pattern formation in spatial ecological systems. His work bridges theoretical mathematics and practical applications in disease modeling and ecology, offering insights into complex population interactions, control strategies, and spatio-temporal dynamics. Currently, he is developing deep learning algorithms to solve PDEs with applications in ecological and epidemiological systems. His ongoing research explores predator-prey interactions, learning in ecological models, and the forecasting of infectious diseases such as monkeypox. Dr. Kumar’s contributions have advanced the understanding of nonlinear systems, providing both analytical and computational tools for studying complex biological and ecological phenomena.

Profile : Google Scholar

Featured Publications

Gupta, R. P., & Kumar, A. (2022). “Endemic bubble and multiple cusps generated by saturated treatment of an SIR model through Hopf and Bogdanov–Takens bifurcations.” Mathematics and Computers in Simulation, 197, 1–21.

Gupta, R. P., Kumar, A., & Yadav, D. K. (2024). “The complex dynamical study of a UAI epidemic model in non-spatial and spatial environments.” The European Physical Journal Plus, 139(2), 117.

Yadav, D. K., Gupta, R. P., & Kumar, A. (2022). “Nonlinear dynamics of a three species prey-predator system incorporating fear effect and harvesting.” Journal of Mathematical Control Science and Applications.

Kumar, A., Gupta, R. P., & Tiwari, S. (2022). “Influences of nonlinear cross-diffusion on a reduced SI epidemic model with saturated treatment.”

Kumar, A., Kumari, N., Mandal, S., & Tiwari, P. K. (2025). “Autonomous and non-autonomous dynamics of an SIRS model with convex incidence rate.” Journal of the Franklin Institute, 108236.

Wei Dai | Quantitative Research | Research Excellence Award

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Dr. Wei Dai | Quantitative Research | Research Excellence Award

Sichuan Cancer Hospital | China

Dr. Wei Dai is an accomplished thoracic surgeon and researcher specializing in minimally invasive surgery for lung cancer and the integration of patient-reported outcomes (PROs) into perioperative care. He earned his MD in Clinical Medicine from Southern Medical University, an MSc in Thoracic Surgery from Peking Union Medical College, a PhD in Thoracic Surgery from Sichuan University, and completed a fellowship in Thoracic Surgery at the University Hospital of Copenhagen, Rigshospitalet. Dr. Dai has progressed through roles from thoracic surgery trainee and resident to attending surgeon, and currently serves as an associate consultant at Sichuan Cancer Hospital. His research focuses on the development and implementation of PRO-based databases, optimization of postoperative symptom management, and enhancement of patient rehabilitation and recovery pathways. Dr. Dai has led multiple national and provincial research grants, including multicenter randomized controlled trials, and has been instrumental in establishing REDCap-based medical research platforms. He has authored over 60 peer-reviewed publications in high-impact journals and serves as a reviewer for several international journals. An active member of leading professional societies, including ESTS, IASLC, and ISOQOL, Dr. Dai is dedicated to advancing precision perioperative care and improving the quality of life for lung cancer patients. His work bridges clinical innovation, research excellence, and patient-centered care.

Profile : Orcid

Featured Publications

Liu, J., Ma, H., Li, D., Li, Y., Qing, H., Wei, X., Shi, Q., Li, Q., Dai, W., & Zhou, P. (2025). “ASO visual abstract: Early post-discharge pain trajectories after thoracoscopic sublobar resection for Stage IA non-small cell lung cancer.” Annals of Surgical Oncology.

Liu, J., Ma, H., Li, D., Li, Y., Qing, H., Wei, X., Shi, Q., Li, Q., Dai, W., & Zhou, P. (2025). “Early post-discharge pain trajectories after thoracoscopic sublobar resection for Stage IA non-small cell lung cancer.” Annals of Surgical Oncology.

Tian, X., Mao, P., Lei, C., Yu, H., Dai, W., Wei, X., Zhang, J., Xu, W., & Shi, Q. (2025). “Patient-reported walking difficulty predicting the post-discharge overall function in patients with lung cancer undergoing minimally invasive surgery.” Journal of Cancer Survivorship.

Liao, J., Hu, X., Wei, X., Dai, W., Yu, H., Tian, X., Wang, Y., Qin, Q., Xu, N., Li, Y., et al. (2025). “Sex-related differences in postoperative patient-reported outcomes among lung cancer patients: A multicenter cohort study.” BMC Cancer.

Zheng, H., Guo, C., Zhang, Y., Gu, H., Zhao, Y., Xiang, R., Dai, W., Wei, X., Xie, T., Li, Q., et al. (2025). “Single staged repair of an anastomotic tracheal fistula following Mckeown esophagectomy via cervical incision: A case report.” Journal of Cardiothoracic Surgery.

Xinyu Liu | Regression Analysis | Young Innovator Award

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Dr. Xinyu Liu | Regression Analysis | Young Innovator Award

Peking University | China

Dr. Xinyu Liu is a postdoctoral researcher specializing in Microeconomics, Development Economics, and Finance, with a particular focus on e-commerce, consumption behavior, entrepreneurship, housing demolition, and cross-shareholding in China. Dr. Liu earned a Ph.D. in Management (Agricultural Economics and Management) from Peking University, with a dissertation examining the impact of e-commerce on rural residents’ sales and consumption, and a B.E. in Economics from Southwestern University of Finance and Economics. Currently, Dr. Liu is a postdoctoral fellow at the China International Capital Corporation (CICC) Global Institute and Fudan University, exploring cross-shareholding and firm innovation under the guidance of senior economists. Dr. Liu also served as a short-term consultant at the World Bank, analyzing fertility preferences. With five publications, including in Economic Development and Cultural Change, Cities, and PNAS Nexus, Dr. Liu’s work has been cited 30 times across 30 documents, achieving an h-index of 2. Recognized for academic excellence, Dr. Liu has received multiple awards, including the Peking University Second-Class Scholarship and Best Paper Awards at international conferences. Current research explores digital transformation in agriculture, rural e-commerce expansion, and the economic consequences of housing demolition. Dr. Liu’s research contributes to sustainable and inclusive economic development in China and beyond.

Profile : Scopus

Featured Publications

Wang, X., Ren, Y., Yamauchi, F., Huang, J., & [Author]. (2025). “The power of the anti-corruption campaign: Evidence from cigarette and alcohol consumptions in China.” Economic Development and Cultural Change, 73(2), 941–977.

He, Z., Shi, X., Sun, X., & [Author]. (2024). “Housing demolition and entrepreneurship: Evidence from China.” Cities, 152, 105201.

Wang, X., Min, S., Jin, S., Huang, J., & [Author]. (2025). “The influence of improved wheat and maize varieties on infant mortality in China.” PNAS Nexus, 4(2), pgaf048.

Huang, J., Su, L., Huang, Q., & [Author]. (2022). “Facilitating inclusive ICT application and e-commerce development in rural China.” Agricultural Economics, 53(6), 938–952.

Yu Xiao | Data Science | Research Excellence Award

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Dr. Yu Xiao | Data Science | Research Excellence Award

Beijing Normal University | China

Yu Xiao He is a lecturer at Beijing Normal University with a strong background in decision science and operations research. He received his B.S. degree in Communication Engineering from Sichuan University, Chengdu, China, in 2014, and earned his Ph.D. in Management Science from the National University of Defense Technology, China, in 2020. He worked at the National University of Defense Technology from 2020 to 2022 before joining Beijing Normal University. His research focuses on multi-criteria decision analysis (MCDA) and rank aggregation, as well as graph theory and voting theory, addressing challenges in ranking, consensus building, and decision support. To date, he has published 24 documents, which have collectively received 158 citations, resulting in an h-index of 8, reflecting his growing influence in his fields of expertise. His contributions combine rigorous theoretical development with practical applications, aiming to improve methods for aggregating rankings and supporting complex decision-making processes. Recognized for his interdisciplinary approach, he integrates engineering, management, and social-choice methodologies in his work. Overall, Yu Xiao He is a rising scholar whose research is advancing both the theory and practice of multi-criteria decision-making, ranking aggregation, and graph-based modeling, offering valuable insights for academics and practitioners alike.

Profiles : Scopus | Orcid

Featured Publications

Xiao, Y., Wang, B., Deng, Y., & Wu, J. (2025). “From ratings to rankings: A complementary approach for student evaluations of teaching in higher education.” Studies in Educational Evaluation.

Zhu, H., Xiao, Y., Chen, D., & Wu, J. (2025). “A robust rating aggregation method based on temporal coupled bipartite network.” Information Processing & Management.

Li, G., Xiao, Y., & Wu, J. (2024). “Rank aggregation with limited information based on link prediction.” Information Processing & Management.

Xiao, Y., Zhu, H., Chen, D., Deng, Y., & Wu, J. (2023). “Measuring robustness in rank aggregation based on the error-effectiveness curve.” Information Processing & Management.

Xiao, Y., Deng, H.-Z., Lu, X., & Wu, J. (2021). “Graph-based rank aggregation method for high-dimensional and partial rankings.” Journal of the Operational Research Society.

Lan Xie | COVID-19 Analysis | Research Excellence Award

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Assoc. Prof. Dr. Lan Xie | COVID-19 Analysis | Research Excellence Award

Tsinghua University | China

Assoc. Prof. Dr. Lan Xie is a leading researcher in drug discovery and traditional Chinese medicine (TCM) integration with modern biomedical technologies. She earned her M.D. in Clinical Medicine from Peking University and a Ph.D. in Biology from Tsinghua University. Dr. Xie completed postdoctoral training at Tsinghua University and a visiting scholar program at the University of Pittsburgh. She has held faculty positions at Tsinghua University since 2014, currently serving as Associate Professor at the School of Basic Medical Sciences. Her research focuses on combining TCM with systems biology, multi-omics, and artificial intelligence to develop novel therapeutic strategies. She pioneered the Molecular Materia Medica database and an intelligent TCM formulation algorithm leveraging gene expression profiles and dysregulated disease pathways. Her work has been recognized with numerous awards, including the Huaxia Medical Science and Technology Award, International Award for Contribution to Chinese Medicine, and multiple Tsinghua University teaching excellence awards. Dr. Xie’s research achievements encompass high-throughput TCM bioactive component screening, single-cell omics-based mechanistic studies, and pathway-guided intelligent drug formulation. With 1,716 citations by 47 documents, 47 publications, and an h-index of 24, her contributions continue to advance modern medicine by bridging traditional knowledge and cutting-edge technology.

Profile : Scopus

Featured Publications

“A universal gene expression signature-based strategy for the high-throughput discovery of anti-inflammatory drugs.” Inflammation Research. 2025.

“Identification of the fruit of Brucea javanica as an anti-liver fibrosis agent working via SMAD2/SMAD3 and JAK1/STAT3 signaling pathways.” Journal of Pharmaceutical Analysis. 2025.

“Pulsatilla chinensis functions as a novel antihyperlipidemic agent by upregulating LDLR in an ERK-dependent manner.” Chinese Medicine United Kingdom. 2024.

“Mechanism study of Xiaoyao San against nonalcoholic steatohepatitis-related liver fibrosis based on a combined strategy of transcriptome analysis and network pharmacology.” Pharmaceuticals. 2024.