Tongcun Liu | Graph Learning and Recommender System | Best Researcher Award

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Assoc. Prof. Dr. Tongcun Liu | Graph Learning and Recommender System | Best Researcher Award

Zhejiang A&F University | China 

Dr. Tongcun Liu is an Associate Professor at Zhejiang A & F University, specializing in computer science and technology with a strong focus on big data analytics and artificial intelligence. He earned his Ph.D. from the Beijing University of Posts and Telecommunications and later enhanced his academic experience as a Visiting Scholar at the Hong Kong University of Science and Technology. His research primarily revolves around advanced algorithms for graph computing, recommendation systems, and AI4Science, contributing significantly to the intersection of data intelligence and computational innovation. Dr. Liu leads multiple research projects funded by the National Natural Science Foundation of China and the Zhejiang Provincial Natural Science Foundation. His current and completed projects include the development of data-driven models for estimating mangrove soil dissolved organic carbon sequestration potential and the creation of cloud-edge collaborative recommendation systems based on session flow methods. With a robust publication record of more than 30 papers in esteemed international journals and conferences, his scholarly work has had a substantial impact on the field of artificial intelligence and data-driven computing. In addition to his academic achievements, Dr. Liu holds over 10 granted patents from more than 20 applications, reflecting his strong commitment to technological innovation and the advancement of AI-based computational methodologies.

Profile : Google Scholar

Featured Publications 

Feng, H., Qiu, J., Wen, L., Zhang, J., Yang, J., Lyu, Z., Liu, T., & Fang, K. (2025). U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision. Neural Networks, 185, 107207.

Fang, K., Deng, J., Dong, C., Naseem, U., Liu, T., Feng, H., & Wang, W. (2025). MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network. Proceedings of the ACM on Web Conference 2025, 5065–5074.

Liu, T., Yu, G., Kwok, H. Y., Xue, R., He, D., & Liang, W. (2025). Enhancing tree-based machine learning for chlorophyll-a prediction in coastal seawater through spatiotemporal feature integration. Marine Environmental Research, 107170.

Shi, Q., Wang, Y., Liu, T., Zhang, L., & Liao, J. (2024). STRL: Writer-Independent Offline Signature Verification with Transformers and Self-Supervised Representation Learning. 2024 10th International Conference on Computer and Communications (ICCC).

Liu, T., Bao, X., Zhang, J., Fang, K., & Feng, H. (n.d.). Enhancing session-based recommendation with multi-interest hyperbolic representation networks. IEEE Transactions on Neural Networks and Learning Systems.

Assist. Prof. Dr. Hyun-Seok Kim – Computer Science – Best Researcher Award 

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Assist. Prof. Dr. Hyun-Seok Kim - Computer Science - Best Researcher Award 

Dept. of Information and Security at ICT Polytech Institute of Korea - South Korea

Author Profile 

GOOGLE SCHOLAR 

🎓 Early academic pursuits

Hyun-Seok Kim embarked on his academic journey with a bachelor’s degree in business management from the korea military academy in 2000. his growing passion for computing and security led him to pursue a master’s in computer science from korea university, completed in 2006. his master’s thesis, which analyzed security protocols using model checking, laid the groundwork for his further exploration into cryptographic systems. in 2009, he earned his ph.d. in computer science from the same institution, with a dissertation titled “cryptographic model and formal design for distributed network system.” during his formative academic years, he established a deep foundation in formal methods, secure system design, and the intersection of computing with national security.

💼 Professional endeavors

dr. kim has accumulated over two decades of professional experience in information security and auditing. from june 2012 to january 2023, he served as an it senior auditor and deputy director at the board of audit and inspection of the republic of korea. in this role, he led several critical national audits covering information protection, cyber safety, and system operations. his notable reports, including reviews of foreign ministry operations and financial sector cyber safety, contributed significantly to national cyber governance. since august 2023, he has taken on a leadership role as the head of the department of information security at ict polytechnic university in gwangju, gyeonggi-do, where he continues to drive research and curriculum advancement, integrating cross-disciplinary insights including areas like power electronics and secure hardware protocols.

🧠 Contributions and research focus

Dr. Kim’s core research revolves around security modeling, formal verification, and secure system architecture. his academic efforts have consistently bridged theory with practical application, focusing on critical areas such as wireless networks, iot, banking systems, and cloud infrastructure. through his rigorous verification techniques and cryptographic models, he has developed Computer Science robust frameworks to identify and mitigate system vulnerabilities. he also delves into security aspects of embedded systems, where topics like power electronics and secure circuit design are becoming increasingly relevant, especially within smart grids and industrial control systems.

🌍 Impact and influence

Hyun-Seok Kim’s work has played a pivotal role in shaping south korea’s public sector it governance. his detailed audit reports have influenced cyber safety protocols and security legislation at national and international levels. beyond policy influence, he has mentored students and professionals, promoting awareness of formal verification, ethical hacking, and critical system audits. Computer Science by integrating insights from adjacent fields such as power electronics, he encourages a broader, systems-level understanding of security in cyber-physical environments.

📚 Academic cites and achievements

Dr. Kim’s academic and governmental publications are frequently cited in the realms of computer security, auditing, and formal verification. he has co-authored at least 14 major audit reports, many of which have been used as reference documents for policy development in ministries like foreign affairs and finance. his patented technologies and cryptographic frameworks are also Computer Science gaining recognition in academic and industrial circles.

🧭 Legacy and future contributions

As he leads the department of information security at ict polytechnic university, dr. kim is focused on cultivating the next generation of cybersecurity experts. he aims to expand interdisciplinary collaboration, particularly in emerging areas like secure autonomous systems, where integration with power electronics is crucial. his vision includes creating resilient security models for future smart cities and industrial automation frameworks. with a legacy rooted in national service, academic excellence, and innovation, he is committed to advancing secure and trustworthy digital infrastructures.

Notable Publications 

  • Title: Bio-2FA-IoD: A Biometric-Enhanced Two-Factor Authentication Protocol for Secure Internet of Drones Operations
    Author(s): H.K.S. Park
    Journal: Mathematics (SCIE), Vol. 13, Issue 13, Article 2177, 2025

  • Title: Efficient and Scalable Authentication Framework for Internet of Drones (IoD) Networks
    Author(s): H. Kim
    Journal: Electronics (SCIE), Vol. 14, Issue 12, Article 2435, 2025

  • Title: Formal Analysis of E-Payment System based on 3-D Secure in Social Payment Environment
    Author(s): H. Kim
    Journal: IEEE SNAMS, 2024

  • Title: Enhancement of user authentication using biometrics in mobile networks
    Author(s): H.S. Kim
    Journal: ICCE-TW, 2024

  • Title: Security Enhancement of Biometric-Based Authentication Systems Using Smart Card
    Author(s): H. Kim
    Journal: IEEE ACCESS (SCIE), Vol. 12, Pages 174053–174065

Dr. Mohsin Masood – Data Scientist – Best Researcher Award 

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Dr. Mohsin Masood - Data Scientist - Best Researcher Award 

Imperial College London - United Kingdom

Author Profile 

GOOGLE SCHOLAR 

🎓 Early academic pursuits

Mohsin Masood began his academic journey with a strong foundation in computer science, securing a merit-based msc funding grant from the university of east london (2011–2012). his early academic achievements laid the groundwork for advanced training in health data science, culminating in a phd in statistical modelling from the university of strathclyde, funded by a competitive grant (2014–2017). his scholarly curiosity extended internationally through the erasmus exchange research grant at vsb-technical university of ostrava (2015–2018), where he deepened his expertise in predictive modelling and bioinformatics. during this phase, his interest in power electronics and algorithmic optimization in medical diagnostics emerged as an interdisciplinary focus.

💼 Professional endeavors

With over a decade of experience, mohsin has held impactful roles across academia and research institutions. as an assistant professor in computer science at abasyn university (2018–2022), he designed and taught advanced courses in ai, ml, and data science, incorporating real-world applications in epidemiology and digital health. later, as a senior research fellow at the university of leeds (2022–2023), he led data engineering for a large-scale 2.9 million patient cohort, employing advanced machine learning and nlp tools to analyze multimorbidity and Data Scientist cardiovascular risks. currently, he serves as a research associate at imperial college london (2023–present), where he leads predictive modelling projects on population health, integrating datasets like uk biobank, sail databank, and triphic. he also fosters collaborations with institutions such as sheffield university and a* university of singapore.

🧠 Contributions and research focus

Mohsin’s core research focus lies in multimorbidity, disease progression modelling, and epidemiological risk assessment, particularly within cardiovascular and pulmonary health Data Scientist domains. he applies deep learning, survival analysis, and multistate modelling to interpret complex patient trajectories and disease clustering. his work with electronic health records (ehrs) like hospital episode statistics (hes) reflects a commitment to improving precision medicine. integrating technologies from power electronics into patient monitoring systems has been a unique dimension of his research, promoting real-time diagnostics and health alerts. he has also contributed to educational leadership by mentoring bsc and msc students in ai-based health science projects.

🌍 Impact and influence

Mohsin’s influence extends beyond academia through his involvement in cross-institutional research, digital outreach, and public health strategy. at imperial’s national heart and lung institute (nhli), he manages social media communications to boost public engagement with medical data science. his high-impact publications have contributed to the growing intersection of Data Scientist computational modelling and health outcomes, often cited in cardiovascular and cancer research communities. he is recognized for bridging the gap between statistical modelling and power electronics, showing how smart analytics can optimize hardware for healthcare delivery.

📚 Academic cites

His academic influence is reflected in high citation counts across journals focusing on biostatistics, machine learning in medicine, and epidemiology. notable contributions include projects funded by imperial nhli’s pilot project award (2023–2024) and royan pharmaceutical’s breast cancer research grant (2020–2021). his statistical toolkits and predictive algorithms are widely referenced in studies involving survival analysis, deep learning applications in ehrs, and risk prediction modelling.

🧬 Legacy and future contributions

Mohsin’s legacy lies in fostering a data-driven healthcare ecosystem where interdisciplinary tools empower clinicians and researchers alike. he aims to develop scalable, explainable ai systems for public health, enabling real-time decision-making in hospitals and remote settings. leveraging power electronics, he envisions the integration of wearable technologies with ai for early disease detection and patient monitoring. his commitment to mentoring the next generation of health data scientists ensures a lasting academic and societal impact.

Notable Publications 

  • Title: Emotion classification and crowd source sensing; a lexicon based approach
    Authors: R. Kamal, M.A. Shah, C. Maple, M. Masood, A. Wahid, A. Mehmood
    Journal: IEEE Access

  • Title: An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks
    Authors: M. Masood, M.M. Fouad, R. Kamal, I. Glesk, I.U. Khan
    Journal: IEEE Access

  • Title: Energy efficient software defined networking algorithm for wireless sensor networks
    Authors: M. Masood, M.M. Fouad, S. Seyedzadeh, I. Glesk
    Journal: Transportation Research Procedia

  • Title: Proposing bat inspired heuristic algorithm for the optimization of GMPLS networks
    Authors: M. Masood, M.M. Fouad, I. Glesk
    Journal: 2017 25th Telecommunication Forum (TELFOR)

  • Title: Analysis of artificial intelligence-based metaheuristic algorithm for MPLS network optimization
    Authors: M. Masood, M.M. Fouad, I. Glesk
    Journal: 2018 20th International Conference on Transparent Optical Networks (ICTON)

Dr. Agnieszka Niemczynowicz – Machine Learning – Best Researcher Award 

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Dr. Agnieszka Niemczynowicz - Machine Learning - Best Researcher Award 

Cracow University of technology - Poland

AUTHOR PROFILE 

ORCID 

EARLY ACADEMIC PURSUITS 🎓

Agnieszka Niemczynowicz began her academic journey in the field of solid-state physics, earning her Ph.D. from the Faculty of Physics and Applied Informatics at the University of Łódź, Poland, in 2014. Her early research laid a strong foundation in the fundamental aspects of physics, equipping her with a deep understanding of physical systems and analytical techniques.

PROFESSIONAL ENDEAVORS 🏢

Upon completing her doctorate, Agnieszka transitioned into academia, taking up the role of Associate Professor at the Cracow University of Technology. She has since been instrumental in bridging the gap between physics and computational sciences, expanding her research horizons to include computational and mathematical methods for analyzing complex data sets across various disciplines.

CONTRIBUTIONS AND RESEARCH FOCUS 🔍

Agnieszka’s research is at the forefront of computational analysis, focusing on multivariate statistics, chemometrics, and deep learning. She has developed advanced statistical and machine Machine Learning learning models that have found applications in diverse fields such as engineering, biology, medicine, and management. Her work is characterized by its interdisciplinary approach, integrating complex data analysis methods into practical applications.

ACCREDITATIONS AND RECOGNITION 🏅

A prolific researcher, Agnieszka has authored around 50 publications in international journals, contributing significantly to her field. Her excellence in research was recognized with the Machine Learning prestigious Doak Award in 2022, highlighting her impactful contributions to the scientific community and her role as a thought leader in computational analysis.

IMPACT AND INFLUENCE 🌍

Agnieszka’s work has had a significant impact on how complex analytical data is interpreted and utilized across various sectors. Her models have improved the accuracy of data-driven Machine Learning decisions in numerous applications, thereby enhancing the efficiency and effectiveness of processes in engineering, biology, medicine, and more.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Currently leading international research grants, Agnieszka investigates the mathematical foundations of hypercomplex neural networks and their applications. Her ongoing work promises to further unravel the complexities of data analysis, pushing the boundaries of what machine learning and computational methods can achieve. Her legacy lies in her pioneering efforts to integrate advanced mathematical models into practical solutions, ensuring that her influence will be felt across multiple disciplines for years to come.

NOTABLE PUBLICATIONS 

  • Title: A critical analysis of the theoretical framework of the Extreme Learning Machine
    Authors: Irina Perfilieva, Nicolás Madrid, Manuel Ojeda-Aciego, Piotr Artiemjew, Agnieszka Niemczynowicz
    Journal: Neurocomputing
  • Title: Use of physicochemical, FTIR and chemometric analysis for quality assessment of selected monofloral honeys
    Authors: Monika Kędzierska-Matysek, Anna Teter, Mariusz Florek, Arkadiusz Matwijczuk, Agnieszka Niemczynowicz, Alicja Matwijczuk, Grzegorz Czernel, Piotr Skałecki, Bożena Gładyszewska
    Journal: Journal of Apicultural Research
  • Title: Conclusions
    Authors: Joanna Nieżurawska, Radosław Antoni Kycia, Agnieszka Niemczynowicz
    Journal: (Book chapter, not a journal)
  • Title: Current research methods in mathematical and computer modelling of motivation management
    Authors: Agnieszka Niemczynowicz, Radosław Antoni Kycia
    Journal: (Book chapter, not a journal)
  • Title: Introduction
    Authors: Joanna Nieżurawska, Radosław Antoni Kycia, Agnieszka Niemczynowicz
    Journal: (Book chapter, not a journal)