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

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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.

 

Md. Nahid Hasan | Computer Science | Best Researcher Awards

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

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

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

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

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

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

🎓 Education

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

💼 Professional Experience

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

🏅 Awards and Recognition 

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

🌍 Research Skills On Computer Science

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

📖  Publication Top Notes

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

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

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

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

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

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

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

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

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

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

Nikolaos Freris | Robotics | Best Researcher Award

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Prof. Dr. Nikolaos Freris | Robotics | Best Researcher Award

Prof. Dr. Nikolaos Freris, University of Science and Technology of China, China

Dr. Nikolaos M. Freris is a distinguished academic and researcher specializing in computer science, robotics, and artificial intelligence. Currently serving as a professor and Vice Dean at the University of Science and Technology of China (USTC), he leads the AIoT Laboratory, advancing cutting-edge innovations in distributed learning and cyber-physical systems. Dr. Freris earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign, graduating with a perfect GPA of 4.0/4.0. With extensive international experience, including positions at IBM Research, EPFL, and NYU Abu Dhabi, his work bridges theoretical advancements and practical applications in intelligent systems. A prolific speaker and educator, he has delivered numerous keynotes and distinguished lectures worldwide. His passion for innovation and knowledge-sharing solidifies his reputation as a leader in AI-driven technologies, fostering global collaborations and pioneering impactful research.

Professional Profile

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Prof. Dr. Nikolaos Freris Summary of Suitability for the Award

Nikolaos M. Freris is an exceptionally accomplished researcher and academic, making him a strong candidate for the Research for Best Researcher Award. His educational background is impeccable, with a Ph.D. in Electrical and Computer Engineering and two master’s degrees (Mathematics and Electrical & Computer Engineering) from the University of Illinois at Urbana-Champaign, all earned with perfect GPAs. His work bridges cutting-edge domains such as systems, decision and control, communication networks, and optimization, demonstrating a deep understanding of complex interdisciplinary challenges.

Professionally, Dr. Freris has held prestigious positions at globally recognized institutions, including his current role as a professor at the University of Science and Technology of China (USTC), where he also serves as Vice Dean of the International College and Director of the AIoT Laboratory.

🎓 Education 

Dr. Nikolaos M. Freris holds a robust academic foundation in engineering and computer science. He received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign, specializing in systems, decision, and control, and achieving a perfect 4.0/4.0 GPA. During his doctoral studies, his groundbreaking dissertation explored optimization models for wireless networks, including clock synchronization and video streaming. He also earned dual Master’s degrees from the same university in Mathematics and Electrical and Computer Engineering. Prior to his postgraduate pursuits, he graduated summa cum laude with a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, where his thesis addressed innovative approaches in biomedical engineering. This exceptional academic journey underpins his expertise in robotics, AI, and distributed systems, driving his impactful research and academic contributions globally.

💼  Professional Experience

Dr. Nikolaos M. Freris boasts a dynamic career in academia and industry. Since 2019, he has been a Professor of Computer Science at USTC, where he directs the AIoT Laboratory, pioneering advances in distributed learning and robotics. He also serves as Vice Dean of the International College, fostering global academic partnerships. Previously, he was an Assistant Professor at NYU Abu Dhabi and a Global Network Assistant Professor at NYU Tandon, leading research on cyber-physical systems. His industry experience includes roles as a Senior Research Scientist at EPFL, managing Qualcomm-funded projects, and as a Postdoctoral Fellow at IBM Research Zurich. His early career featured internships at Deutsche Telekom and Xerox, where he explored innovative computational solutions. With a strong track record of leadership in interdisciplinary projects, Dr. Freris continues to bridge the gap between theoretical research and real-world applications.

🏅  Awards and Recognition

Dr. Nikolaos M. Freris is a recipient of numerous prestigious awards recognizing his contributions to computer science and engineering. His academic excellence is reflected in his perfect GPA achievements during his Ph.D. and Master’s studies. He has delivered keynotes at leading conferences such as the International Conference on Internet of Things and AIoT forums. His research on distributed learning, robotics, and AIoT has earned him accolades, including invited talks at institutions like Zhejiang University and international symposiums. As a leader in global academic initiatives, he was recognized for his innovative teaching and impactful research by organizations such as IEEE and CAS. Dr. Freris’s accolades underscore his commitment to advancing intelligent systems and fostering collaborations across disciplines, enhancing the global understanding of cutting-edge technologies.

🌍 Research Skills On Robotics 

Dr. Nikolaos M. Freris demonstrates exceptional research skills in robotics, distributed systems, and artificial intelligence. His expertise spans optimization theory, machine learning, and cyber-physical systems, with applications in AIoT and intelligent transportation. He specializes in communication-efficient algorithms, real-time data learning, and bio-inspired robotics, contributing to sustainable solutions for modern technological challenges. As the founder and director of AIoT and Cyberphysical Systems Labs, he leads transformative research initiatives that bridge theoretical models and real-world implementation. His methodological rigor and creative problem-solving skills are evident in his widely cited publications and pioneering keynote addresses. Dr. Freris’s ability to synthesize complex systems into actionable innovations positions him as a thought leader in emerging AI and robotics domains.

📖 Publication Top Notes

Title: Randomized extended Kaczmarz for solving least squares
  • Authors: A. Zouzias, N. M. Freris
    Journal: SIAM Journal on Matrix Analysis and Applications
    Citations: 349
    Year: 2013
Title: Fundamental limits on synchronizing clocks over networks
  • Authors: N. M. Freris, S. R. Graham, P. R. Kumar
    Journal: IEEE Transactions on Automatic Control
    Citations: 270
    Year: 2010
Title: Fundamentals of large sensor networks: Connectivity, capacity, clocks, and computation
  • Authors: N. M. Freris, H. Kowshik, P. R. Kumar
    Journal: Proceedings of the IEEE
    Citations: 151
    Year: 2010
Title: A new randomized block-coordinate primal-dual proximal algorithm for distributed optimization
  • Authors: P. Latafat, N. M. Freris, P. Patrinos
    Journal: IEEE Transactions on Automatic Control
    Citations: 89
    Year: 2019
Title: Fundamental limits on synchronization of affine clocks in networks
  • Authors: N. M. Freris, P. R. Kumar
    Conference: 2007 46th IEEE Conference on Decision and Control
    Citations: 79
    Year: 2007
Distortion-aware scalable video streaming to multinetwork clients
  • Authors: N. M. Freris, C. H. Hsu, J. P. Singh, X. Zhu
    Journal: IEEE/ACM Transactions on Networking
    Citations: 69
    Year: 2012
Fast distributed smoothing of relative measurements
  • Authors: N. M. Freris, A. Zouzias
    Conference: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC)
    Citations: 64
    Year: 2012
Multiplayer Stackelberg–Nash game for nonlinear system via value iteration-based integral reinforcement learning
  • Authors: M. Li, J. Qin, N. M. Freris, D. W. C. Ho
    Journal: IEEE Transactions on Neural Networks and Learning Systems
    Citations: 56
    Year: 2020
A synchrophasor data-driven method for forced oscillation localization under resonance conditions
  • Authors: T. Huang, N. M. Freris, P. R. Kumar, L. Xie
    Journal: IEEE Transactions on Power Systems
    Citations: 56
    Year: 2020
A model-based approach to clock synchronization
  • Authors: N. M. Freris, V. S. Borkar, P. R. Kumar
    Conference: Proceedings of the 48th IEEE Conference on Decision and Control (CDC)
    Citations: 53
    Year: 2009