Ean Teng Khor | Learning Analytics | Best Researcher Award

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Dr. Ean Teng Khor | Learning Analytics | Best Researcher Award

Faculty at Nanyang Technological University | Singapore

Dr. Ean Teng Khor is a distinguished scholar in the fields of learning analytics, educational technology, and data-driven approaches to teaching and learning, recognized for advancing innovative methodologies that enhance how learners interact with digital environments. With a strong academic foundation in information technology, she has developed an interdisciplinary research portfolio centered on machine learning, data mining, social network analysis, and artificial intelligence applications in education. Her work consistently bridges theoretical insight with real-world educational challenges, exploring how data can meaningfully inform instructional decision-making, personalize learning pathways, and support predictive models that improve student performance. She has contributed extensively to international journals through influential publications that examine emerging trends such as multimodal learning analytics, generative AI feedback, intelligent educational chatbots, and networked learning in professional settings. Her scholarship also includes systematic reviews that synthesize global research on personalized learning, MOOCs, workplace learning behaviors, and K-12 teacher experiences with data literacy. Known for her ability to articulate complex analytical approaches in ways that are accessible to educators and policymakers, she plays a significant role in shaping conversations around the responsible and impactful use of AI in education. Her collaborations with multidisciplinary teams reflect her commitment to enhancing both technological design and pedagogical practice, ensuring that learning technologies are grounded in evidence-based strategies that support diverse learners. Through her leadership in research, teaching, and academic community engagement, she continues to influence the development of more intelligent, equitable, and data-empowered learning ecosystems, earning recognition as an important contributor to the future of technology-enhanced education.

Profiles: Scopus | ORCID

Featured Publications

Khor, E. T., Chee, A., & Lee, S. S. (2025). Beyond the numbers: K–12 teachers’ experiences, beliefs, and challenges in developing data literacy.

Khor, E. T., Chan, L., Koh, E., & Seow, P. (2025). Exploring students’ perceptions of generative AI-generated feedback in learning programming.

Khor, E. T., How, Z. J., Koh, E., Looi, C. K., & Lok, C. (2025). Learning analytics techniques: An overview and future research possibilities.

Ani, A., & Khor, E. T. (2024). Development and evaluation of predictive models for predicting students’ performance in MOOCs.

Khor, E. T., & Darshan, D. (2024). Prediction of students’ performance in online learning using supervised machine learning.

Nikolaos Gkrekas | Mathematics | Best Researcher Award

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Mr. Nikolaos Gkrekas | Mathematics | Best Researcher Award

Researcher at University of Kansas | United States

Mr. Nikolaos Gkrekas is a mathematician whose research and academic contributions bridge the domains of dynamical systems, mathematical modeling, and applied analysis. His scholarly work demonstrates an interdisciplinary approach, uniting mathematical theory with practical applications in science, engineering, and education. He has authored several peer-reviewed papers in internationally recognized journals, addressing complex phenomena such as chaos, quasi-geostrophic equations, and epidemiological models, while also exploring the evolving role of artificial intelligence in mathematics education and research. His research reveals a consistent focus on nonlinear dynamics, mathematical modeling, and the interplay between theoretical structures and real-world systems. In addition to his research, he has participated in numerous international conferences, seminars, and workshops hosted by institutions such as Harvard University, Kyoto University, and the University of Essex, reflecting his active engagement with the global mathematics community. His involvement as a peer reviewer for top-ranked journals, including Chaos, Solitons & Fractals and Nonlinear Engineering, alongside his editorial role in applied mathematics publications, underscores his academic credibility and contribution to maintaining high standards in scientific communication. Nikolaos is also affiliated with prominent mathematical societies and research groups, emphasizing his dedication to advancing mathematical sciences and fostering collaborative research. His intellectual versatility, combined with his passion for analytical problem-solving, positions him as an emerging figure in modern mathematical research, recognized for integrating rigorous mathematical theory with insightful applications to complex systems and education.

Profile: Google Scholar

Featured Publications

Rizos, I., & Gkrekas, N. (2023). Incorporating history of mathematics in open-ended problem solving: An empirical study.

Rizos, I., & Gkrekas, N. (2022). Teaching and learning sciences within the COVID-19 pandemic era in a Greek university department.

Rizos, I., & Gkrekas, N. (2023). Is there room for conjectures in mathematics? The role of dynamic geometry environments.

Gkrekas, N. (2024). Applying Laplace transformation on epidemiological models as Caputo derivatives.

Rizos, I., & Gkrekas, N. (2022). The historical background of a famous indeterminate problem and some teaching perspectives.

Abraham Marquez | Power Electronics | Best Researcher Award

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Dr. Abraham Marquez | Power Electronics | Best Researcher Award

Researcher at Universidad de Sevilla | Spain

Dr. Abraham Márquez Alcaide is a distinguished researcher in electronic engineering, renowned for his pioneering contributions to power electronics, advanced modulation strategies, and predictive control systems. Currently based at the University of Seville, he has played a vital role in the TIC-109 research group, advancing modular power converter technologies with a focus on improving system reliability, efficiency, and lifetime through smart thermal and predictive maintenance control. His prolific academic output includes over ninety peer-reviewed publications in leading high-impact journals and international conferences, several of which are recognized as highly cited by the Web of Science. Dr. Márquez has collaborated globally with eminent scholars and research institutions, including the Harbin Institute of Technology in China and Universidad Técnica Federico Santa María in Chile, fostering innovation in renewable energy integration, electric vehicle charging systems, and industrial automation. A multiple recipient of the IEEE Industrial Electronics Best Paper Award, he is widely respected for his ability to bridge theoretical advancements and industrial applications. Beyond research, he is actively engaged in academic leadership, mentoring numerous postgraduate students, organizing international conference sessions, and contributing to editorial and peer-review processes in reputed journals. His expertise spans modulation techniques, model predictive control, and active thermal management in high-reliability power electronic systems. With his visionary approach and international recognition, Dr. Márquez stands out as a leading figure shaping the future of smart, efficient, and sustainable power conversion technologies.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

Vazquez, S., Leon, J. I., Franquelo, L. G., Rodriguez, J., Young, H. A., & Marquez, A., et al. (2014). Model predictive control: A review of its applications in power electronics.

Liu, J., Vazquez, S., Wu, L., Marquez, A., Gao, H., & Franquelo, L. G. (2016). Extended state observer-based sliding-mode control for three-phase power converters.

Vazquez, S., Marquez, A., Aguilera, R., Quevedo, D., Leon, J. I., & Franquelo, L. G. (2014). Predictive optimal switching sequence direct power control for grid-connected power converters.

Liu, J., Shen, X., Alcaide, A. M., Yin, Y., Leon, J. I., Vazquez, S., Wu, L., et al. (2021). Sliding mode control of grid-connected neutral-point-clamped converters via high-gain observer.

Zhang, J., Tian, J., Alcaide, A. M., Leon, J. I., Vazquez, S., Franquelo, L. G., Luo, H., et al. (2023). Lifetime extension approach based on the Levenberg–Marquardt neural network and power routing of DC–DC converters.

Rahul Wankhede | A/B Testing | Rising Star in Data Science Award

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Mr. Rahul Wankhede | A/B Testing | Rising Star in Data Science Award

Director at Humana | United States

Mr. Rahul Wankhede is a distinguished data science leader and marketing analytics innovator with over a decade of proven excellence in transforming enterprise decision-making through data-driven intelligence. Renowned for architecting large-scale analytics frameworks and AI-driven marketing solutions across Fortune 50 companies, he has pioneered unified measurement systems integrating media mix modeling, causal inference, and machine learning for end-to-end marketing optimization. His leadership has driven measurable business outcomes, including multi-million-dollar ROI enhancements, customer engagement uplift, and enterprise adoption of AI-led strategies. As Director of Data Science at Humana, Rahul has built high-performing analytics teams that bridge business strategy with advanced data science, enabling unified performance measurement across marketing, customer, and healthcare domains. His earlier tenure at Walmart saw him revolutionize marketing science through the OMEGA measurement platform and drive cross-channel innovation in membership, loyalty, and advertising analytics. Recognized through prestigious honors such as the ANA Genius Award, Humana Excellence Award, and Invoca Rookie of the Year, Rahul exemplifies thought leadership and strategic foresight in the evolving intersection of AI, marketing, and customer intelligence. A member of the Forbes Technology Council and IEEE Senior Member, he actively contributes to shaping the global data science community through advisory roles and industry judging panels. His expertise spans marketing measurement, personalization, AI product development, and enterprise data strategy—anchored by a strong vision to empower organizations to make smarter, data-informed decisions that inspire growth and innovation.

Profile: ORCID

Featured Publications

Wankhede, R. (2026). Proven product design strategies cybersecurity teams should adopt. Forbes.

Wankhede, R. (2025). Post-quantum cryptography solutions for business leaders. Forbes.

Wankhede, R. (2025). On-prem data centers still matter — here’s why. Forbes.

Wankhede, R. (2025). How to build robust AI for regulated, high-impact sectors. Forbes.

Wankhede, R. (2025). Digital marketing ROI: From clicks to causality. Forbes.

Nanfu Zong | AI & Materials | Best Researcher Award

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Dr. Nanfu Zong | AI & Materials | Best Researcher Award

Leader of Digital Intelligence Research Institute at Ben Gang Group Corporation | China

Dr. Nanfu Zong is a visionary leader and pioneering researcher recognized for his transformative work at the intersection of artificial intelligence and the iron and steel industry. As the Director of the Digital Intelligence Research Institute at Ben Gang Group Corporation and a senior engineer of distinction, he has driven the intelligent, high-end, and green evolution of steel production through advanced AI integration. His expertise spans digital modeling, intelligent control, and sustainable process optimization, bridging theoretical innovation with industrial practice. With a robust research portfolio encompassing over forty SCI-indexed journal publications, sixteen major projects, and fifteen patents, Dr. Zong has significantly advanced digital manufacturing intelligence and industrial innovation. His leadership has inspired collaborations with global research powerhouses such as the University of Leicester, Tsinghua University, and major steel enterprises, reinforcing his role as a key figure in the digital transformation of metallurgical engineering. An active member of professional societies and editorial boards, he contributes to shaping the future of intelligent manufacturing through thought leadership and scientific rigor. His research excellence has earned him numerous accolades, including top provincial awards for scientific achievement, underscoring his impact on both academia and industry. Through his strategic vision and pioneering spirit, Dr. Zong continues to redefine how artificial intelligence can revolutionize traditional industries, promoting efficiency, sustainability, and innovation within the global steel sector.

Profile: Scopus

Featured Publications

Zong, N. F., Jing, T., & Gebelin, J. (2025). Intelligent empowerment for green steel manufacturing: Artificial intelligence‐driven process optimization.

Zong, N. F., Jing, T., & Gebelin, J. (2025). Machine learning for tandem cold rolling: Exploring innovations, challenges, and industrial applications.

Zong, N. F., Jing, T., & Gebelin, J. (2025). Machine learning techniques for the comprehensive analysis of the continuous casting processes: Slab defects.

Quanmin Zhu | Data Driven Decision Making | Best Researcher Award

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Prof. Quanmin Zhu | Data Driven Decision Making | Best Researcher Award

Distinguished Professor at University of the West of England | United Kingdom

Professor Quanmin Zhu is a distinguished academic and leading authority in control systems engineering, currently serving at the School of Engineering, University of the West of England, Bristol. With a career grounded in rigorous research and scholarly excellence, he has significantly advanced the fields of complex system modelling, identification, and control through both theoretical innovation and practical application. His prolific contributions include the authorship of over 300 peer-reviewed publications and the editorial oversight of major works with prestigious publishers such as Springer and Elsevier. A Chartered Engineer and Fellow of the Institution of Engineering and Technology (FIET) as well as the Higher Education Academy (FHEA), Professor Zhu is widely recognized for his commitment to bridging the gap between academic research and industrial practice. His expertise has been instrumental in shaping methodologies that enhance system performance, reliability, and adaptability across diverse engineering domains. As Editor of Elsevier’s Emerging Methodologies and Applications in Modelling, Identification and Control series, he continues to influence emerging directions in modern control theory and intelligent systems. Professor Zhu’s academic leadership, combined with his dedication to mentorship and collaboration, underscores his enduring impact on the global engineering community and his role in fostering innovation at the intersection of computation, automation, and control science.

Profile: Google Scholar

Featured Publications

Azar, A. T., & Zhu, Q. (2015). Advances and applications in sliding mode control systems.

Zhu, Q., & Azar, A. T. (2015). Complex system modelling and control through intelligent soft computations.

Li, S., Zhang, Y., & Zhu, Q. (2005). Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem.

Billings, S. A., & Zhu, Q. M. (1994). Nonlinear model validation using correlation tests.

Chen, J., Zhu, Q., & Liu, Y. (2020). Modified Kalman filtering based multi-step-length gradient iterative algorithm for ARX models with random missing outputs.

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.

Nilay Kushawaha | Continual Learning for Robotics | Best Researcher Award

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Mr. Nilay Kushawaha | Continual Learning for Robotics | Best Researcher Award

PhD Scholar at Scuola Superiore Sant’Anna | Italy

Mr. Nilay Kushawaha is an innovative researcher in Artificial Intelligence and Robotics, specializing in continual learning, multimodal data fusion, and adaptive control for soft robotic systems. As a doctoral candidate at the Biorobotics Institute, Scuola Superiore Sant’Anna, his work bridges advanced AI modeling with experimental robotics, creating intelligent machines capable of learning and adapting in real time. His contributions reflect a deep understanding of neural computation, reinforcement learning, and data-driven control, with research outcomes published in leading journals such as IEEE Transactions on Neural Networks and Learning Systems and Advanced Robotics Research. Nilay’s approach combines theoretical insight with practical implementation, evident in his development of algorithms like SynapNet and AGPNN, which enhance robot perception and continual learning efficiency. His interdisciplinary expertise spans physics, machine learning, and robotic design, refined through global collaborations, including research at the National University of Singapore and Jefferson Lab in the USA. Recognized for academic excellence through multiple international scholarships and awards, Nilay also contributes to academic outreach by creating tutorials and coordinating robotics initiatives. His technical fluency in Python, C++, and ROS, along with proficiency in deep learning frameworks, complements his passion for intelligent system design. Dedicated to pushing the boundaries of bioinspired robotics, Nilay’s vision centers on developing autonomous systems capable of adaptive, human-like learning and perception. His research continues to contribute significantly to the advancement of continual learning in robotics, marking him as a promising scholar and innovator in intelligent autonomous systems.

Profile: ORCID

Featured Publications

Kushawaha, N., Fruzetti, L., Donato, E., & Falotico, E. (2024). SynapNet: A complementary learning system inspired algorithm with real-time application in multimodal perception.

Kushawaha, N., & Falotico, E. (2025). Continual learning for multimodal data fusion of a soft gripper.

Kushawaha, N., Perovic, G., Donato, E., & Falotico, E. (n.d.). AGPNN: A dynamic architecture-based continual reinforcement learning algorithm for robotic control.

Kushawaha, N., Nazeer, S., Laschi, C., & Falotico, E. (n.d.). SMPL: A continual learning approach for dynamic modeling of modular soft robots.

Kushawaha, N., Pathan, R., Pagliarani, N., Cianchetti, M., & Falotico, E. (2025). Adaptive drift compensation for soft sensorized finger using continual learning.

Kushawaha, N., Alessi, C., Fruzetti, L., & Falotico, E. (2025). Domain translation of a soft robotic arm using conditional cycle generative adversarial network.

Zhongqiang Zhang | Mechanical Engineering | Best Researcher Award

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Prof. Dr. Zhongqiang Zhang | Mechanical Engineering | Best Researcher Award

Dean at Jiangsu University | China

Professor Zhongqiang Zhang is a distinguished scholar in mechanical engineering whose research bridges fundamental mechanics and advanced nanotechnology. He has made significant contributions to the understanding of solid–fluid interfacial phenomena, including boundary slip, interfacial friction, and their implications for the design of next-generation micro/nano fluidic systems. As a faculty member and academic leader at Jiangsu University, his work has advanced the frontiers of nanofluidics, soft robotics, and smart material design through innovative integration of theory, computation, and experiment. Professor Zhang’s pioneering studies on graphene-based membranes, droplet dynamics, and energy-efficient fluid systems have been published in leading international journals such as Science Advances, Chemical Engineering Journal, and ACS Applied Materials & Interfaces. His research has introduced novel mechanisms for unidirectional droplet transport and enhanced bubble collection, providing new pathways for desalination, energy harvesting, and biomedical applications. Recognized for his excellence and originality, he has received multiple prestigious honors, including the ICCES Outstanding Young Researcher Award and the Jiangsu Provincial Science and Technology Awards. His projects funded by the National Natural Science Foundation of China underscore his leadership in advancing electro-mechanical coupling and interface-driven transport in microstructured materials. Professor Zhang’s work embodies the synergy of mechanics, materials, and nanotechnology, continually inspiring innovation in sustainable energy systems and intelligent fluidic devices. His scholarly achievements reflect a sustained commitment to interdisciplinary research excellence, making him a leading figure shaping the global discourse in advanced mechanical and fluid systems engineering.

Profile: Google Scholar

Featured Publications

Cheng, G. G., Jiang, S. Y., Li, K., Zhang, Z. Q., Wang, Y., Yuan, N. Y., Ding, J. N., et al. (2017). Effect of argon plasma treatment on the output performance of triboelectric nanogenerator.

Zheng, Y., Ye, H., Zhang, Z., & Zhang, H. (2012). Water diffusion inside carbon nanotubes: Mutual effects of surface and confinement.

Zhang, H., Ye, H., Zheng, Y., & Zhang, Z. (2011). Prediction of the viscosity of water confined in carbon nanotubes.

Zhang, H., Zhang, Z., & Ye, H. (2012). Molecular dynamics-based prediction of boundary slip of fluids in nanochannels.

Zhang, Z., Li, S., Mi, B., Wang, J., & Ding, J. (2020). Surface slip on rotating graphene membrane enables the temporal selectivity that breaks the permeability–selectivity trade-off.

Vinay Chaudhri | Knowledge Engineering | Best Researcher Award

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Dr. Vinay Chaudhri | Knowledge Engineering | Best Researcher Award

Principal Scientist at Knowledge Systems Research LLC | United States

Dr. Vinay K. Chaudhri is a distinguished technology executive and research scientist renowned for his pioneering contributions in artificial intelligence, knowledge representation, and automated reasoning. His work bridges the gap between deep research and impactful real-world applications across domains such as education, finance, and law. As a thought leader in knowledge graphs, semantic reasoning, and large language models, Dr. Chaudhri has led groundbreaking projects that demonstrate the transformative potential of AI in enhancing learning outcomes, improving financial intelligence, and enabling computational understanding of complex legal structures. His tenure at organizations including SRI International, Stanford University, JPMorgan Chase, and Knowledge Systems Research has been marked by visionary leadership and innovation. At SRI, he played a key role in landmark AI initiatives like Project Halo and CALO—the latter evolving into Apple’s Siri—demonstrating his ability to architect systems that advance both academic inquiry and commercial technology. At Stanford, his efforts in intelligent education tools such as the HaloBook and Intelligent Textbook showcased AI’s capacity to personalize and elevate learning experiences, while his leadership in the LogicForAll initiative expanded logic education nationwide. In the finance sector, his work integrating NLP and graph-based AI systems has delivered multi-million-dollar impacts through enhanced analytics, compliance, and decision intelligence. A prolific researcher with numerous publications and awards, Dr. Chaudhri continues to shape the future of AI with a vision grounded in rigorous science, ethical innovation, and societal benefit, making him a respected figure in the global AI research and technology community.

Profile: Google Scholar

Featured Publications

Chaudhri, V. K., Farquhar, A., Fikes, R., Karp, P. D., & Rice, J. P. (1998). OKBC: A programmatic foundation for knowledge base interoperability.

Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education.

Burger, J., Cardie, C., Chaudhri, V., Gaizauskas, R., Harabagiu, S., Israel, D., … & Weischedel, R. (2001). Issues, tasks and program structures to roadmap research in question & answering (Q&A).

Karp, P. D., Chaudhri, V. K., & Thomere, J. (1999, July). XOL: An XML-based ontology exchange language.

Chaudhri, V. K., Farquhar, A., Fikes, R., Karp, P. D., & Rice, J. P. (1998). Open knowledge base connectivity 2.0.