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

 

Md. Nahid Hasan | Computer Science | Best Researcher Awards

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Mr. Md. Nahid Hasan | Computer Science | Best Researcher Awards

Mr. Md. Nahid Hasan, Dhaka International University, Bangladesh

Md. Nahid Hasan is a dedicated academic and researcher in Computer Science and Engineering, currently serving as a Lecturer at Dhaka International University. With a strong foundation in software development, machine learning, and data science, he has published several peer-reviewed articles in reputed journals and international conferences. He is known for blending advanced AI techniques with real-world challenges, particularly in health analytics, text classification, biosensors, and cybersecurity. Md. Hasan is pursuing his M.Sc. Engineering in CSE from BUET with a CGPA of 3.75 and previously graduated with distinction from Khulna University. His diverse research has garnered international attention, reflecting his deep curiosity, discipline, and passion for innovation. A former winner of the IEEE YESIST12 Innovation Challenge, he continues to contribute to both academia and industry with impactful research and teaching. Md. Hasan envisions a future driven by ethical AI and smart technologies that elevate human potential.

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Suitability Assessment for Research for Best Researcher Award: Md. Nahid Hasan

Md. Nahid Hasan demonstrates a strong profile for the Research for Best Researcher Award based on his academic background, research contributions, and professional engagement in the field of Computer Science & Engineering. Currently pursuing an M.Sc. in Computer Science & Engineering at Bangladesh University of Engineering and Technology (BUET), he has already established a solid foundation with a B.Sc. degree where he graduated with a commendable GPA of 3.87 and secured the 2nd position in his class.

His employment history highlights consistent academic involvement as a lecturer at reputed universities, including Dhaka International University and Daffodil International University, showcasing his dedication to both teaching and research simultaneously. This professional experience provides him with a practical platform to influence and contribute to academic development.

🎓 Education

Md. Nahid Hasan’s educational journey exemplifies academic excellence and dedication. He is currently pursuing his M.Sc. Engineering in Computer Science and Engineering from the prestigious Bangladesh University of Engineering and Technology (BUET), holding a CGPA of 3.75 with thesis remaining. His undergraduate studies were completed at Khulna University, where he graduated with a CGPA of 3.87 and secured the second position in his class. His strong foundation was built at Dinajpur Govt. College and Dinajpur Zilla School, where he achieved perfect GPAs of 5.00 in both HSC and SSC. Throughout his academic life, he has demonstrated exceptional analytical skills, logical reasoning, and innovative thinking. His curriculum has been enriched with practical programming, AI applications, and research projects, which paved the way for his contributions in machine learning, cybersecurity, and biosensor modeling. This educational background not only underpins his current research but also fuels his ambitions for advancing intelligent technologies.

💼 Professional Experience

Md. Nahid Hasan has steadily progressed through various academic roles, currently holding a Lecturer position in the Department of Computer Science and Engineering at Dhaka International University since January 2024. Prior to this, he served as a Lecturer at Daffodil International University (Jan 2023 – Jan 2024) and previously at Dhaka International University (Feb 2022 – Dec 2022). In these roles, he has taught core CSE subjects, mentored undergraduate research, and contributed to academic course development. His teaching philosophy centers around interactive learning, analytical thinking, and real-world application of computing principles. Outside the classroom, he is actively involved in research collaborations, interdisciplinary projects, and conference presentations. His industry-relevant insight and academic rigor allow him to bridge the gap between theoretical knowledge and emerging technologies. Through his academic appointments, Md. Hasan continues to inspire students, encourage innovation, and strengthen institutional research output in Bangladesh’s higher education landscape.

🏅 Awards and Recognition 

Md. Nahid Hasan’s academic journey is adorned with several accolades that reflect his brilliance and commitment. Notably, he was the Winner of the IEEE YESIST12 Innovation Challenge Track 2021, an internationally recognized competition that celebrates innovative technological solutions. He has also been a recipient of multiple merit-based scholarships throughout his undergraduate studies at Khulna University, a testament to his consistent academic performance and leadership potential. His research works have been accepted and presented at esteemed IEEE international conferences across Europe and Asia. With journal articles published in reputed outlets like Array and EAI Endorsed Transactions on IoT, he is quickly gaining recognition in global research circles. Md. Hasan’s contributions span across machine learning, bioinformatics, and cybersecurity—areas critical to the digital transformation of society. His awards not only highlight his technical abilities but also his potential to drive meaningful change through data-driven innovation.

🌍 Research Skills On Computer Science

Md. Nahid Hasan possesses a rich blend of research skills at the intersection of artificial intelligence, machine learning, and computational modeling. His expertise includes advanced statistical analysis, neural networks (ANN, LSTM, Bi-LSTM), and ensemble learning models, often applied in areas such as mental health prediction, biosensor simulation, natural language processing, and cybersecurity. He is proficient in PyTorch, Python, SQL, and C++, and utilizes LaTeX for scholarly writing. His research often involves building predictive models, performing comparative classifier analyses, and optimizing AI pipelines for complex data systems. He is also skilled in academic publishing, technical documentation, and collaborative research design. With hands-on experience in multiple IEEE conferences, Md. Hasan continues to refine his methodologies through peer feedback, interdisciplinary collaboration, and continual learning. His ability to translate real-world problems into algorithmic solutions exemplifies a future-ready research mindset grounded in ethical and impactful innovation.

📖  Publication Top Notes

  • Title: Computing Confinement Loss of Open-Channels Based PCF-SPR Sensor with ANN Approach
    Authors: N. Islam, M.S.I. Khan, M.N. Hasan, M.A. Yousuf
    Citation: 4
    Year: 2023

  • Title: Computing Optical Properties of Open–Channels Based Plasmonic Biosensor Employing Plasmonic Materials with ML Approach
    Authors: N. Islam, I.H. Shibly, M.M.S. Hasan, M.N. Hasan, M.A. Yousuf
    Citation: 4
    Year: 2023

  • Title: A Comparative Study on Machine Learning Classifiers for Cervical Cancer Prediction: A Predictive Analytic Approach
    Authors: K.M.M. Uddin, I.A. Sikder, M.N. Hasan
    Citation: 1
    Year: 2024

  • Title: An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features
    Authors: K.M.M. Uddin, M.A. Islam, M.N. Hasan, K. Ahmad, M.A. Haque
    Citation: 1
    Year: 2023

  • Title: Stacked Ensemble Method: An Advanced Machine Learning Approach for Anomaly-based Intrusion Detection System
    Authors: A. Rahman, M.S.I. Khan, M.D.Z.A. Eidmum, P. Shaha, B. Muiz, N. Hasan, …
    Citation: — (citation not provided)
    Year: 2025

  • Title: Language Prediction of Twitch Streamers using Graph Convolutional Network
    Authors: M.N. Hasan, N. Saha, M.A. Rahman
    Citation: — (citation not provided)
    Year: 2025

  • Title: Artificial Neural Network-Assisted Confinement Loss Prediction of D-Shaped PCF-SPR Biosensor
    Authors: N. Islam, M.M.S. Hasan, M.N. Hasan, I.H. Shibly, M.A. Yousuf, M.Z. Uddin
    Citation: — (citation not provided)
    Year: 2024

  • Title: Credibility Analysis of Robot Speech Based on Bangla Language Dialect
    Authors: M.N. Hasan, R. Azim, S. Sharmin
    Citation: — (citation not provided)
    Year: 2024

  • Title: A Comparative Study on Machine Learning Classifiers for Early Diagnosis of Cervical Cancer
    Authors: I.A. Sikder, M.N. Hasan, R. Jahan, A. Mohamed, Y. Dirie
    Citation: — (citation not provided)
    Year: 2024

  • Title: Machine Learning Classification Approach for Refractive Index Prediction of D-Shape Plasmonic Biosensor
    Authors: N. Islam, M.N. Hasan, M.M.S. Hasan, I.H. Shibly, M.A. Yousuf, M.Z. Uddin
    Citation: — (citation not provided)
    Year: 2024

Alimul Rajee | Computer Science | Young Scientist Award

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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

Ahlem Ayari | Computer Science | Best Researcher Award

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Dr. Ahlem Ayari | Computer Science | Best Researcher Award

Dr. Ahlem Ayari, Higher Institute of Management of Sousse, Tunisia 

Ahlem Ayari is a dedicated PhD student at the National Engineering School of Sousse (ENISo), specializing in cloud computing, edge computing, and high-performance computing. She has extensive experience as a lecturer and trainer in computer science, particularly in database management, object-oriented programming, and web development. With a strong foundation in Java, C, JavaScript, and other programming languages, Ahlem has contributed to various software and web-based projects. Her research focuses on distributed systems, artificial intelligence, and machine learning algorithms. She has actively participated in international conferences and academic collaborations, highlighting her expertise in deploying e-health applications on cloud and edge computing environments. Ahlem is passionate about advancing technology in healthcare and has successfully developed innovative platforms that integrate deep learning algorithms for medical data analysis. She remains committed to contributing to technological advancements and mentoring the next generation of computer scientists through her academic and research pursuits.

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Suitability for the Research for Best Researcher Award – Ahlem Ayari

Ahlem Ayari demonstrates a strong academic background and technical expertise, making her a promising candidate for the Research for Best Researcher Award. She is currently a PhD student at the National Engineering School of Sousse (ENISo), Tunisia, specializing in advanced computing fields such as distributed systems, high-performance computing, artificial intelligence, and machine learning. Her research contributions, particularly in cloud and edge computing for e-health applications, highlight her commitment to solving real-world challenges through innovative technology.

Her experience extends beyond academia, as she has actively contributed to teaching and training at multiple institutions, including Higher Institute of Management (ISG) and The Maghreb Institute of Economic Sciences and Technology (IMSET). She has taught courses in database management, programming, UML, MERISE, and cloud computing, demonstrating her ability to disseminate knowledge effectively. Additionally, her industry experience as a web developer strengthens her profile by bridging the gap between theoretical research and practical application.

🎓 Education

  • 2023-2024: PhD Student in Computer Science, “Mars” Laboratory, National Engineering School of Sousse (ENISo), Sousse University. Supervisor: Prof. Mohamed Nazih Omri, Co-supervisor: Hassen Hamdi.
  • 2021-2023: Master in Business Computing, Higher Institute of Management (ISG), Sousse University. Supervisor: Hassen Hamdi.
  • 2017-2020: Bachelor’s Degree in Business Computing, Higher Institute of Management (ISG), Sousse University. Supervisor: Kamel Garrouch.
  • 2017: Mathematics Baccalaureate, Ibn Rachik Kairouan.

Ahlem Ayari’s academic journey reflects her commitment to interdisciplinary learning, combining computer science and business computing. Her doctoral research focuses on cloud and edge computing, aiming to optimize computational efficiency and data security in medical applications. Throughout her studies, she has acquired expertise in artificial intelligence, database management, software engineering, and statistical analysis, contributing to her proficiency in designing and implementing complex computational systems.

💼 Professional Experience 

  • Trainer, Maghreb Institute of Economic Sciences and Technology (IMSET) (2024)
    • Taught courses on E-commerce, UML, MERISE, and SQL database management.
    • Conducted practical training in software development and IT systems.
  • Assistant Master, Higher Institute of Management (ISG), Sousse (2024)
    • Lectured in object-oriented programming (Java), cloud computing, big data, and C2I (Certificate in Computer Science and Internet).
  • Trainer, Higher Institute of Management (ISG), Sousse (2024)
    • Conducted LaTeX training for CV, report, and presentation creation.
  • Web Developer, Access Leader (March 2020 – September 2020)
    • Developed a web application using Angular for truck sales management.

🏅 Awards and Recognition

  • 12th International Conference of ISG Sousse (2023): Recognized for research on deploying e-health applications in edge and cloud computing environments.
  • 28th International Conference on Knowledge-Based and Intelligent Information Engineering Systems, Seville, Spain (2024): Presented innovative research on IoMT-based e-health applications.
  • Academic Distinction: Awarded recognition for excellence in cloud computing and AI research.
  • Best Research Presentation Award: Acknowledged for outstanding work in high-performance computing.

🌍 Research Skills On Computer Science

Ahlem Ayari possesses advanced research skills in distributed computing, cloud and edge computing, big data analytics, and AI-driven applications. Her expertise extends to designing and implementing machine learning models for real-time data processing. She is proficient in various programming languages (Java, C, Python) and frameworks (Angular, Symfony, Node.js). Her research methodologies involve deep learning algorithms for e-health applications and optimizing computational infrastructures. Ahlem actively contributes to international conferences, presenting innovative solutions in high-performance computing.

📖 Publication Top Notes

E-health Application In IoMT Environment Deployed in An Edge And Cloud Computing Platforms

  • Author: A., Ayari, Ahlem, H., Hassen, Hamdi, K.A., Alsulbi, Khlil Ahmad

Yukun Shi | Computer Science | Best Scholar Award

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Assoc. Prof. Dr. Yukun Shi | Computer Science | Best Scholar Award

Assoc. Prof. Dr. Yukun Shi, Beijing University of Chemical Technology, China

Dr. Yukun Shi is an accomplished researcher and Associate Professor at the Department of Information Science and Technology, Beijing University of Chemical Technology. He specializes in multi-agent systems, control system network attacks, and distributed estimation. Dr. Shi earned his Ph.D. in Control Science and Engineering from Beijing University of Chemical Technology in 2022. His academic journey includes a one-year research visit to the University of Victoria, Canada, in 2021. His contributions to the field are significant, particularly in advancing secure state estimation and consensus control. He has published extensively in top-tier journals, addressing challenges in network security and distributed control. With a strong background in system modeling and cybersecurity, Dr. Shi continues to drive innovations in multi-agent collaboration and resilience against malicious attacks. His research not only contributes to theoretical advancements but also has practical implications for industrial and technological applications worldwide.

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Suitability for the Research for Best Scholar Award – Yukun Shi

Dr. Yukun Shi, an Associate Professor at the Beijing University of Chemical Technology, has demonstrated remarkable academic and research excellence in the field of control science and engineering. His expertise spans critical areas such as multi-agent systems, control system network attacks, distributed estimation, and consensus control, making his contributions highly relevant to modern automation and cybersecurity challenges. His work is particularly notable in the area of secure state estimation, where he has investigated the robustness of networked control systems against malicious sensor attacks, an emerging concern in industrial and cyber-physical systems.

Dr. Shi’s research output includes several publications in prestigious IEEE journals, such as IEEE Transactions on Automation Science and Engineering and IEEE Transactions on Control of Network Systems, highlighting his ability to contribute cutting-edge advancements in his field. His scholarly work is well-cited, reflecting both its impact and recognition within the scientific community. Additionally, his international exposure, including a research visit at the University of Victoria, Canada, underscores his global perspective and collaborative research approach.

🎓 Education 

Dr. Yukun Shi pursued his Ph.D. in Control Science and Engineering at Beijing University of Chemical Technology, graduating in 2022. His doctoral research focused on secure state estimation in multi-agent systems under adversarial conditions, bridging control theory with cybersecurity. As part of his academic development, he undertook a one-year research visit to the University of Victoria, Canada, in 2021, where he collaborated on cutting-edge projects related to network security and control systems. His education provided him with a strong foundation in distributed control, estimation algorithms, and robust filtering techniques. Throughout his studies, Dr. Shi honed his expertise in tackling cyber threats to industrial control systems, laying the groundwork for his future research in resilient multi-agent networks. His academic journey is marked by rigorous training, innovative problem-solving, and contributions to the field of control and automation engineering.

💼 Professional Experience

Dr. Yukun Shi currently serves as an Associate Professor at the Department of Information Science and Technology, Beijing University of Chemical Technology. With a research focus on multi-agent systems, network security, and distributed estimation, he has made significant contributions to securing cyber-physical systems. His professional journey includes leading research projects on sensor attacks, consensus control, and fault-tolerant filtering in distributed networks. Dr. Shi actively collaborates with international institutions to develop advanced methodologies for improving the resilience of control systems against malicious threats. His role extends beyond research, encompassing mentorship, curriculum development, and industry partnerships. He is a sought-after speaker at academic conferences and has peer-reviewed numerous articles in high-impact journals. His dedication to cybersecurity and control engineering has positioned him as a thought leader in the field, driving innovation and practical solutions to safeguard modern industrial and technological infrastructures.

🏅 Awards and Recognition 

Dr. Yukun Shi has received multiple accolades for his pioneering work in control systems and cybersecurity. He has been recognized for his contributions to secure multi-agent systems and networked control security. His research papers have been published in high-impact journals, earning him best paper awards at leading automation and control conferences. Dr. Shi has also been a recipient of prestigious research grants that support his work in developing robust estimation algorithms against cyber threats. His outstanding contributions have been acknowledged by industry associations, positioning him as a key figure in distributed system security. His work has not only influenced academia but also guided practical implementations in industrial automation and cybersecurity frameworks. Additionally, Dr. Shi has served as a reviewer for top-tier journals, further highlighting his expertise and influence in the scientific community. His relentless pursuit of excellence continues to shape the future of secure control systems.

🌍 Research Skills On Computer Science

Dr. Yukun Shi possesses a robust research skill set centered around multi-agent systems, control system security, and distributed estimation. His expertise includes developing secure state estimation techniques to mitigate network attacks in cyber-physical systems. He specializes in designing fault-tolerant control algorithms that enhance the resilience of distributed networks. His research also encompasses consensus control strategies to improve synchronization in multi-agent environments. Dr. Shi is proficient in advanced filtering techniques, such as Kalman filtering and observer design, to ensure accurate system monitoring despite adversarial interference. He actively applies mathematical modeling and optimization methods to enhance decision-making in complex systems. His work in secure control frameworks has broad applications in autonomous systems, industrial automation, and networked infrastructures. With a keen focus on practical implementation, Dr. Shi’s research continues to bridge theoretical advancements with real-world security challenges, contributing to the evolution of resilient cyber-physical networks.

📖 Publication Top Notes

  • Title: Optimal Output-Feedback Controller Design Using Adaptive Dynamic Programming: A Permanent Magnet Synchronous Motor Application
    • Authors: Zhongyang Wang, Huiru Ye, Youqing Wang, Yukun Shi, Li Liang
    • Citation: IEEE Transactions on Circuits and Systems II: Express Briefs
    • Year: 2025
  • Title: Distributed Filter Under Homologous Sensor Attack and Its Application in GPS Meaconing Attack
    • Authors: Yukun Shi, Wenjing He, Li Liang, Youqing Wang
    • Citation: IEEE Transactions on Automation Science and Engineering
    • Year: 2024
  • Title: Event-triggered distributed secure state estimation for homologous sensor attacks
    • Authors: Yukun Shi, Haixin Ma, Jianyong Tuo, Youqing Wang
    • Citation: ISA Transactions
    • Year: 2023
  • Title: Distributed Secure State Estimation of Multi-Agent Systems Under Homologous Sensor Attacks
    • Authors: Yukun Shi, Youqing Wang, Jianyong Tuo
    • Citation: IEEE/CAA Journal of Automatica Sinica
    • Year: 2023
  • Title: Online Secure State Estimation of Multiagent Systems Using Average Consensus
    • Authors: Yukun Shi, Youqing Wang
    • Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems
    • Year: 2022
  • Title: Asymptotically Stable Filter for MVU Estimation of States and Homologous Unknown Inputs in Heterogeneous Multiagent Systems
    • Authors: Yukun Shi, Changqing Liu, Youqing Wang
    • Citation: IEEE Transactions on Automation Science and Engineering
    • Year: 2022
  • Title: Secure State Estimation of Multiagent Systems With Homologous Attacks Using Average Consensus
    • Authors: Yukun Shi, Changqing Liu, Youqing Wang
    • Citation: IEEE Transactions on Control of Network Systems
    • Year: 2021