Bei Guan | Computer Science | Best Researcher Award

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

 

Alimul Rajee | Computer Science | Young Scientist Award

Mr. Alimul Rajee | Computer Science | Young Scientist Award

Mr. Alimul Rajee, Dept. of ICT, Comilla University, Kotbari, Bangladesh

Alimul Rajee is a Lecturer at the Department of Information and Communication Technology, Comilla University. His academic journey includes a stellar performance with a CGPA of 3.69 in his M.Sc. in Information Technology from Jahangirnagar University. Rajee’s research interests span Machine Learning, Data Science, Artificial Intelligence, Cyber Security, and Robotics, with a focus on real-world applications such as traffic accident data analysis and smart waste management. He has contributed significantly to several research projects, and his work has been published in prestigious journals, such as Knowledge-Based Systems and Heliyon. In addition to his research, Rajee is an active educator, mentoring students and supervising projects in areas like IoT and deep learning. His dedication extends beyond the classroom to extracurricular activities, where he has received multiple awards and recognitions, including an international award for his project at Fujitsu Research Institute in Tokyo.

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Suitability Summary of Young Scientist Awards

Alimul Rajee stands out as an excellent candidate for the Research for Young Scientist Award due to his impressive academic achievements, significant research contributions, and commitment to advancing in the fields of Machine Learning, Data Science, Artificial Intelligence, Cyber Security, and IoT. He has a strong educational background, earning his M.Sc. and B.Sc. with high CGPA rankings from Jahangirnagar University, which reflects his deep knowledge and dedication to his field.

Rajee’s research work is highly commendable, with several publications in reputable, Scopus-indexed journals such as Knowledge-Based Systems and Heliyon, where he has contributed to the development of novel algorithms and methodologies, especially in big data analysis, sentiment analysis, and AI-based applications. His ongoing and completed research projects, including a hybrid smart waste management system and aspect-based sentiment analysis for Bengali text, further showcase his innovative thinking and practical application of emerging technologies to address real-world problems. Additionally, his leadership in supervising over 40 academic projects and his participation in global training programs, like those held at the Fujitsu Research Institute in Japan, illustrate his proactive approach to both learning and teaching.

🎓  Education

Alimul Rajee completed his M.Sc. in Information Technology from Jahangirnagar University, securing a CGPA of 3.69 out of 4, ranking 6th in his batch. Before this, he earned his B.Sc. (Hons.) in the same field, also from Jahangirnagar University, with a CGPA of 3.71, again securing the 6th position. Rajee’s academic excellence dates back to his secondary education, where he achieved the highest CGPA of 5.00 in both his HSC and SSC exams from Govt. Ananadamohan College and Islamnagar Sailampur High School. His continuous pursuit of academic excellence earned him merit-based scholarships throughout his education. His academic prowess has laid a strong foundation for his research and professional career, as he continues to excel in his field with a focus on cutting-edge technologies such as AI and IoT.

💼 Professional Experience

Alimul Rajee’s professional career began as a Junior Data Scientist at Oculin Tech BD Ltd., where he worked from March 2020 to May 2021. He then served as a Senior Officer (ICT) at Sonali Bank PLC for a brief period before becoming a Lecturer at Comilla University in November 2021, where he currently teaches. Rajee’s teaching journey includes roles at Bangladesh University of Business and Technology (BUBT) and Jahangirnagar University (IIT-JU), where he was a Teacher Assistant. His extensive experience also includes supervising over 40 academic projects focused on machine learning, deep learning, and IoT. As an educator, he fosters a positive learning environment, guiding students through complex technical concepts while contributing to the development of innovative research and real-world applications.

🏅  Awards and Recognition

Alimul Rajee’s achievements have been recognized at both national and international levels. He has received several awards, including the UGC Research Grant from Comilla University for consecutive fiscal years, which is a testament to his research capabilities. Rajee’s work has been recognized by prestigious institutions such as Fujitsu Research Institute (FRI) in Tokyo, where his final project won 1st prize. He has also been a reviewer for the International Conference on Embracing Industry 4.0 for Sustainable Business Growth. His consistent academic and research excellence has earned him regular merit-based scholarships and fellowships, such as the National Science & Technology Fellowship from the ICT Division of Bangladesh.

🌍 Research Skills On Computer Science

Alimul Rajee specializes in the application of cutting-edge technologies such as Machine Learning, Artificial Intelligence, Cyber Security, and IoT. His research includes a diverse range of topics like traffic accident data analysis, sentiment analysis of Bengali text, and smart waste management. Rajee has honed his expertise in Data Science and deep learning methods, contributing to several high-impact publications in renowned journals such as Knowledge-Based Systems and Heliyon. His current research projects include Aspect-Category-Opinion-Sentiment Quad Extraction for Bengali Text and a Hybrid Smart Waste Management Technique using Deep Learning and IoT. Rajee’s proficiency in data analysis, algorithm design, and system integration showcases his strong research skills and his commitment to advancing technology for societal benefit.

📖 Publication Top Notes

  • “Aspect-based sentiment analysis for Bengali text using bidirectional encoder representations from transformers (BERT)”
    • Authors: MM Samia, A Rajee, MR Hasan, MO Faruq, PC Paul
    • Citation: International Journal of Advanced Computer Science and Applications, 13(12)
    • Year: 2022
  • “Detecting the provenance of price hike in agri-food supply chain using private Ethereum blockchain network”
    • Authors: MH Sayma, MR Hasan, M Khatun, A Rajee, A Begum
    • Citation: Heliyon, 10(11)
    • Year: 2024
  • “Analyzing depression on social media utilizing machine learning and deep learning methods”
    • Authors: PC Paul, MT Ahmed, MR Hasan, A Rajee, K Sultana
    • Citation: Indian Journal of Computer Science and Engineering, 14(5), 740-746
    • Year: 2023
  • “WFFS—An ensemble feature selection algorithm for heterogeneous traffic accident data analysis”
    • Authors: A Rajee, MS Satu, MZ Abedin, KMA Ali, S Aloteibi, MA Moni
    • Citation: Knowledge-Based Systems, 113089
    • Year: 2025