Bei Guan | Computer Science | Best Researcher Award

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Assoc. Prof. Dr. Bei Guan | Computer Science | Best Researcher Award

Assoc. Prof. Dr. Bei Guan, Institute of Software, Chinese Academy of Sciences, China

Dr. Bei Guan is a distinguished Senior Engineer (Associate Professor) at the Institute of Software, Chinese Academy of Sciences. With deep-rooted expertise in Big Data Analytics, Cyber Security, and Knowledge Graph-based systems, he has established himself as a key contributor to intelligent system development. Dr. Guan earned prominence through innovative work in operating system virtualization, malicious domain detection, and traditional Chinese medicine analytics. His postdoctoral research at QCRI, Qatar, led to the breakthrough “Guilt by Association” framework for cyber threat detection. Beyond academia, he has led impactful national and industrial projects ranging from AI in civil aviation to smart manufacturing platforms. Passionate about applying data science to real-world problems, Dr. Guan consistently pushes the frontier of technological application in intelligent diagnostics and threat intelligence systems. His career exemplifies a balance of theoretical rigor and practical innovation in computer science.

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Suitability Summary for Research for Best Researcher Award: Bei Guan

Bei Guan demonstrates strong qualifications that align well with the prestigious Research for Best Researcher Award. As a Senior Engineer (Associate Professor) at the Institute of Software, Chinese Academy of Sciences, his academic and professional journey shows a deep and sustained commitment to cutting-edge research in multiple high-impact areas such as Big Data Analytics, Cyber Security Analytics, Threat Intelligence, Virtualization, and Knowledge Graphs.

His research portfolio reflects significant contributions, particularly in developing novel algorithms and systems for detecting malicious cyber activities—work that has practical applications in national and global cybersecurity. The “Guilt by Association” graph inference technique he helped develop has been recognized as a major achievement, evidencing not only innovation but also real-world impact. Moreover, his leadership roles in major funded projects (with grants up to 1.5 million RMB) on intelligent diagnosis in Traditional Chinese Medicine and big data applications in industrial manufacturing highlight his capability to manage complex, interdisciplinary research programs successfully.

🎓 Education 

Dr. Bei Guan holds a Ph.D. in Computer Science, during which he cultivated his proficiency in virtualization, cloud computing, and security systems. His academic training emphasized system-level design and optimization, culminating in research focused on enhancing VM communication efficiency and integrity measurement in virtual environments. Notably, he contributed to Google Summer of Code (GSoC) projects from 2011 to 2013, where he optimized I/O performance in Xen environments and advanced support for OVMF virtual firmware. These global collaborations provided hands-on experience with open-source communities and cutting-edge system architecture. Additionally, he participated in the prestigious Chinese Academy of Sciences (CAS) Main Direction Program for Cloud OS development, solidifying his role in trusted computing. This rigorous academic foundation, enriched by diverse international projects, laid the groundwork for Dr. Guan’s pioneering efforts in secure computing and big data analysis, which now anchor his research and leadership roles at the Chinese Academy of Sciences.

💼 Professional Experience 

Dr. Bei Guan currently serves as a Senior Engineer (Associate Professor) at the Institute of Software, Chinese Academy of Sciences, where he has led national and industry-backed projects since 2018. Before that, from 2014 to 2018, he was a postdoctoral researcher at Qatar Computing Research Institute (QCRI), contributing to the renowned MADA project on malicious activity intelligence. His key roles involved developing graph-based inference systems to detect stealthy domains and contributing to one of QCRI’s major milestones, the “Guilt by Association” algorithm. At ISCAS, he spearheaded intelligent diagnostic systems using Traditional Chinese Medicine (TCM) data and big data analytics. He also managed AI-centric platforms in industrial manufacturing and civil aviation, employing microservices and neural networks for predictive analytics. Earlier in his career, he made significant contributions to virtualization and system security under GSoC and CAS initiatives. His work bridges academic excellence with practical, scalable system deployments.

🏅 Awards and Recognition 

Dr. Bei Guan has earned notable recognition for his impactful research in cybersecurity and big data systems. His co-authored paper, “A Domain is only as Good as its Buddies,” presented at CODASPY 2018, received the Best Paper Award, affirming the importance of his graph-based malicious domain inference technique. His breakthrough work under the “Guilt by Association” framework was also prominently highlighted on the official website of QCRI as one of their leading achievements. In addition, Dr. Guan was a three-time recipient of Google’s highly selective Summer of Code (GSoC) grant, which underscored his technical innovation and collaboration with the open-source community. His continued success in securing significant national funding, including 1.5 million RMB from China’s Ministry of Science and Technology for TCM diagnostics, showcases the trust placed in his leadership. These honors reflect Dr. Guan’s ability to merge academic rigor with real-world impact in computer science.

🌍 Research Skills On Computer Science

Dr. Bei Guan demonstrates a multidisciplinary research portfolio combining system security, data analytics, knowledge representation, and AI. He is proficient in developing inference algorithms, designing knowledge graphs, and building data pipelines in complex domains like Traditional Chinese Medicine, civil aviation, and manufacturing. His core technical skills include graph-based anomaly detection, neural networks, virtualization technologies (Xen, OVMF), and microservice architecture. Dr. Guan effectively utilizes big data frameworks such as Hadoop and applies machine learning to detect malicious activity in DNS logs, IP clusters, and online behavior. His “Guilt by Association” model represents a milestone in cybersecurity analytics. Equally adept at theoretical modeling and system deployment, he integrates entity extraction, deep learning, and natural language processing in domain-specific knowledge bases. As a project manager and team leader, he brings strategic vision and execution capability to research translation. His dynamic skills enable him to contribute effectively across academic and industrial research collaborations.

📖 Publication Top Notes

  • Large language models meet nl2code: A survey
    Authors: D. Zan, B. Chen, F. Zhang, D. Lu, B. Wu, B. Guan, Y. Wang, J.G. Lou
    Citation: 202
    Year: 2022

  • CERT: Continual pre-training on sketches for library-oriented code generation
    Authors: D. Zan, B. Chen, D. Yang, Z. Lin, M. Kim, B. Guan, Y. Wang, W. Chen, J.G. Lou
    Citation: 140
    Year: 2022

  • Discovering malicious domains through passive DNS data graph analysis
    Authors: I. Khalil, T. Yu, B. Guan
    Citation: 135
    Year: 2016

  • When language model meets private library
    Authors: D. Zan, B. Chen, Z. Lin, B. Guan, Y. Wang, J.G. Lou
    Citation: 79
    Year: 2022

  • CIVSched: A Communication-aware Inter-VM Scheduling Technique for Decreased Network Latency between Co-located VMs
    Authors: B. Guan, J. Wu, Y. Wang, S.U. Khan
    Citation: 48
    Year: 2014

  • Private-library-oriented code generation with large language models
    Authors: D. Zan, B. Chen, Y. Gong, J. Cao, F. Zhang, B. Wu, B. Guan, Y. Yin, Y. Wang
    Citation: 32
    Year: 2023

  • Predictive value of serum thyroglobulin for structural recurrence following lobectomy for papillary thyroid carcinoma
    Authors: S. Xu, H. Huang, X. Zhang, Y. Huang, B. Guan, J. Qian, X. Wang, S. Liu, Z. Xu, …
    Citation: 31
    Year: 2021

  • A domain is only as good as its buddies: Detecting stealthy malicious domains via graph inference
    Authors: I.M. Khalil, B. Guan, M. Nabeel, T. Yu
    Citation: 30
    Year: 2018

  • Following passive DNS traces to detect stealthy malicious domains via graph inference
    Authors: M. Nabeel, I.M. Khalil, B. Guan, T. Yu
    Citation: 28
    Year: 2020

  • Return-Oriented Programming Attack on the Xen Hypervisor
    Authors: B. Ding, Y. Wu, Y. He, S. Tian, B. Guan, G. Wu
    Citation: 27
    Year: 2012

 

Iustina Ivanova | Computer Science | Best Researcher Award

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Mrs. Iustina Ivanova | Computer Science | Best Researcher Award

👤 Mrs. Iustina Ivanova, FBK, Italy

Iustina Ivanova is an accomplished researcher in the field of Artificial Intelligence (AI) with a focus on computer vision and machine learning applications in real-world scenarios. She holds a Master’s degree in Artificial Intelligence from the University of Southampton, where she earned distinction for her research on neural networks for object detection. Currently, Iustina is engaged in AI research in smart agriculture at the Fondazione Bruno Kessler in Italy. Over the years, she has contributed to a variety of high-impact projects, including developing a recommender system for outdoor sport climbers and researching sensors for sports activity analysis. Her work has earned her several well-regarded publications and recognition in the AI and computer vision communities.

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🌟 Summary of Suitability for the Research for Best Researcher Award

Iustina Ivanova demonstrates exceptional qualifications for the “Research for Best Researcher Award.” Her academic background, professional experience, and research contributions highlight her significant impact on the fields of artificial intelligence (AI), machine learning, and computer vision. Her academic journey is distinguished by a Master’s degree in Artificial Intelligence with distinction from the University of Southampton and ongoing research pursuits during her Ph.D. studies. While her Ph.D. remains incomplete, the work she has undertaken—such as her contributions to recommender systems and computer vision—showcases her ability to address complex, real-world problems.

Professionally, Iustina’s research experience is diverse and impactful. At the Fondazione Bruno Kessler, she has been actively involved in applying AI to smart agriculture, addressing sustainability and innovation in the domain. Her previous roles, including as a Computer Vision Data Scientist and Data Science Moderator, further demonstrate her ability to bridge academia and industry.

🎓 Education

Iustina Ivanova has an impressive academic background in computer science and AI. She completed her Master of Science in Artificial Intelligence with distinction at the University of Southampton, UK, in 2018. Before that, she earned a Specialist degree in Software Engineering from Bauman Moscow State Technical University, Russia, in 2013. In 2019, she pursued a PhD in Computer Science at the Free University of Bolzano, Italy, although she later decided to focus more on practical AI applications. Her academic journey includes notable achievements such as developing research in neural networks for object detection, which has been the cornerstone of her professional career in AI.

💼  Professional Experience 

Iustina Ivanova has a diverse and robust professional background in AI and computer vision. She currently works as a researcher at the Fondazione Bruno Kessler, Italy, specializing in the use of AI for smart agriculture. Prior to this, Iustina served as a Data Science Moderator at Netology, Russia, where she designed and delivered online courses in statistics and mathematics for data science students. She also worked as a Computer Vision Data Scientist at OCRV, Russia, where she helped develop a video-based tracking system for railway workers, focusing on object detection and worker time measurement. Iustina’s role as a teacher of informatics and mathematics at Repetitor.ru involved preparing high school students for their final exams, ensuring that many students successfully entered top universities. Throughout her career, she has collaborated on numerous innovative projects in AI, particularly in outdoor sports and smart agriculture.

🏅Awards and Recognition 

Iustina Ivanova’s dedication and excellence in the field of AI have been recognized through multiple prestigious awards and accolades. Notably, she won several editions of the NOI Hackathon, including the SFSCON Edition (2021, 2022, 2024) and the Open Data Hub Edition (2022), showcasing her ability to create cutting-edge solutions in AI and data science. Her contributions to research and development in AI for sports activity analysis and computer vision have been published in highly regarded journals and conferences, such as the ACM Conference on Recommender Systems and IEEE Conferences. Iustina has also received recognition for her teaching contributions, inspiring future generations of data scientists. Her projects, especially those related to sports climbers’ recommender systems and sensor data analysis, have received wide acclaim for their innovation and real-world impact.

🌍 Research Skills On Computer Science

Iustina Ivanova’s research expertise spans artificial intelligence, machine learning, computer vision, and recommender systems. She is particularly skilled in using AI techniques to solve complex problems in real-world applications. Her work with neural networks for object detection and sensor data analysis has led to significant advancements in both sports and smart agriculture sectors. Iustina is proficient in Python, OpenCV, machine learning frameworks like PyTorch and TensorFlow, and data analysis tools such as Jupyter Notebook and Git. She is well-versed in the development of recommender systems and has implemented innovative solutions for outdoor sports, including climbing crag recommendations. Her interdisciplinary approach combines knowledge from software engineering, data science, and AI to design systems that enhance user experience and improve decision-making. Iustina is committed to the continual development of her skills, evident in her participation in advanced data science and deep learning courses, as well as her extensive practical work in AI.

📖 Publication Top Notes

  • Climbing crags repetitive choices and recommendations
    • Author: Ivanova, I.
    • Citation: Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
    • Year: 2023
    • Pages: 1158–1164
  • How can we model climbers’ future visits from their past records?
    • Authors: Ivanova, I., Wald, M.
    • Citation: UMAP 2023 – Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2023
    • Pages: 60–65
  • Introducing Context in Climbing Crags Recommender System in Arco, Italy
    • Authors: Ivanova, I.A., Wald, M.
    • Citation: International Conference on Intelligent User Interfaces, Proceedings IUI
    • Year: 2023
    • Pages: 12–15
  • Climbing crags recommender system in Arco, Italy: a comparative study
    • Authors: Ivanova, I., Wald, M.
    • Citation: Frontiers in Big Data
    • Year: 2023
    • Volume: 6, Article: 1214029
  • Map and Content-Based Climbing Recommender System
    • Authors: Ivanova, I.A., Buriro, A., Ricci, F.
    • Citation: UMAP2022 – Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2022
    • Pages: 41–45
  • Climbing Route Difficulty Grade Prediction and Explanation
    • Authors: Andric, M., Ivanova, I., Ricci, F.
    • Citation: ACM International Conference Proceeding Series
    • Year: 2021
    • Pages: 285–292
  • Climber behavior modeling and recommendation
    • Author: Ivanova, I.
    • Citation: UMAP 2021 – Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2021
    • Pages: 298–303
  • Knowledge-based recommendations for climbers
    • Authors: Ivanova, I., Andrić, M., Ricci, F.
    • Citation: CEUR Workshop Proceedings
    • Year: 2021
    • Volume: 2960
  • Climbing activity recognition and measurement with sensor data analysis
    • Authors: Ivanova, I., Andric, M., Janes, A., Ricci, F., Zini, F.
    • Citation: ICMI 2020 Companion – Companion Publication of the 2020 International Conference on Multimodal Interaction
    • Year: 2020
    • Pages: 245–249
  • Video and Sensor-Based Rope Pulling Detection in Sport Climbing
    • Authors: Ivanova, I., Andrić, M., Moaveninejad, S., Janes, A., Ricci, F.
    • Citation: MMSports 2020 – Proceedings of the 3rd International Workshop on Multimedia Content Analysis in Sports
    • Year: 2020
    • Pages: 53–60