Dimitrios Tsourounis | Computer Science | Best Researcher Award

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Dr. Dimitrios Tsourounis | Computer Science | Best Researcher Award

Dr. Dimitrios Tsourounis | Computer Science | University of Patras | Greece

Dimitrios Tsourounis is a passionate computer scientist specializing in computer vision, deep learning, and quantum machine learning. Born on February 26, 1991, in Greece, Dimitrios earned his Ph.D. from the University of Patras in 2023, focusing on deep learning strategies for problems with limited data. He has contributed significantly to advancing machine learning methods and quantum computing integration, currently working as a Research Scientist at Quantum Neural Technologies (QNT) in Athens. Dimitrios is also involved in autonomous aerial systems research at the Athena Research Center, applying computer vision techniques to fuse radar and RGB camera data for UAVs. His multidisciplinary expertise includes physics, electronics, and artificial intelligence, supported by multiple successful EU-funded projects. With a proven track record in innovation and real-world applications, Dimitrios is recognized for bridging theoretical research and industrial challenges, particularly in quantum-enhanced machine learning and biometric security.

Author Profile

Scopus | Orcid | Google Scholar

Education 

Dimitrios completed his Ph.D. in Computer Vision at the University of Patras, Greece (2017-2023), specializing in deep learning, neural networks, and AI strategies for limited data scenarios under Prof. George Economou’s supervision. His doctoral thesis explored novel transfer learning and knowledge distillation techniques. Prior to this, Dimitrios earned an M.Sc. in Electronics, Engineering and Computer Science (2015-2017) from the University of Patras, graduating summa cum laude with a thesis on deep sparse coding. His academic foundation was built on a B.Sc. in Physics (2010-2015) from the same university, graduating magna cum laude, with research focused on sparse representation for offline handwritten signature recognition. Dimitrios also briefly studied medicine before shifting to physics and computing, showcasing a diverse academic background. Throughout his studies, he demonstrated academic excellence, receiving top grades and honors in rigorous technical fields that combine physical sciences with computer engineering.

Experience

Dimitrios currently works as a Research Scientist in Quantum Machine Learning at Quantum Neural Technologies (QNT) in Athens, designing quantum algorithms and integrating machine learning with quantum computing for industrial applications such as pharmaceuticals, cryptography, and finance. Since July 2025, he has been a Computer Vision Scientist at the Athena Research Center, focusing on UAV systems that fuse radar and camera data for autonomous aerial navigation. His Ph.D. research (2017-2023) involved deep learning for limited data, emphasizing convolutional neural networks and biometric applications. Dimitrios contributed to the DeepSky project on cloud type estimation using multi-sensor data and worked on Greek lip reading datasets employing deep sequential models. He also participated in RoadEye, developing AI solutions for road condition monitoring, pothole, and speed bump detection. Throughout his career, Dimitrios has utilized tools like Python, PyTorch, TensorFlow, Qiskit, and Matlab, continuously merging theoretical innovation with practical applications in computer vision, AI, and quantum technologies.

Awards and Honors

Dimitrios Tsourounis has received notable recognition for his academic and research excellence. He was awarded a prestigious scholarship from the Greek State Scholarships Foundation (IKY) to support his Ph.D. studies, reflecting his outstanding merit. Throughout his academic career, Dimitrios graduated summa cum laude for his M.Sc. and magna cum laude for his B.Sc., highlighting consistent academic distinction. His research contributions have been supported by competitive European Union and Greek national funding programs, including co-funding for projects such as DeepSky and RoadEye. Dimitrios has also been acknowledged within the quantum computing and AI research communities for pioneering integration of machine learning with quantum frameworks. His work has earned invitations to collaborate with leading academic and industry partners, reinforcing his reputation as an innovative scientist. While yet to accumulate traditional prize awards, his growing publication record and project leadership positions underscore his impact and future promise in computer science and quantum technologies.

Research Focus 

Dimitrios Tsourounis’s research centers on computer vision, deep learning, and quantum machine learning, with a particular focus on addressing challenges of limited data availability in neural network training. His Ph.D. work pioneered transfer learning and knowledge distillation methods tailored to biometric security and pattern recognition. Currently, Dimitrios explores quantum-enhanced machine learning algorithms leveraging variational quantum circuits to improve performance on complex scientific and industrial problems. His expertise also spans multimodal data fusion, combining radar and visual data in autonomous aerial systems to enhance object detection accuracy. Additionally, he investigates sequential deep learning architectures for tasks such as lip reading in the Greek language and environmental sensing through cloud type recognition using thermal and all-sky cameras. Dimitrios integrates classical machine learning frameworks like PyTorch with quantum programming tools such as Qiskit and Pennylane, pushing the frontier of hybrid classical-quantum AI. His work aims to bridge theoretical advances and practical applications across fields including cryptography, healthcare, and autonomous vehicles.

Publications 

  • “Deep Sparse Coding for Signal Representation”

  • “Neural Networks for Biometric Applications with Limited Data”

  • “Quantum Variational Circuits in Machine Learning”

  • “Fusion of Radar and RGB Data in UAV Object Detection”

  • “Lip Reading Greek Words Using Sequential Deep Learning”

  • “Cloud Type Estimation with All-Sky and Thermal Cameras”

  • “Real-Time Road Condition Monitoring via Computer Vision”

  • “Knowledge Distillation Techniques in Convolutional Neural Networks”

Conclusion

Dimitrios Tsourounis exemplifies a forward-thinking computer scientist, seamlessly integrating deep learning and quantum computing to tackle real-world challenges. His academic excellence, coupled with his innovative research in limited-data neural networks and quantum-enhanced AI, positions him as a leading researcher in computer vision and machine learning. Dimitrios’s contributions advance both theoretical knowledge and practical solutions across diverse sectors, from autonomous systems to pharmaceuticals. His dedication and interdisciplinary approach promise significant future impact in computer science and emerging quantum technologies.

 

Arivumalar Ravichandran | Computer science | Academic Excellence Award

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Dr. Arivumalar Ravichandran | Computer science | Academic Excellence Award

Dr. Arivumalar Ravichandran | Computer science | GreatLakes Institute of management | India

Dr. Arivumalar Ravichandran is an accomplished academician and researcher with an interdisciplinary background encompassing Information Technology, Computer Science, Engineering, and Human Resource Management. With over 17 years of teaching and research experience, she has held pivotal roles in prestigious institutions including Great Lakes Institute of Management, Sri Sairam Engineering College, and PRIST University. Her academic pursuit culminated in a Ph.D. in Techno-Management, expected to be conferred in 2025. Dr. Ravichandran’s work bridges computer science innovation with pragmatic management principles, enriching both technical and managerial education. Her research primarily targets IoT in agriculture, cloud-based smart campuses, cybersecurity, and logistics optimization. She is widely published in IEEE Xplore and international journals and known for translating theory into practice through her progressive teaching and research approach. Her dedication to both engineering and management education continues to inspire the next generation of data-driven, technology-enabled professionals.

Author Profile

Google Scholar

Education

Dr. Arivumalar Ravichandran’s educational journey reflects her diverse and rich academic expertise. She began with an M.Sc. in Information Technology from A.D.M. College for Women, followed by an M.Phil. in Computer Science from Periyar University, both with First Class distinction. To deepen her technical capabilities, she pursued an M.Tech in Computer Science and Engineering from PRIST University. Demonstrating her interdisciplinary vision, she obtained an M.B.A. in Human Resource Management from Bharathidasan University, blending technological acumen with managerial skills. Currently, she is a Ph.D. scholar in Techno-Management at Dr. N.G.P Institute of Technology, Coimbatore, with completion anticipated in 2025. This comprehensive academic background enables her to explore computer science from both an engineering and organizational perspective, making her uniquely suited for research that involves smart technology deployment in business and societal contexts.

Experience 

Dr. Arivumalar Ravichandran’s career spans over 17 years across leading academic institutions in India. She currently serves as Assistant Professor in Analytics & Operations at Great Lakes Institute of Management (since January 2024). She previously held dual roles in Sri Sairam Engineering College and SRM Valliammai Engineering College, teaching both CSBS and MBA programs. Her foundational experience includes five years as Assistant Professor in CSE at P.R. Engineering College and earlier academic roles at ARJ College, S.K. College of Arts & Science, and RDB College. Her career trajectory reflects an interdisciplinary footprint across Computer Science, MCA, and Management departments. She has a proven record of mentoring students, leading IT programs, and integrating research with curriculum delivery. As a department head and senior faculty, she has contributed to shaping institutional academic strategies while also engaging in publication-worthy research that aligns with industry and technology trends.

Awards and Honors

Dr. Arivumalar Ravichandran has consistently demonstrated excellence in research, academia, and leadership, earning her accolades in each institution she served. Though formal award titles are not explicitly listed, her career reflects significant recognitions in the form of trusted appointments in interdisciplinary teaching roles and departmental leadership. Her successful publication in prestigious Scopus-indexed and IEEE Xplore conferences and journals stands as a testament to her scholarly impact. Additionally, she has presented at international conferences and contributed to critical discourse in areas such as IoT in agriculture and risk management in logistics. These achievements mark her as a respected scholar and mentor in both technical and management circles. Her elevation to Assistant Professor roles across diverse departments and her long-standing service history are indicative of institutional recognition and peer trust. Her work continues to gain traction in the broader academic community.

Research Focus

Dr. Arivumalar Ravichandran’s research is rooted in addressing real-world challenges through advanced computing technologies. Her interdisciplinary focus spans IoT, cloud computing, cybersecurity, AI-driven smart campuses, and risk analysis in logistics. One of her prominent works involves developing a hybrid data acquisition model for precision agriculture using IoT, showcased at the ICOEI 2023 conference. She also investigates the role of cloud computing in building smart campuses, highlighting scalable solutions for educational transformation. Her earlier work focused on cyber threats, specifically mitigating malicious scripting via content security policies. Moreover, she explores techno-managerial topics such as global transportation risk management—blending IT expertise with operational strategy. This blend of computer science and business intelligence forms the core of her research philosophy: leveraging technology for sustainable, secure, and efficient solutions. Her ongoing Ph.D. enhances this integrative approach, promising further contributions at the intersection of computing, analytics, and enterprise systems.

Publication Titles 

  1. A Hybrid Data Acquisition Model for Precision Agriculture using IoT – IEEE Xplore, ICOEI 2023

  2. Analysis of Developing IoT and Cloud Computing Based Smart Campuses and its Applications – IEEE ACCAI 2024

  3. A Study on Risk Management of Global Transportation Service – Research Journal of Humanities and Social Sciences, 2023

  4. Mitigating Malicious Scripting Attacks with a Content Security Policy – IJARTET, July 2017

Conclusion

Dr. Arivumalar Ravichandran stands as a transformative figure in computer science education and research, integrating cutting-edge technical knowledge with human-centric solutions. With her strong academic background, robust publication record, and diverse teaching experience, she is a deserving candidate for the Computer Science Award. Her work continues to make a significant impact in academia and applied research, particularly in areas like IoT, smart systems, and security, reflecting both innovation and practical relevance.

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.

Profile

Google Scholar

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

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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 

Orcid

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

Ji Xu | Big Data Analytics | Best Researcher Award

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Prof. Ji Xu | Big Data Analytics | Best Researcher Award

Prof. Ji Xu, Guizhou University, China

Ji Xu (M’22) is an associate professor at the State Key Laboratory of Public Big Data, Guizhou University, China. He obtained his B.S. in Computer Science from Beijing Jiaotong University in 2004 and earned his Ph.D. in Computer Science from Southwest Jiaotong University in 2017. With expertise in data mining, granular computing, and machine learning, he has significantly contributed to the field through extensive research and publications. Dr. Xu has authored and co-authored over 30 papers in prestigious international journals, including IEEE TFS, IEEE TCYB, and Information Sciences. He also serves as a reviewer for top-tier journals like IEEE TNNLS, IEEE TFS, and Pattern Recognition. As an active member of IEEE, CCF, and CAAI, he remains at the forefront of technological advancements in artificial intelligence and big data analytics. His work continues to shape the future of intelligent computing and large-scale data processing.

Professional Profile

Google Scholar

Summary of Suitability for the Research for Best Researcher Award

Ji Xu is highly suitable for the “Research for Best Researcher Award” due to his impressive academic and professional achievements in the field of computer science, with a particular focus on data mining, granular computing, and machine learning. His educational background includes a Bachelor’s degree from Beijing Jiaotong University and a Ph.D. from Southwest Jiaotong University, which demonstrate his foundational expertise in these critical fields. As an associate professor at the State Key Laboratory of Public Big Data at Guizhou University, Xu has a clear commitment to advancing research in his area of specialization.

Xu’s research productivity further demonstrates his suitability for the award. He has authored over 30 peer-reviewed papers in prestigious international journals such as IEEE TFS, IEEE TCYB, IEEE JIoT, Information Sciences, and others. His contributions to these journals reflect his high-level expertise and ability to make significant advancements in his field. Furthermore, Xu has co-authored a book, showcasing his ability to synthesize and communicate complex ideas to a broader audience.

🎓 Education 

Ji Xu’s academic journey began at Beijing Jiaotong University, where he obtained his Bachelor of Science (B.S.) in Computer Science in 2004. He later pursued advanced studies at Southwest Jiaotong University, earning his Doctor of Philosophy (Ph.D.) in Computer Science in 2017. His doctoral research focused on artificial intelligence, data mining, and computational intelligence, laying a strong foundation for his contributions to big data analytics. Throughout his academic career, he demonstrated exceptional analytical skills and a deep understanding of machine learning techniques. His education provided him with the technical expertise required to explore complex datasets and develop intelligent computing models. Additionally, his training at two leading Chinese universities equipped him with interdisciplinary knowledge in software engineering, algorithms, and large-scale data processing. His academic background remains a cornerstone of his professional research, guiding his work in advanced computational methods and innovative AI applications.

💼 Professional Experience

Dr. Ji Xu is currently an associate professor at the State Key Laboratory of Public Big Data, Guizhou University. In this role, he leads research in big data analytics, machine learning, and granular computing. His professional experience spans academia and research, with a focus on developing intelligent algorithms for large-scale data processing. Over the years, he has collaborated with industry and academia on high-impact projects related to artificial intelligence and computational intelligence. As an active member of IEEE, CCF, and CAAI, he contributes to the global research community through technical publications, conference presentations, and journal reviews. In addition to his research, he mentors graduate students, guiding them in innovative AI and data science projects. His expertise in handling complex data-driven challenges has established him as a prominent researcher in the field. Dr. Xu’s work continues to influence advancements in big data and artificial intelligence applications.

🏅 Awards and Recognition

Dr. Ji Xu has received multiple accolades for his contributions to computer science, particularly in big data analytics, machine learning, and granular computing. He has been recognized for his research excellence through numerous best paper awards at international conferences. His extensive publication record in prestigious journals such as IEEE TFS, IEEE TCYB, and Neurocomputing has earned him a reputation as a leading researcher in artificial intelligence. Additionally, he serves as a reviewer for top-tier journals, including IEEE TNNLS, IEEE TFS, and Pattern Recognition, demonstrating his influence in shaping the field. As a distinguished member of IEEE, CCF, and CAAI, he actively participates in research communities and contributes to major advancements in computational intelligence. His innovative work in data science and AI continues to garner international recognition, positioning him among the top researchers driving the future of intelligent data processing and analytics.

🌍 Research Skills On Big Data Analytics

Dr. Ji Xu’s research expertise encompasses data mining, granular computing, and machine learning. His ability to analyze large-scale datasets and develop intelligent algorithms has led to groundbreaking contributions in big data analytics. He specializes in computational intelligence, predictive modeling, and pattern recognition, applying advanced AI techniques to solve complex real-world problems. His skills extend to deep learning, natural language processing (NLP), and algorithm optimization, enabling him to create efficient data-driven solutions. With a strong foundation in mathematical modeling and statistical analysis, he excels in deriving meaningful insights from high-dimensional data. His role as a reviewer for IEEE TFS, IEEE TNNLS, and Pattern Recognition reflects his deep understanding of AI methodologies. Additionally, he collaborates on interdisciplinary projects, integrating AI with emerging technologies such as IoT and edge computing. His research continues to push the boundaries of artificial intelligence, transforming data analytics and intelligent systems.

📖 Publication Top Notes

  • DenPEHC: Density peak based efficient hierarchical clustering
    Authors: J Xu, G Wang, W Deng
    Journal: Information Sciences, 373, 200-218
    Citations: 142
    Year: 2016

  • A survey of smart water quality monitoring system
    Authors: J Dong, G Wang, H Yan, J Xu, X Zhang
    Journal: Environmental Science and Pollution Research, 22(7), 4893-4906
    Citations: 139
    Year: 2015

  • Granular computing: from granularity optimization to multi-granularity joint problem solving
    Authors: G Wang, J Yang, J Xu
    Journal: Granular Computing, 2(3), 105-120
    Citations: 138
    Year: 2017

  • Self-training semi-supervised classification based on density peaks of data
    Authors: D Wu, M Shang, X Luo, J Xu, H Yan, W Deng, G Wang
    Journal: Neurocomputing, 275, 180-191
    Citations: 130
    Year: 2018

  • Review of big data processing based on granular computing
    Authors: J Xu, GY Wang, H Yu
    Journal: Chinese Journal of Computers, 38(8), 1497-1517
    Citations: 59
    Year: 2015

  • 基于粒计算的大数据处理 (Big Data Processing Based on Granular Computing)
    Authors: 徐计 (J Xu), 王国胤 (G Wang), 于洪 (H Yu)
    Journal: 计算机学报 (Chinese Journal of Computers), 38(8), 1497-1517
    Citations: 50
    Year: 2015

  • Fat node leading tree for data stream clustering with density peaks
    Authors: J Xu, G Wang, T Li, W Deng, G Gou
    Journal: Knowledge-Based Systems, 120, 99-117
    Citations: 44
    Year: 2017

  • Piecewise two-dimensional normal cloud representation for time-series data mining
    Authors: W Deng, G Wang, J Xu
    Journal: Information Sciences, 374, 32-50
    Citations: 40
    Year: 2016

  • A multi-granularity combined prediction model based on fuzzy trend forecasting and particle swarm techniques
    Authors: W Deng, G Wang, X Zhang, J Xu, G Li
    Journal: Neurocomputing, 173, 1671-1682
    Citations: 37
    Year: 2016

  • Local-Density-Based Optimal Granulation and Manifold Information Granule Description
    Authors: J Xu, G Wang, T Li, W Pedrycz
    Journal: IEEE Transactions on Cybernetics
    Citations: 28
    Year: 2017

Vijaya G | Computer Science | Women Researcher Award

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Dr. Vijaya G | Computer Science | Women Researcher Award

Dr. Vijaya G, Sri Krishna College of Engineering and Technology, Coimbatore, India

Dr. G. Vijaya is an accomplished educator, researcher, and administrator with over 18 years of experience in academia and operations management. Currently a Professor at Sri Krishna College of Engineering and Technology, she has previously held leadership roles, including Principal In-Charge and Head of Department. Her expertise spans curriculum development, faculty training, research supervision, and program implementation. She has been instrumental in introducing innovative courses, securing research funding, and mentoring students in cutting-edge areas like Artificial Intelligence, Machine Learning, and Cybersecurity. With multiple certifications, including Fortinet Certified Associate in Cybersecurity and NPTEL mentorship, she has made a significant impact on student learning and institutional growth. Dr. Vijaya has been recognized with several awards, including the Honorary Doctorate (D.Litt.) from the University of South America and the IEAE Young Achiever Award. Her dedication to research and education has positioned her as a leader in Computer Science and Engineering.

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Summary of Suitability for the Research for Women Researcher Award

Dr. G. Vijaya M.E. Ph.D. stands out as a highly qualified and accomplished educator with over 18 years of experience across multiple domains, including administration, operations management, teaching, curriculum development, and research. Her extensive academic and professional journey has consistently focused on empowering students, particularly women, and making significant contributions to the educational landscape.

Dr. Vijaya has been deeply involved in initiatives that promote the advancement of women in engineering and technology. As the Principal In-Charge at Bapatla Women’s Engineering College and later as a professor at Sri Krishna College of Engineering and Technology, she has taken leadership roles that directly impact female students. She has been instrumental in spearheading projects that aim to enhance the skill sets of women, such as the Pradhan Mantri Kaushal Vikas Yojana, which focuses on rural self-employment, and the various Faculty Development Programs she has initiated.

🎓 Education

Dr. G. Vijaya holds a Doctorate (Ph.D.) in Computer Science and Engineering from Annamalai University, specializing in Artificial Intelligence and Machine Learning applications. Her Master’s degree (M.E.) in Computer Science has provided her with a strong foundation in software development, data structures, and emerging technologies. She completed her Bachelor’s degree in Computer Science and Engineering, excelling in her academic pursuits and demonstrating a passion for research from an early stage. Throughout her educational journey, she has been an active participant in technical workshops, conferences, and training programs. She has also completed various certifications, including NPTEL courses in Python for Data Science and Soft Skills Development. Her academic background has enabled her to bridge the gap between theoretical knowledge and real-world applications, allowing her to develop innovative solutions and contribute to cutting-edge research in Artificial Intelligence, Cybersecurity, and Computational Biology.

💼 Professional Experience 

Dr. G. Vijaya has an extensive career in academia, spanning multiple prestigious institutions. She started as an Assistant Professor at Kalasalingam University and later progressed to Associate Professor at Bapatla Women’s Engineering College. She has served as Principal In-Charge, overseeing institutional development, faculty training, and curriculum enhancement. Currently a Professor at Sri Krishna College of Engineering and Technology, she is actively involved in mentoring students, leading research initiatives, and implementing AI-driven educational advancements. Her expertise includes academic administration, accreditation coordination (NBA, NAAC), and research proposal development. She has successfully secured government funding for AICTE-sponsored projects, focusing on ethical AI and smart technology applications. Additionally, she has played a key role in increasing departmental pass percentages and establishing industry-academic collaborations. With a passion for leadership, she has served as a Board of Studies (BoS) member, IEEE Coordinator, and Department Advisory Committee (DAC) Coordinator.

🏅 Awards and Recognition

Dr. G. Vijaya’s contributions to academia and research have been widely recognized. She was awarded an Honorary Doctorate (D.Litt.) from the University of South America for her exceptional work in Computer Science. She also received the IEAE Young Achiever Award for her research excellence. She has been certified as a Fortinet Certified Associate in Cybersecurity and recognized as a mentor for the IEEE Sustainable Solution for Humanity 2024. Her book, Futuristic Trends in Computing Technologies and Data Sciences, has been published by Iterative International Publishers. She has received multiple Certificates of Appreciation for achieving outstanding student results in AI and Machine Learning courses. Additionally, she secured research funding from AICTE, CSIR, and TNSCST for projects on AI ethics and smart agriculture. Her dedication to education, leadership in accreditation processes, and mentorship in research have made her a distinguished figure in Computer Science.

🌍 Research Skills On Computer Science

Dr. G. Vijaya specializes in Artificial Intelligence, Machine Learning, Deep Learning, and Cybersecurity. She has extensive experience in AI-driven predictive analytics, ethical AI integration, and IoT-enabled smart solutions. Her research contributions extend to Computational Biology, Natural Language Processing, and Blockchain for secure computing. She has successfully guided research projects in AI ethics, data security, and cloud computing. She has authored high-impact research papers and collaborated with industry experts on AI applications. As a Board of Studies (BoS) member, she has helped shape Computer Science curricula to incorporate cutting-edge technologies. She has mentored students in AI-based problem-solving competitions and secured research grants for AICTE-sponsored projects. Her ability to integrate AI methodologies with real-world applications has positioned her as a leading researcher in the field. She continues to contribute to emerging trends in AI governance, cybersecurity, and data-driven decision-making.

📖 Publication Top Notes

  1. Optimization and analysis of microwave-assisted extraction of bioactive compounds from Mimosa pudica L. using RSM & ANFIS modeling

    • Authors: V. Ganesan, V. Gurumani, S. Kunjiappan, T. Panneerselvam, …
    • Citations: 43
    • Year: 2018

  2. An adaptive preprocessing of lung CT images with various filters for better enhancement

    • Authors: G. Vijaya, A. Suhasini
    • Citations: 36
    • Year: 2014

  3. A novel noise reduction method using double bilateral filtering

    • Authors: G. Vijaya, V. Vasudevan
    • Citations: 17
    • Year: 2010

  4. Automatic detection of lung cancer in CT images

    • Authors: G. Vijaya, A. Suhasini, R. Priya
    • Citations: 15
    • Year: 2014

  5. Drivers Drowsiness Detection using Image Processing and I-Ear Techniques

    • Authors: S. Ananthi, R. Sathya, K. Vaidehi, G. Vijaya
    • Citations: 13
    • Year: 2023

  6. A simple algorithm for image denoising based on ms segmentation

    • Authors: G. Vijaya, V. Vasudevan
    • Citations: 13
    • Year: 2010

  7. Bilateral filtering using modified fuzzy clustering for image denoising

    • Authors: G. Vijaya, V. Vasudevan
    • Citations: 9
    • Year: 2010

  8. Image Denoising based on Soft Computing Techniques

    • Authors: G. Vijaya, V. Vasudevan
    • Citations: 4
    • Year: 2011

  9. A review analysis of attack detection using various methodologies in network security

    • Authors: P. R. Kanna, S. Gokulraj, K. Karthik, G. Vijaya, G. S. Kumar, G. Rajeshkumar
    • Citations: 3
    • Year: 2022

  10. Deep learning-based computer-aided diagnosis system

  • Authors: G. Vijaya
  • Citations: 2
  • Year: 2022

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

Sai Venkatesh Chilukoti | Computer Science | Best Researcher Award

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Mr. Sai Venkatesh Chilukoti | Computer Science | Best Researcher Award

Mr. Sai Venkatesh Chilukoti, University of Louisiana at Lafayette, United States

Sai Venkatesh Chilukoti is a dedicated researcher in Computer Engineering, currently pursuing a Ph.D. at the University of Louisiana at Lafayette under Dr. Xiali Hei. With a stellar academic record and a CGPA of 4.0, he specializes in Deep Learning, Network Security, and Cyber-Physical Systems. His research interests span Federated Learning, Differential Privacy, and Machine Learning applications in healthcare and security. Sai Venkatesh has contributed to multiple peer-reviewed journals and conferences, focusing on privacy-preserving AI and identity recognition. As a Research Assistant in the Wireless Embedded Device Security (WEDS) Lab, he has worked on cutting-edge projects integrating AI, privacy-enhancing techniques, and embedded security. With experience as a Teaching Assistant, he mentors students in Neural Networks, Python, and AI-related fields. His passion lies in translating research into real-world applications, particularly in medical imaging and cybersecurity. Sai is fluent in English, Hindi, and Telugu, and has strong technical skills in Python, PyTorch, and TensorFlow.

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Suitability for the Research for Best Researcher Award – Sai Venkatesh Chilukoti

Sai Venkatesh Chilukoti demonstrates an impressive academic and research portfolio, making him a strong contender for the Research for Best Researcher Award. Currently pursuing a Ph.D. in Computer Engineering at the University of Louisiana at Lafayette with a perfect 4.0/4.0 CGPA, he has developed expertise in deep learning, distributed computing, network security, and cyber-physical systems. His academic credentials are further strengthened by a solid foundation in electronics and communication engineering at the undergraduate level.

His research contributions are notable, particularly in privacy-preserving deep learning, federated learning, and medical AI applications. His work on diabetic retinopathy classification, gastrointestinal cancer prediction, and differential privacy models for healthcare data showcases both technical depth and real-world impact. His involvement in cutting-edge machine learning techniques, including LSTMs, Transformers, and convolutional networks, highlights his ability to innovate within the field. Furthermore, his research assistantship in Wireless Embedded Device Security (WEDS) Lab and role as a teaching assistant demonstrate both research rigor and mentorship capabilities.

🎓 Education 

Sai Venkatesh Chilukoti is currently pursuing a Ph.D. in Computer Engineering at the University of Louisiana at Lafayette (2021-2025, expected), under the supervision of Dr. Xiali Hei, with a perfect CGPA of 4.0. His coursework includes Deep Learning, Network Security, Distributed Computing, and Cyber-Physical Systems. His research focuses on privacy-preserving AI, federated learning, and deep learning model optimization.

He completed his B.Tech. in Electronics and Communication Engineering at Velagapudi Ramakrishna Siddhartha Engineering College (2017-2021), earning a CGPA of 8.53/10. His undergraduate studies encompassed AI, Python, Artificial Neural Networks, and Digital Signal Processing.

Throughout his education, Sai has actively engaged in research projects, including identity recognition using mmWave radar sensors, privacy-aware medical imaging, and deep learning applications in cybersecurity. His academic journey reflects a strong foundation in computational intelligence and a commitment to solving real-world challenges through innovative AI techniques.

💼 Professional Experience

Sai Venkatesh Chilukoti has extensive research and teaching experience, specializing in Deep Learning, Cybersecurity, and Federated Learning. As a Research Assistant at the Wireless Embedded Device Security (WEDS) Lab (2021-present), he has worked on privacy-preserving AI models, security solutions for embedded devices, and deep learning-based medical imaging applications. His work includes designing federated learning frameworks for decentralized AI and developing privacy-aware deep learning techniques.

As a Teaching Assistant for Neural Networks (2024-present), Sai mentors students in probability, calculus, and AI programming using PyTorch and Scikit-learn.

He has led numerous projects, such as statistical analysis of COVID-19 data, AI-driven financial forecasting, and deep learning applications in 3D printing quality control. His expertise extends to programming in Python, C, SQL, and MATLAB, along with experience in cloud computing and AI model deployment. He has also reviewed papers for IEEE Access and other reputed journals.

🏅 Awards and Recognition 

Sai Venkatesh Chilukoti has been recognized for his outstanding contributions to AI research, deep learning, and cybersecurity. He has received multiple conference paper acceptances, including at the Hawai’i International Conference on System Sciences (HICSS-56) and CHSN2021. His work on privacy-preserving AI has been published in high-impact journals like BMC Medical Informatics and Decision Making and Electronic Commerce Research and Applications.

He has also earned certifications in Deep Learning Specialization (Coursera), AI for Everyone (deeplearning.ai), and Applied Machine Learning in Python (University of Michigan). His research in Federated Learning has gained attention for its innovative approach to privacy protection in healthcare AI models. Additionally, Sai has contributed as a reviewer for IEEE Access and Euro S&P, demonstrating his expertise in computer security and AI ethics. His contributions to machine learning, cybersecurity, and privacy-aware AI continue to impact both academic and industrial domains.

🌍 Research Skills On Computer Science

Sai Venkatesh Chilukoti specializes in Federated Learning, Differential Privacy, and Deep Learning model optimization. His expertise spans AI-driven cybersecurity, identity recognition using mmWave radar sensors, and privacy-preserving medical imaging. He has worked extensively with machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn.

Sai has developed AI models for secure collaborative learning, utilizing techniques like DP-SGD for privacy preservation. His research also explores transformer-based architectures, convolutional networks, and ensemble learning methods to enhance predictive performance. He has integrated advanced optimization techniques, including adaptive gradient clipping and label smoothing, into deep learning pipelines.

He has hands-on experience with federated learning platforms like Flower and privacy-preserving AI models in medical data analysis. His work in statistical modeling, computer vision, and neural networks has contributed to breakthroughs in security and healthcare AI. Sai’s research aims to advance AI applications while maintaining ethical and privacy standards.

📖 Publication Top Notes

  • A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric
      • Authors: Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Anthony S. Maida, Xiali Hei
      • Journal: BMC Medical Informatics and Decision Making
      • Volume: 24, Issue 1
      • Article Number: 37
      • Year: 2024
  • Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
      • Authors: Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
      • Conference: 56th Hawaii International Conference on System Sciences
      • Year: 2023
  • Single Image Multi-Scale Enhancement for Rock Micro-CT Super-Resolution Using Residual U-Net
    • Authors: Liqun Shan, Chengqian Liu, Yanchang Liu, Yazhou Tu, Sai Venkatesh Chilukoti
    • Journal: Applied Computing and Geosciences
    • Year: 2024
  • Kernel-Segregated Transpose Convolution Operation
    • Authors: Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu
    • Conference: 56th Hawaii International Conference on System Sciences
    • Year: 2023
  • Modified ResNet Model for MSI and MSS Classification of Gastrointestinal Cancer
    • Authors: Sai Venkatesh Chilukoti, C. Meriga, M. Geethika, T. Lakshmi Gayatri, V. Aruna
    • Book Title: High Performance Computing and Networking: Select Proceedings of CHSN 2021
    • Year: 2022
  • Enhancing Unsupervised Rock CT Image Super-Resolution with Non-Local Attention
    • Authors: Chengqian Liu, Yanchang Liu, Liqun Shan, Sai Venkatesh Chilukoti, Xiali Hei
    • Journal: Geoenergy Science and Engineering
    • Volume: 238
    • Article Number: 212912
    • Year: 2024
  • Method for Performing Transpose Convolution Operations in a Neural Network
    • Inventors: Vijay Srinivas Tida, Sonya Hsu, Xiali Hei, Sai Venkatesh Chilukoti, Yazhou Tu
    • Patent Application: US Patent App. 18/744,260
    • Year: 2024
  • IdentityKD: Identity-wise Cross-modal Knowledge Distillation for Person Recognition via mmWave Radar Sensors
    • Authors: Liqun Shan, Rujun Zhang, Sai Venkatesh Chilukoti, Xingli Zhang, Insup Lee
    • Conference: ACM Multimedia Asia
    • Year: 2024
  • Facebook Report on Privacy of fNIRS Data
    • Authors: M. I. Hossen, Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Xiali Hei
    • Preprint: arXiv preprint arXiv:2401.00973
    • Year: 2024

Ahona Ghosh | Computer Science | Best Researcher Award

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Ms. Ahona Ghosh | Computer Science | Best Researcher Award

👤 Ms. Ahona Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India

Ahona Ghosh is a promising researcher in the field of Computer Science and Engineering with a focus on artificial intelligence, machine learning, and rehabilitation technologies. Currently completing her Ph.D. at Maulana Abul Kalam Azad University of Technology, West Bengal, Ahona has made significant strides in the academic and research community. Her work involves a blend of deep learning, cognitive rehabilitation, and IoT-based systems for improving quality of life. With several publications in prestigious international journals and conferences, she has earned recognition for her contributions to the scientific community. Ahona has been awarded the Best Paper Award for her work on IoT-based waste management and has ranked highly in various competitions like MAKATHON’22. She is passionate about leveraging technology for social good, particularly in healthcare and rehabilitation systems.

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🌟 Ms. Ahona Ghosh, Summary of Suitability

Dr. Ahona Ghosh is an outstanding candidate for the Research for Best Researcher Award, demonstrating exceptional academic accomplishments, innovative research contributions, and consistent excellence throughout her career. Her extensive academic background includes a Ph.D. in Computer Science and Engineering from Maulana Abul Kalam Azad University of Technology, West Bengal, with her pre-submission and viva completed, reflecting her advanced expertise and dedication to her field. She has received accolades for her academic and research endeavors, such as the Best Paper Award for her IoT-based waste management system and the Academic Excellence Award from Brainware University.

Her robust portfolio of research contributions includes an impressive array of international journal articles, conference papers, patents, and book chapters. Dr. Ghosh’s work spans cutting-edge topics such as deep learning, cognitive rehabilitation, IoT applications, and fuzzy systems, addressing societal challenges like healthcare, rehabilitation, and sustainable development.

🎓 Education 

Ahona Ghosh has a strong academic background, with a Ph.D. in Computer Science and Engineering from Maulana Abul Kalam Azad University of Technology (MAKAUT), where she is in the final stages of her thesis submission. She completed her Master of Technology (M.Tech.) in the same field at MAKAUT in 2019, with a CGPA of 8.73. Her Bachelor’s degree in Computer Science and Engineering (B.Tech.) was awarded by Techno India College of Technology in 2017, where she achieved a CGPA of 7.66. Ahona’s early education includes Higher Secondary in Science from Taki House Government Sponsored Girls High School, with an aggregate of 67.4%. She also passed the Madhyamik Pariksha (Class 10) from Duff High School for Girls with a remarkable score of 82.88%. Ahona is also certified in NTA-NET for the years 2018 and 2019.

💼  Professional Experience

Ahona Ghosh has worked extensively in academia and research, focusing on artificial intelligence, IoT, and healthcare applications. Currently, she is a Doctoral Fellow at Maulana Abul Kalam Azad University of Technology (MAKAUT). Her research includes contributions to cognitive rehabilitation using machine learning and EEG-based sensor systems. She has also been involved in various projects concerning IoT-based solutions for healthcare, such as designing smart systems for cognitive rehabilitation and enhancing data-driven rehabilitation methods. In addition, Ahona has been a part of multiple international conferences where she presented papers, co-authored patents, and contributed to the scientific community with impactful research. Her teaching experience includes mentoring undergraduate students and guiding research projects, as well as working on industry collaborations in technology development. Ahona’s expertise in both theoretical and applied aspects of Computer Science has shaped her as a versatile professional in the field.

🏅 Awards and Recognition

Ahona Ghosh has received several accolades for her academic and research achievements. She won the Best Paper Award at the IETE Eastern Zonal Seminar with ISF Congress in 2017 for her paper on “Waste Management System Based on Internet of Things (IoT)”. Her innovative contributions earned her the Academic Excellence Award from Brainware University in January 2020, based on exceptional student feedback. She also achieved 2nd place in the MAKATHON’22 competition organized by MAKAUT’s Innovation Council. Ahona’s recognition extends beyond awards, as she is a prominent figure in academic circles, having presented her research at several prestigious IEEE conferences. Her qualifications include passing the NTA-NET exams in 2018 and 2019, reinforcing her academic prowess. Ahona’s dedication to research and innovation continues to receive recognition, making her an influential presence in her field.

🌍 Research Skills On Computer Science 

Ahona Ghosh has developed a comprehensive set of research skills, particularly in the areas of Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Rehabilitation. Her expertise extends to using IoT for healthcare applications, including creating systems for rehabilitative therapy and mental health analysis. Ahona is proficient in data analysis, algorithm design, and modeling for both real-time and research-driven applications. Her experience with neural networks, sensor systems, and signal processing further enhances her ability to tackle complex problems. Ahona has contributed to developing innovative frameworks using fuzzy logic, sensor networks, and electroencephalography (EEG) in health-related projects. She excels in academic writing, having published in numerous peer-reviewed journals and international conferences. Additionally, she is well-versed in patent filing, research methodology, and project management, which are crucial in carrying out high-impact scientific work.

📖 Publication Top Notes

Scope of Sentiment Analysis On News Articles Regarding Stock Market and GDP in Struggling Economic Condition
  • Authors: S Biswas, A Ghosh, S Chakraborty, S Roy, R Bose
    Journal: International Journal of Emerging Trends in Engineering Research, 8 (7), 3594
    Citation: 30
    Year: 2020
A Detailed Study on Data Centre Energy Efficiency and Efficient Cooling Techniques
  • Authors: D Mukherjee, S Chakraborty, I Sarkar, A Ghosh, S Roy
    Journal: International Journal of Advanced Trends in Computer Science and Engineering
    Citation: 26
    Year: 2020
Recognition of hand gesture image using deep convolutional neural network
  • Authors: KM Sagayam, AD Andrushia, A Ghosh, O Deperlioglu, AA Elngar
    Journal: International Journal of Image and Graphics, 22 (03), 2140008
    Citation: 22
    Year: 2022
Service aware resource management into cloudlets for data offloading towards IoT
  • Authors: D Guha Roy, B Mahato, A Ghosh, D De
    Journal: Microsystem Technologies, 1-15
    Citation: 21
    Year: 2022
Mathematical modelling for decision making of lockdown during COVID-19
  • Authors: A Ghosh, S Roy, H Mondal, S Biswas, R Bose
    Journal: Applied Intelligence
    Citation: 17
    Year: 2021
Secured Energy-Efficient Routing in Wireless Sensor Networks Using Machine Learning Algorithm: Fundamentals and Applications
  • Authors: A Ghosh, CC Ho, R Bestak
    Journal: Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks
    Citation: 12
    Year: 2020
A survey on Internet-of-Thing applications using electroencephalogram
  • Authors: D Chakraborty, A Ghosh, S Saha
    Book: Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach, 21-47
    Citation: 12
    Year: 2020
Rehabilitation using neighbor-cluster based matching inducing artificial bee colony optimization
  • Authors: S Saha, A Ghosh
    Conference: 2019 IEEE 16th India Council International Conference (INDICON), 1-4
    Citation: 12
    Year: 2019
Dtnma: identifying routing attacks in delay-tolerant network
  • Authors: S Chatterjee, M Nandan, A Ghosh, S Banik
    Book: Cyber Intelligence and Information Retrieval: Proceedings of CIIR 2021, 3-15
    Citation: 11
    Year: 2022
Emotion detection using generative adversarial network
  • Authors: S Das, A Ghosh
    Book: Generative Adversarial Networks and Deep Learning, 165-182
    Citation: 10
    Year: 2023