Shaoyang Luo | Time Series Analysis | Research Excellence Award

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Dr. Shaoyang Luo | Time Series Analysis | Research Excellence Award

Doctor of Philosophy in Engineering | Nanchang University | China

Dr. Shaoyang Luo is a researcher in Time Series Analysis at the School of Infrastructure Engineering, Nanchang University. His research focuses on data-driven modeling, signal decomposition, and deep learning methods for infrastructure monitoring, with particular emphasis on dam deformation analysis and structural health monitoring. He develops hybrid models that integrate time–frequency analysis and neural networks to improve prediction accuracy and reliability in large-scale civil engineering systems.

Citation Metrics (Scopus)

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Citations
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Featured Publications

Peik Foong Yeap | Artificial Intelligence | Best Academic Researcher Award

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Dr. Peik Foong Yeap | Artificial Intelligence | Best Academic Researcher Award

Senior Lecturer at University of Newcastle | Singapore

Dr. Yeap Peik Foong is a distinguished academic and researcher whose career reflects a deep commitment to advancing knowledge in strategic management, organisational development, cross-cultural management, sustainability practices, and innovation within higher education and industry. Renowned for her interdisciplinary perspective, she has contributed extensively to scholarly literature through impactful journal articles, book chapters, and international conference presentations that explore themes such as digital transformation, human–AI collaboration, leadership effectiveness, consumer behaviour, knowledge management, environmental sustainability, and community-based tourism. Her work is recognized for its ability to merge theoretical frameworks with real-world applications, offering insights that guide policy development, organisational strategy, and educational leadership. She has played influential roles in shaping academic programs, strengthening research culture, and supporting curriculum innovation, while also contributing actively as a reviewer, editorial board member, and examiner for reputable journals, conferences, and institutions worldwide. Her research leadership is further demonstrated through her involvement in numerous funded projects that address emerging challenges in digital well-being, workplace resilience, global responsibility, cybersecurity, internationalisation of higher education, and interorganisational collaboration. Known for her mentorship and supervision of postgraduate candidates, she has supported research that spans management, marketing, organisational behaviour, and industry-specific strategic studies, helping shape future scholars and professionals. Her consistent engagement with global academic communities, coupled with her ability to foster collaborative networks, reflects her dedication to elevating research standards and promoting sustainable, innovative, and culturally aware practices across sectors. Dr. Yeap’s body of work positions her as a respected thought leader whose scholarly contributions and service continue to influence contemporary debates and future directions in management, education, and organisational sustainability.

Profile: Scopus

Featured Publications

Ha, H., Yeap, P. F., Loh, H. S., & Pidani, R. (2025). Environmental sustainability and CSR practices by banks in Indonesia, Malaysia, and Singapore.

Tan, K. L., Yeap, P. F., Cheong, K. C. K., & Shanu, R. (2025). Crafting an organizational strategy for the new era: A qualitative study of artificial intelligence transformation in a homegrown Singaporean hotel chain.

Tan, K.-L., Loganathan, S. R., Pidani, R. R., Yeap, P.-F., Ng, D. W. L., Chong, N. T. S., Liow, M. L. S., Cheong, K. C.-K., & Yeo, M. M. L. (2024). Embracing imperfections: A predictive analysis of factors alleviating adult leaders’ digital learning stress on Singapore’s lifelong learning journey.

Yeap, P. F., & Liow, M. L. S. (2023). Tourist walkability and sustainable community-based tourism: Conceptual framework and strategic model.

Ong, H. B., Chong, L. L., Choon, S. W., Tan, S. H., Yeap, P. F., & Kasuma, N. M. H. (2022). Retaining skilled workers through motivation: The Malaysian case.

Lee, Y. W., Dorasamy, M., Ahmad, A. A., Jambulingam, M., Yeap, P. F., & Harun, S. (2021). Synchronous online learning during movement control order in higher education institutions: A systematic review.

Mahendra Gaikwad | Machine Learning | Best Researcher Award

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Dr. Mahendra Gaikwad | Machine Learning | Best Researcher Award

Assistant Professor at Veermata Jijabai Technological Institute (VJTI) | Mumbai | India

Dr. Mahendra Uttam Gaikwad is a forward-thinking mechanical and manufacturing engineering professional whose work reflects a deep commitment to advancing modern machining, smart materials research, sustainable manufacturing, and AI-driven optimization in industrial systems. Renowned for his ability to bridge theoretical innovation with practical engineering applications, he has built a strong scholarly footprint through impactful publications in SCI and Scopus-indexed journals, contributions to influential book chapters, and editorial leadership in notable international volumes focused on advanced materials and digital-age manufacturing. His research explores critical themes such as electrical discharge machining, surface integrity analysis, optimization algorithms, additive manufacturing, fatigue modelling, and machine learning applications in production environments, consistently demonstrating an aptitude for tackling complex engineering challenges through empirical investigation and computational modelling. In addition to his academic contributions, he has shown commendable innovation through multiple national and international patents addressing smart systems, sustainable material utilization, and intelligent manufacturing solutions. He has also been an active collaborator with academic institutions, research groups, and industry partners, contributing to advancements in machining automation, performance benchmarking, and data-driven design methodologies. A dedicated mentor, he has guided numerous undergraduate and postgraduate research projects, fostering a research-oriented learning environment and supporting the next generation of engineers. His work as a reviewer, conference contributor, and knowledge disseminator further underscores his commitment to strengthening global engineering discourse. Known for his leadership qualities, professional integrity, and continuous pursuit of technological excellence, Dr. Gaikwad has earned recognition for his contributions to teaching and research, positioning himself as a noteworthy contributor to the evolving landscape of smart and sustainable manufacturing.

Profiles: ORCID | Google Scholar

Featured Publications

Gaikwad, M. U., Somatkar, A. A., Ghadge, M., Majumder, H., Shinde, A. M., & Lohakare, A. V. (2025). Effect of dry and wet machining environments on surface quality of Al6061 using particle swarm optimization (PSO).

Sargar, T., Gautam, N. K., Jadhav, A., & Gaikwad, M. U. (2025). A comparative investigation of kerf width during CO₂ and fiber laser machining of SS 316L material.

Khan, M. A. J., Pohekar, S. D., Bagade, P. M., Gaikwad, M. U., & Singh, M. (2025). CFD analysis of NACA 4415 marine propeller ducts for managing flow separation.

Nishandar, S. V., Pise, A. T., Bagade, P. M., Gaikwad, M. U., & Singh, A. (2025). Computational modelling and analysis of heat transfer enhancement in straight circular pipe with pulsating flow.

Gaikwad, M. U., Gaikwad, P. U., Ambhore, N., Sharma, A., & Bhosale, S. S. (2025). Powder bed additive manufacturing using machine learning algorithms for multidisciplinary applications: A review and outlook.

Sohong Dhar | Data Science | Analytics Excellence Award

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Dr. Sohong Dhar | Data Science | Analytics Excellence Award

Data Scientist at Jadavpur University | India

Dr. Sohong Dhar is a distinguished Information Scientist whose career bridges the fields of data science, digital marketing, and business analytics with remarkable proficiency. He is recognized for his ability to transform complex data into actionable insights that drive innovation, efficiency, and strategic growth across diverse industries. With expertise spanning machine learning, artificial intelligence, cloud computing, and advanced statistical analysis, he demonstrates an exceptional command of both theoretical and applied aspects of data-driven problem-solving. His multidisciplinary academic foundation, strengthened through advanced studies in data science and information science, has empowered him to approach challenges with analytical precision and creative foresight. Sohong has made impactful contributions to research, data modeling, and algorithmic development, delivering intelligent systems that enhance operational performance and decision-making processes. His fluency in multiple languages, combined with an understanding of literature and information systems, reflects a rare synthesis of technical acumen and intellectual versatility. He has collaborated effectively in cross-functional environments, employing platforms such as Microsoft Azure, SQL, and GCP to implement scalable and efficient data solutions. Beyond his technical mastery, Sohong’s work reflects a strong commitment to continuous learning, innovation, and excellence in the evolving domain of information and data science. His professional journey stands as a testament to the integration of analytical rigor, technological depth, and strategic thinking, establishing him as a forward-thinking expert dedicated to advancing the digital transformation landscape through intelligent, evidence-based insights and data-led decision frameworks.

Profile: Scopus

Featured Publications

Melba Kani, R., Karimli Maharram, V., Dhar, S., Samisha, B., Rajendran, P., & Ahmed, S. A. (2025). Automating grading to enhance student feedback and efficiency in higher education with a hybrid ensemble learning model.

Deepti, Nalluri, M., Mupparaju, C. B., Rongali, A. S., Dhar, S., & Ajitha, P. (2023). Retracted: Analyzing the impact of deep learning approaches on real-time data analysis in machine learning.

Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

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Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

Lecturer,  Global Banking School, United Kingdom

Mr. Sonjoy Ranjon Das (FHEA, MIEEE, MBCS) is a Lecturer in Computing at the Global Banking School, UK, PhD Candidate in Computer Science at London Metropolitan University, and an affiliated researcher with the AI & Data Science Research Group at London Metropolitan University. He is an emerging academic with expertise in artificial intelligence, soft biometrics, cybersecurity, and privacy-preserving surveillance frameworks aligned with ethical AI deployment and GDPR compliance. Mr. Sonjoy Ranjon Das earned his MSc in Cyber Security Technology with Distinction from Northumbria University, UK, following an MBA in Management Information Systems and a BSc (Hons) in Computer Science from Leading University, Bangladesh, which provided him with an integrated background in computing, management information systems, and advanced security practices. Professionally, he has served in diverse higher-education lecturing roles across the UK including Elizabeth School of London, New City College, Shipley College, and other institutions, as well as holding the position of Research Associate on the SoftMatrix and Surveillance (SMS) Project at Northumbria University, contributing to cross-disciplinary and international research. Mr. Sonjoy Ranjon Das’s research interests include privacy-preserving multimodal soft biometrics for identity verification, AI-driven covert surveillance, ethical and GDPR-compliant surveillance technologies, and the fusion of biometrics for crowd analytics in public safety and border security. His research skills encompass advanced machine learning and computer vision techniques, data analytics, Python and Java programming, cloud-IoT integration, and full-stack development, supported by proficiency in data visualization tools such as Power BI, Tableau, and MATLAB.

Profile :  GOOGLE SCHOLAR

Featured Publications

  • Das, S. R., Kruti, A., Devkota, R., & Sulaiman, R. B. (2023). Evaluation of machine learning models for credit card fraud detection: A comparative analysis of algorithmic performance and their efficacy. FMDB Transactions on Sustainable Technoprise Letters. 12 citations.

  • Thinesh, M. A., Varmann, S. S., Sharmila, S. L., & Das, S. R. (2023). Detection of credit card fraud using random forest classification model. FMDB Transactions on Sustainable Technologies Letters. 9 citations.

  • Pranav, R. P., Prawin, R. P., Subhashni, R., & Das, S. R. (2023). Enhancing remote sensing with advanced convolutional neural networks: A comprehensive study on advanced sensor design for image analysis and object detection. FMDB Transactions on Sustainable Computer Letters. 8 citations.

  • Das, S. R., Hassan, B., Patel, P., & Yasin, A. (2024). Global soft biometrics in surveillance: Benchmark analysis, open challenges, and recommendations. Multimedia Tools and Applications. 6 citations.

Ms. Wenqing Bao | Computer Science | Best Researcher Award

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Ms. Wenqing Bao | Computer Science | Best Researcher Award

Ms. Wenqing Bao | Computer Science | The Home Depot | United States

Ms. Wenqing Bao is a highly skilled Data Analyst and Quantitative Researcher with expertise in SQL, Python, predictive analytics, and machine learning. With a strong foundation in finance, e-commerce, and customer insights, she has consistently demonstrated her ability to transform complex datasets into actionable strategies that drive business growth and operational efficiency. She possesses a unique blend of technical proficiency and analytical problem-solving, enabling her to design predictive models, automate data pipelines, and develop intelligent dashboards. Throughout her professional journey, she has collaborated with cross-functional teams to optimize pricing strategies, improve customer retention, and streamline business operations, establishing herself as a result-driven data specialist committed to innovation and excellence.

Professional Profile

SCOPUS

GOOGLE SCHOLAR

Summary of Suitability

Ms. Wenqing Bao is a highly skilled Data Analyst and Quantitative Researcher with a strong academic background and practical expertise in data science, machine learning, predictive analytics, and financial modeling. With a Master’s in Analytical Finance – Data Science from Emory University (GPA 4.0/4.0) and a Bachelor’s in Mathematics & Finance from The Ohio State University, she has demonstrated an exceptional ability to combine theoretical knowledge with real-world applications.Her research-oriented projects, innovative data-driven solutions, and application of advanced analytical techniques position her as a highly suitable candidate for the Best Researcher Award.

Education

Ms. Wenqing Bao holds a Master of Science in Analytical Finance – Data Science from Emory University, Goizueta Business School, where she achieved a perfect GPA of 4.0/4.0. Her rigorous training in data-driven finance, portfolio modeling, and machine learning enabled her to build a strong foundation in financial analytics and quantitative techniques. She also earned a Bachelor of Science with a double major in Mathematics and Finance from The Ohio State University, where she developed critical problem-solving skills, statistical modeling expertise, and financial risk assessment capabilities. This multidisciplinary background has equipped her with a deep understanding of both technical data science methodologies and business-focused decision-making.

Experience

Ms. Wenqing Bao brings a diverse professional background across logistics, finance, and technology, demonstrating her adaptability and leadership in analytical roles. At Americold Logistics, she serves as a Business Analyst, where she develops automated SQL scripts to extract and analyze performance data, enabling strategic site and customer profitability decisions. She has designed and implemented Power BI dashboards for real-time insights, conducted annual pricing analyses, and collaborated on profitability models, reducing analysis time by 50% and improving operational workflows.Previously, at Invesco, she worked as a Quantitative Researcher, conducting web scraping, portfolio back-testing, and Monte Carlo simulations to enhance investment performance. She developed an LSTM-based price prediction model in Python, improving forecasting accuracy and optimizing portfolio returns.As a Product Data Analyst at HIWOO LLC, she built an ETL pipeline for multi-client data integration and visualization using Tableau, achieving a 12% improvement in customer retention and identifying opportunities that drove a 50% increase in service enrollments. At American Yuncheng Gravure Cylinder, she analyzed large datasets, created dashboards for tracking business KPIs, and contributed to $1M in cost savings through actionable insights.

Research Interests

Ms. Wenqing Bao research focuses on predictive modeling, financial risk analytics, and customer behavior analysis. She is passionate about developing machine learning models for credit risk prediction, portfolio optimization, and customer segmentation. Her academic and professional work explores applying AI-driven techniques to enhance decision-making in finance, logistics, and e-commerce. With growing expertise in time-series forecasting, neural networks, and natural language processing, she aims to bridge the gap between advanced data science methodologies and real-world business applications.

Awards

Ms. Wenqing Bao has been consistently recognized for her academic excellence, professional impact, and analytical contributions. Her achievements include outstanding academic performance, excellence in predictive modeling, and impactful contributions to data-driven decision-making. She has received recognition for developing advanced pricing models, implementing data automation pipelines, and creating innovative dashboards that enhanced business performance. Her work reflects a strong commitment to leveraging data science to deliver measurable outcomes and support organizational growth.

Publication Top Notes

Innovative application of artificial intelligence technology in bank credit risk management
Year: 2024
Citations: 26

Research on the application of data analysis in predicting financial risk
Year: 2024
Citations: 24

The challenges and opportunities of financial technology innovation to bank financing business and risk management
Year: 2024
Citations: 22

Customer-centric AI in banking: Using AIGC to improve personalized services
Year: 2024
Citations: 17

Application progress of natural language processing technology in financial research
Year: 2024
Citations: 17

Conclusion

Ms. Wenqing Bao is an accomplished data analyst and quantitative researcher whose expertise bridges the fields of data science, finance, and predictive analytics. Her career demonstrates a proven record of success in automating processes, optimizing decision-making, and delivering actionable insights that drive performance and growth. With a strong academic foundation, diverse professional experience, and impactful research contributions, she stands out as an innovative problem-solver dedicated to advancing data-driven strategies across industries. Her achievements reflect not only technical mastery but also a commitment to applying advanced analytics to create tangible business value, making her a highly deserving candidate for prestigious research and professional awards.

Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

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Dr. Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

Associate Professor at University of Guilan, Rasht, Iran

Dr. Hemad Zareiforoush is an Assistant Professor at the Department of Biosystems Engineering, University of Guilan, Rasht, Iran, where he has been contributing to both academic and practical advancements in biosystems engineering since 2015. With a focus on agricultural machinery, automation, and quality inspection systems, his work bridges engineering and food science, particularly in areas like computer vision, image processing, and renewable energy applications. His research is highly interdisciplinary, combining mechanical engineering principles with computational intelligence for improving the agricultural industry’s efficiency.

Profile

Google Scholar

Education

Dr. Zareiforoush’s educational background is robust, with a PhD in Mechanical and Biosystems Engineering from Tarbiat Modares University in Tehran, Iran, completed in 2014. His academic excellence is evident in his GPA of 17.84 out of 20. He earned his MSc in Mechanical Engineering of Agricultural Machinery at Urmia University in 2010, where he graduated with a remarkable GPA of 19.29 out of 20. Earlier, Dr. Zareiforoush obtained his BSc in the same field from Urmia University in 2007, graduating with a GPA of 15.75 out of 20. He also attended a specialized governmental high school for excellent pupils, where he focused on mathematics and physics, graduating with a GPA of 18.71 out of 20.

Experience

Since joining the University of Guilan in 2015, Dr. Zareiforoush has been teaching various courses, including Engineering Properties of Food and Agricultural Products, Renewable Energy, and Measurement and Instrumentation Principles. His practical experience spans various engineering disciplines, with a particular emphasis on instrumentation, automation in agriculture, and food quality monitoring. Notably, his research has led to the development of innovative systems for rice quality inspection using computer vision and fuzzy logic. Additionally, he has been involved in numerous projects related to agricultural machinery, renewable energy, and automation for optimizing food production processes.

Research Interests

Dr. Zareiforoush’s research interests lie at the intersection of biosystems engineering, computational intelligence, and food science. He is particularly interested in computer vision applications for food quality inspection, using advanced image processing techniques to enhance product quality and safety. His work also explores hyperspectral imaging and spectroscopy for monitoring the quality of food materials. Another key area of his research is the application of machine learning algorithms for modeling and classifying food products based on their quality attributes. Additionally, he is involved in renewable energy applications in agriculture, focusing on solar-assisted drying systems and energy-efficient food processing methods.

Awards

Dr. Zareiforoush has received several prestigious awards throughout his academic career. He was honored with the Iran Ministry of Science, Research, and Technology Scholarship in 2012 and the National Elite Scholarship by the Iran National Foundation for Elites (INFE) in 2011. His exceptional academic performance earned him the title of “Best Student” at Urmia University in 2009. Additionally, he has been recognized as a “Talented Student” at Tarbiat Modares University and ranked 1st among MSc students in his department.

Publications

Dr. Zareiforoush has published several influential papers in high-impact journals. Some of his notable publications include:

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features. Journal of Food Measurement and Characterization, 14: 1402–1416, Cited by: 43.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features. Plant Methods, 16:153, Cited by: 25.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2021). Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared-assisted continuous hybrid solar dryer. Food Science & Nutrition (JCR), 9(1), 532-543, Cited by: 12.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2016). Design, Development, and Performance Evaluation of an Automatic Control System for Rice Whitening Machine Based on Computer Vision and Fuzzy Logic. Computers and Electronics in Agriculture, 124: 14-22, Cited by: 67.

Soodmand-Moghaddam, S., Sharifi, M., Zareiforoush, H. (2020). Mathematical modeling of lemon verbena leaves drying in a continuous flow dryer equipped with a solar pre-heating system. Quality Assurance and Safety of Crops & Foods, 12(1): 57-66, Cited by: 30.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2015). Qualitative Classification of Milled Rice Grains Using Computer Vision and Metaheuristic Techniques. Journal of Food Science and Technology (Springer), 53(1): 118-131, Cited by: 45.

Zareiforoush, H., Komarizadeh, M.H., Alizadeh, M.R. (2010). Effects of crop-screw parameters on rough rice grain damage in handling with a horizontal screw auger. Journal of Food, Agriculture and Environment, 8(3): 132-138, Cited by: 19.

Conclusion

Dr. Hemad Zareiforoush’s academic and professional contributions significantly impact the fields of biosystems engineering, food science, and agricultural machinery. His work in developing intelligent systems for quality inspection and automation has improved agricultural productivity and food safety. His expertise in computational techniques, including fuzzy logic and machine learning, continues to shape the future of smart farming and food processing. With numerous awards, highly cited publications, and a track record of excellence, Dr. Zareiforoush is a leading figure in his field.

Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

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Assist. Prof. Dr. Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

Assistant Professor at Department of Electronic Engineering, Tsinghua University, China

Quanming Yao is a world-class researcher in the field of machine learning, holding the position of Assistant Professor in the Department of Electronic Engineering at Tsinghua University. With a strong academic background and extensive experience in deep learning, Yao’s research focuses on creating efficient and parsimonious solutions in machine learning, particularly in deep networks and graph learning. His work aims to enhance interpretability in AI models and has led to groundbreaking advancements, such as the development of EmerGNN, the first deep learning model that interprets drug-drug interaction predictions for new drugs. His contributions have significantly impacted both academia and industry, leading to the commercialization of his methods in the AI unicorn 4Paradigm.

Profile

Orcid

Education

Yao earned his Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology (HKUST) between 2013 and 2018. Prior to this, he completed his undergraduate studies at Huazhong University of Science and Technology, where he obtained a degree in Electronic and Information Engineering in 2013.

Experience

Before becoming an assistant professor at Tsinghua University in 2021, Yao worked as a researcher and senior scientist at 4Paradigm Inc. in Hong Kong, from June 2018 to May 2021. In his current academic role, he serves as a Ph.D. advisor, leading research in machine learning and AI, with a specific focus on making deep learning models more efficient and interpretable.

Research Interests

Yao’s research interests revolve around the concept of “parsimonious deep learning,” wherein he explores how simple solutions can lead to substantial improvements in machine learning models. His work is especially notable for its emphasis on automated graph learning methods, which has earned him first place in the Open Graph Benchmark, an equivalent to ImageNet in graph learning. He is also dedicated to the development of deep learning methods that provide interpretable results, particularly in domains like drug discovery, where his innovations have had a direct impact on creating a synthetic biology startup, Kongfoo Technology.

Awards

Yao’s exceptional contributions to the field of machine learning have earned him numerous prestigious awards. These include the Inaugural Intech Prize in 2024, the Aharon Katzir Young Investigator Award in 2023, Forbes 30 Under 30 in the Science & Healthcare Category (China) in 2020, and the Google Ph.D. Fellowship in 2016. He was also recognized as one of the World’s Top 2% Scientists in 2023, highlighting his influence in the global research community.

Publications

Yao has published over 100 papers in top-tier international journals and conferences, with a significant citation record (around 12,000 citations and an h-index of 36). His work includes several landmark papers, such as:

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network, Nature Computational Science, 2023.

AutoBLM: Bilinear Scoring Function Search for Knowledge Graph Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

Efficient Low-rank Tensor Learning with Nonconvex Regularization, Journal of Machine Learning Research (JMLR), 2022.

Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels, Advance in Neural Information Processing Systems (NeurIPS), 2018.

These papers showcase his innovative work in the areas of drug interaction prediction, knowledge graph learning, and robust training of deep neural networks, significantly impacting both theoretical and practical aspects of AI.

Conclusion

Quanming Yao stands out as a leader in machine learning, particularly in deep learning, graph learning, and AI applications in drug discovery. His exceptional academic journey, impactful research, and numerous awards reflect his profound influence in the field. Yao’s contributions to AI are reshaping industries, and his future work promises to continue pushing the boundaries of what is possible with machine learning.