Md. Nahid Hasan | Computer Science | Best Researcher Awards

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

Francesco Agnelli | Graph Neural Networks | Best Researcher Award

Dr. Francesco Agnelli | Graph Neural Networks | Best Researcher Award

Dr. Francesco Agnelli, University of Milan, Italy

Francesco Agnelli is an Italian researcher and PhD candidate at the University of Milan, where he delves into the frontiers of deep learning and artificial intelligence. Born in 1998 in Cantù, Italy, Francesco began his academic journey with a stellar Bachelor’s and Master’s degree in Mathematics, both earned cum laude at the University of Insubria. His academic focus evolved from Morse Theory to applied mathematics and computational intelligence, leading to his cutting-edge research on Graph Neural Networks (GNNs). Francesco's work bridges advanced machine learning methods with real-world problems like affective computing and graph isomorphism. With industry experience at Power Reply and teaching stints in local schools, he combines theory with practical impact. Francesco also shares his knowledge as a tutor and speaker in major academic and tech platforms, including an NVIDIA webinar and the 2024 ECCV Conference. He continues to contribute to the intersection of mathematics, AI, and neural computation.

Professional Profile 

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Summary of Suitability for the 'Research for Best Researcher Award'

Francesco Agnelli stands out as an exceptional candidate for the 'Research for Best Researcher Award' due to his impressive academic background, comprehensive research experience, and contributions to cutting-edge fields in mathematics and computer science, particularly through his work with Graph Neural Networks (GNNs).

Francesco holds a Master’s degree in Mathematics with honors (110/110 cum laude) from Università degli Studi dell'Insubria, where his academic pursuits focused on computational mathematics and machine learning. His work has specifically contributed to the application of GNNs in the domains of graph isomorphism and affective computing, demonstrating his ability to innovate within highly specialized research areas. His ongoing Ph.D. studies at Università degli studi di Milano further highlight his dedication to advancing human understanding through deep learning and multi-modal input integration.

Education 

Francesco Agnelli holds a Master’s degree in Mathematics with highest honors (110/110 cum laude) from the University of Insubria, where he focused on computational mathematics and machine learning. His thesis explored the application of Graph Neural Networks to the graph isomorphism problem. During his studies, he completed an Erasmus semester at KU Leuven, Belgium, earning top grades in advanced courses like Wavelets and Applications (29/30), Life Insurance (30/30), and Machine Learning (29/30). He also earned a Bachelor’s degree in Mathematics, again with 110/110 cum laude, from the same university. Francesco participated in a 24 CFU course for teacher training and holds a Scientific High School diploma from Liceo Scientifico Enrico Fermi with a score of 96/100. Now pursuing a PhD at the University of Milan, Francesco investigates the use of GNNs in affective computing, incorporating multimodal inputs and foundation models to advance human-centered AI.

Professional Experience

Francesco Agnelli is currently a PhD student at the University of Milan, conducting research at PhuseLab on deep learning and human understanding. His work integrates Graph Neural Networks and foundation models to process multimodal affective data. Previously, Francesco worked as an IT Consultant at Power Reply in Milan, where he supported the Eni Multicard CRM project. His tasks included transitioning the system from Siebel to Salesforce, performing data analysis, and implementing technical corrections using tools like SOQL and Excel. Earlier, he served as a Mathematics and Science teacher at Istituto Comprensivo Como Lora, where he engaged with younger students to foster scientific curiosity. In parallel, he has tutored university-level courses like Mathematical Analysis and Computational Mathematics. His technical fluency spans Python (especially PyTorch), Matlab, SOQL, and Java. Francesco’s diverse experiences reflect a strong ability to bridge academic rigor with real-world application in both corporate and educational environments.

Awards and Recognition

Francesco Agnelli has received consistent recognition throughout his academic and professional journey. He graduated cum laude in both his Bachelor’s and Master’s degrees in Mathematics, reflecting outstanding academic excellence. As a department representative and member of multiple university commissions at the University of Insubria, Francesco was honored for his leadership and advocacy in academic governance. He was also selected as a university tutor, mentoring students in Mathematical Analysis and Computational Mathematics. His expertise in artificial intelligence earned him an invitation to speak at the prestigious NVIDIA webinar on "Enhancing Visual Understanding With Generative AI". Furthermore, he was a volunteer at the renowned ECCV 2024 Conference, showcasing his commitment to engaging with the AI research community. These accolades affirm his growing impact in academia and the AI research landscape, positioning him as a promising thought leader in the field of Graph Neural Networks and beyond.

Research Skill On Graph Neural Networks

Francesco Agnelli’s research skills are deeply rooted in computational mathematics, with a specialized focus on Graph Neural Networks (GNNs). His academic evolution—from Morse Theory to graph isomorphism problems—demonstrates a solid foundation in abstract mathematics and its translation into real-world computing tasks. In his PhD at the University of Milan, he explores the fusion of GNNs with affective computing, particularly multimodal input processing and fine-tuning of foundation models. His technical toolkit includes Python (PyTorch), Matlab, and data query languages like SOQL. Francesco exhibits high proficiency in integrating theoretical algorithms with deep learning frameworks, often experimenting with cross-domain solutions. He has hands-on experience working with numerical analysis, approximation methods, and neural architectures, allowing him to simulate and interpret graph-structured data effectively. His research reflects a forward-thinking and collaborative approach to AI that bridges data, emotions, and decision-making through intelligent systems grounded in strong mathematical logic.

  Publication Top Notes

  • Title: KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis

  • Authors: Francesco Agnelli, Giuseppe Facchi, Giuliano Grossi, Raffaella Lanzarotti

  • Journal: Array

  • DOI: 10.1016/j.array.2025.100392

  • Year: 2025

  • Citation: Agnelli, F., Facchi, G., Grossi, G., & Lanzarotti, R. (2025). KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis. Array. https://doi.org/10.1016/j.array.2025.100392

Yukun Shi | Computer Science | Best Scholar Award

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.

Professional Profile

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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