Francesco Gullo | Graph Data Structures | Graph Analytics Achievement Recognition 

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Dr. Francesco Gullo | Graph Data Structures | Graph Analytics Achievement Recognition 

University of L'Aquila | Italy

Dr. Francesco Gullo is an Associate Professor of Computer Science at the University of L’Aquila (DISIM), Italy, where he specializes in artificial intelligence and data science with a strong focus on algorithmic foundations. He earned his PhD in Computer and Systems Engineering from the University of Calabria in 2010 and previously held research and industry positions at the University of Calabria, the University of Catanzaro, George Mason University (USA), Yahoo Labs and Fundació Barcelona Media (Spain), and the UniCredit banking group (Italy). His recent research interests span graph machine learning, graph data management, and trustworthy AI, and he has authored over 100 publications in leading international venues. Prof. Gullo has also made significant contributions to the scientific community, serving as Associate Editor for EPJ Data Science and IJDSA, General Co-Chair of WSDM 2026, Finance Chair of CIKM 2024, ADS Track PC Co-Chair of ECML-PKDD 2026, Workshop Co-Chair of KDD 2024 and ICDM 2016, and Industry Track PC Co-Chair of ASONAM 2024, in addition to organizing numerous workshops and serving regularly on senior program committees of top-tier conferences.

Profiles: Scopus | Orcid | Google Scholar

"Polarized Communities meet Densest Subgraph: Efficient and Effective Polarization Detection in Signed Networks", F Gullo, D Mandaglio, A Tagarelli, ACM Transactions on Knowledge Discovery from Data, 2025.

"Cyber Attack Protection via Temporal Online Graph Representation Learning", B Lakha, J Layne, E Serra, F Gullo, S Jajodia, IEEE Transactions on Big Data, 2025.

"Holistic dry resist optimization on bright field EUV contact hole patterning", CC Huang, F Gullo, M Brouri, A De Silva, A Lushington, N Kenane, International Conference on Extreme Ultraviolet Lithography, 2025.

"Segmentation of temporal graphs", R Giancotti, F Gullo, PH Guzzi, E Serra, P, Veltri Information Sciences, 2025

"Discovering Balance-Aware Polarized Communities in Signed Networks with Graph Neural Networks", F Gullo, D Mandaglio, A Tagarelli, 2025.

Stef Van Den Elzen | Network Visualization | Research Excellence Award

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Dr. Stef Van Den Elzen | Network Visualization | Research Excellence Award

Eindhoven University of Technology | Netherlands

Dr. Stef Van Den Elzen is an accomplished expert in visual analytics, artificial intelligence, and advanced data visualization, currently serving as an Assistant Professor in Visual Analytics at Eindhoven University of Technology since 2021. Prior to this, he worked as a Senior Scientist and Product Owner at Philips Research, where he led an agile team of over 15 members and drove the integration of state-of-the-art AI technologies into impactful innovations. His industry leadership includes serving as VP of Engineering at SyneRScope, guiding strategic direction, technical roadmaps, and product development, following earlier roles as Visualization Architect and Software Engineer in which he developed pioneering visual analytics techniques and supported international research projects. Dr. Van Den Elzen began his career with roles in software visualization, interactive data analysis, and educational support, and he earned his PhD in Visual Analytics for dynamic networks from Eindhoven University of Technology, graduating cum laude. He also holds a cum laude Master’s degree in Computer Science and Engineering with a specialization in Visualization, a minor in Game and Multimedia Technology from Utrecht University, and a Bachelor’s degree in Computer Science, building a strong foundation that has enabled his innovative contributions to research, industry, and technology development.

Profiles: Scopus | Orcid | Google Scholar

"Towards multi-faceted visual process analytics", S van den Elzen, M Jans, N Martin, F Pieters, C Tominski, MC Villa-Uriol, Information Systems, 2025.

"When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities", FV Paulovich, A Arleo, S van den Elzen Computer Graphics Forum, 2025.

"Long sequences with a lot of events (LoLo): A visual analytics approach for analyzing long event sequences", S van der Linden, B Cappers, A Vilanova, S van den Elzen Information Visualization, 2025.

"Cluster-Based Random Forest Visualization and Interpretation", M Sondag, C Meinecke, D Collaris, T Von Landesberger, S Elzen, arXiv preprint arXiv:2507.22665, 2025.

"Automated Refined Comic Generation: From Investigation Provenance to Data Comics using Visual Narrative Structure", K Roggenbuck, A Vilanova, S van den Elzen, 2025 Eurographics Conference on Visualization, 2025.

Kexue Sun | Graph Data Structures | Best Researcher Award 

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Prof. Kexue Sun | Graph Data Structures | Best Researcher Award 

Nanjing University of Posts and Communications | China

Prof. Kexue Sun is a distinguished Professor at the School of Electronic and Optical Engineering and the School of Flexible Electronics (Future Technologies), Nanjing University of Posts and Telecommunications (NJUPT). He earned his Ph.D. in Acoustics from the School of Physics, Nanjing University (2012–2018), an M.E. in Software Engineering from the Beijing University of Posts and Telecommunications (2004–2006), and a B.E. in Electronic Information Engineering from the Artillery Academy of the Chinese People's Liberation Army, Hefei (1998–2002). Prof. Sun has served NJUPT in various academic roles, including Lecturer (2007–2013), Associate Professor (2013–2020), and currently as Professor since 2020, with international experience as a Visiting Scholar at the Chinese University of Hong Kong (2018–2019). He is an active member of IEEE, a technical expert for high-tech enterprises in Jiangsu Province, and a review expert for the Degree and Graduate Education Development Center of the Ministry of Education, while also serving on the Specialized Committee on Biomedical Information Detection and Processing of the Jiangsu Society of Biomedical Engineering. His research spans Electronic Technology, FPGA Applications, Electrical and Electronic Experiments, Optoelectronic Information Materials, and Acoustic Devices. Prof. Sun has participated in over ten national and enterprise research projects, co-authored one monograph and seven textbooks, published more than 100 academic papers, and holds over 20 authorized Chinese invention patents. Additionally, he has made significant contributions to higher education research and teaching reform, leading more than ten national and provincial projects, publishing over 20 papers in this field, and earning prestigious honors such as the Teaching Model Award, the First Prize for Teaching Achievements at NJUPT, and the Special Prize for Teaching Achievements of Jiangsu Province.

Profiles: Orcid ID

Featured Publications

"Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution"

"Heart Sound Classification Network Based on Convolution and Transformer"

"Optimization of Indoor Luminaire Layout for General Lighting Scheme Using Improved Particle Swarm Optimization"

Yinyan Liu | Network Visualization | Best Researcher Award

Published on by Network Science

Dr. Yinyan Liu | Network Visualization | Best Researcher Award

The University of Sydney | Australia

Author Profiles

Scopus

Orcid ID

Google Scholar

Early Academic Pursuits

Dr. Yinyan Liu demonstrated exceptional academic aptitude from the beginning of her educational journey. She earned a Bachelor of Engineering in Measuring & Controlling Technology and Instrument from North China Electric Power University, receiving multiple scholarships for outstanding performance. She continued her studies with a Master’s in Control Engineering at Tsinghua University, where she ranked among the top 2% of students and was awarded the National Scholarship by China’s Ministry of Education. Dr. Liu further advanced her expertise by completing a Ph.D. in Electrical & Information Engineering at the University of Sydney, supported by the University of Sydney International Scholarship and a Top-up Scholarship, reflecting her strong dedication to research and innovation in engineering.

Professional Endeavors

Following her doctoral studies, Dr. Liu embarked on a distinguished career bridging academia and industry. She has served as a Lecturer at the University of Sydney, overseeing teaching, course coordination, and student supervision, while conducting cutting-edge research on energy transition and sustainability. Her prior roles include a Postdoctoral Research Associate at UNSW, algorithmic engineering in industry settings, and control engineering for power plants, reflecting a rare combination of academic rigor and practical problem-solving expertise. She has also contributed as a course developer and consultant for renewable energy companies, demonstrating her ability to translate research into actionable solutions.

Contributions and Research Focus

Dr. Liu’s research primarily focuses on integrated energy systems, distributed renewable energy, and data-driven technologies for sustainability. Her work spans the development of 100% renewable energy systems, optimization algorithms for energy management, fault detection in photovoltaic systems, and innovative business models for shared energy storage. She has also advanced methods in home energy management systems, peak price forecasting, and non-intrusive load monitoring using statistical, machine learning, and deep learning techniques. Her research blends mathematical modeling, algorithmic optimization, and empirical analysis, positioning her at the forefront of sustainable energy innovation.

Impact and Influence

Dr. Liu’s research has had significant influence both academically and industrially. She has successfully collaborated with industrial partners and universities, translating complex energy challenges into practical solutions that enhance decarbonization and energy efficiency. By co-supervising students, securing grants, and contributing to interdisciplinary courses, she has fostered knowledge transfer and skill development for the next generation of energy researchers. Her work on renewable energy integration and smart energy management has potential to inform policy, improve energy infrastructure, and support sustainable societal development globally.

Academic Citations

While specific citation metrics are not provided in the available data, Dr. Liu’s consistent contributions to high-impact projects and publications in renewable energy, energy optimization, and smart grid technologies reflect a strong academic footprint. Her research in energy system modeling, fault diagnosis, and non-intrusive load monitoring positions her as a recognized contributor in both applied and theoretical domains of electrical and renewable energy engineering.

Legacy and Future Contributions

Dr. Liu has established a legacy of combining rigorous academic research with practical, real-world applications in sustainable energy. Her future work promises to further advance renewable energy integration, enhance intelligent energy management systems, and promote decentralized, data-driven solutions to global energy challenges. Her ongoing mentorship of students and collaboration with industry partners ensures that her impact will continue to grow, inspiring future researchers and practitioners in the field.

Conclusion

Dr. Yinyan Liu exemplifies a researcher whose academic excellence, professional expertise, and innovative contributions converge to advance sustainable energy systems. Her blend of theoretical rigor, practical problem-solving, and leadership in collaborative research positions her as a leading figure in her field, with a legacy that promises enduring influence on both academia and industry.

Notable Publications

“A methodological review of cost-effective data-driven fault detection and diagnosis in distributed photovoltaic systems" 

  • Author: Yinyan Liu; Earl Duran; Anna Bruce; Baran Yildiz; Bernardo Mendonca Severiano; Ibrahim Anwar Ibrahim; Jonathan Rispler; Chris Martell; Fiacre Rougieux
  • Journal: Applied Energy
  • Year: 2025

"Economic feasibility and backup capabilities of solar-battery systems for residential customers" 

  • Author: Yinyan Liu; Baran Yildiz‏
  • Journal: Energy
  • Year: 2025

"Techno-economic optimization of electric water heater and battery energy storage for residential dwellings with rooftop PV systems" 

  • Author: Yinyan Liu; Baran Yildiz
  • Journal: Energy and Buildings
  • Year: 2025

"A Flowrate Estimation Method for Gas–Water Two-Phase Flow Based on Multimodal Sensors and Hybrid LSTM-CNN Model" 

  • Author: Yuxiao Jiang; Yinyan Liu; Baijin Mao; Xing Lu; Yi Li; Lihui Peng‏
  • Journal: IEEE Transactions on Instrumentation and Measurement‏
  • Year: 2024

"A Flow Rate Estimation Method for Gas–Liquid Two-Phase Flow Based on Transformer Neural Network" 

  • Author: Yuxiao Jiang; Hao Wang; Yinyan Liu; Lihui Peng; Yanan Zhang; Bing Chen; Yi Li
  • Journal: IEEE Sensors Journal
  • Year: 2024