Dimitrios Tsourounis | Computer Science | Best Researcher Award

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.

 

Le Yao | Computer Science | Best Researcher Award

Prof. Le Yao | Computer Science | Best Researcher Award

Prof. Le Yao, Hangzhou Normal University, China

Le Yao is an accomplished Associate Professor at the School of Mathematics, Hangzhou Normal University, China. With a strong background in control science and engineering, he specializes in data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. His research focuses on deep learning, interpretable modeling, and causal analysis for industrial applications. Le Yao has been actively involved in multiple funded projects supported by NSFC and the China Postdoctoral Science Foundation. He has an impressive academic record, with numerous high-impact publications in IEEE Transactions and other renowned journals. Recognized for his contributions, he has received prestigious awards, including the National Scholarship for Ph.D. and Outstanding Dissertation Awards. His innovative work bridges the gap between theoretical advancements and practical applications in industrial processes, making significant contributions to smart manufacturing and intelligent systems.

Professional Profile

Scopus

Orcid

Google Scholar

Summary of Suitability for the ‘Research for Best Researcher Award’

Le Yao is an exceptional candidate for the ‘Research for Best Researcher Award,’ given his impressive academic journey, extensive research contributions, and leadership in the field of industrial data-driven modeling. His work focuses on crucial areas such as soft sensor modeling, quality prediction, fault diagnosis, and causal analysis, with significant contributions to process control in industrial settings. His innovations in deep learning, causal analysis, and interpretable process modeling have greatly advanced the application of machine learning techniques to complex, large-scale industrial systems.

Notably, his research on scalable and distributed parallel modeling for big process data, combined with his exploration of probabilistic modeling and causal discovery methods, reflects a profound understanding of both theoretical and practical aspects of industrial systems. His ability to fuse domain knowledge with data-driven techniques has led to breakthroughs in process quality prediction and fault detection, impacting industries significantly. Furthermore, Le Yao has successfully secured competitive research funding from prestigious sources, such as the National Natural Science Foundation of China (NSFC) and the China Postdoctoral Science Foundation, demonstrating his capability to lead high-level research initiatives.

🎓 Education

Le Yao holds a Ph.D. in Control Science and Engineering from Zhejiang University (2019), where he specialized in big process data modeling, quality prediction, and process monitoring. His doctoral studies were pivotal in advancing soft sensor modeling techniques for industrial applications. Prior to his Ph.D., he earned an M.S. (2015) from Jiangnan University, where he focused on soft sensor modeling and system identification. His bachelor’s degree (2012) was also from Jiangnan University, where he developed a strong foundation in control science and engineering. Throughout his academic journey, Le Yao has consistently demonstrated excellence, securing prestigious scholarships and honors. His multidisciplinary expertise enables him to develop innovative solutions for industrial automation, smart manufacturing, and data-driven decision-making. His research contributions have influenced numerous industrial applications, bridging the gap between academic advancements and real-world implementations.

💼 Professional Experience 

Le Yao is currently an Associate Professor at Hangzhou Normal University (2022–present), where he leads research on deep learning, causal analysis, and interpretable modeling for industrial systems. Prior to this, he served as a Postdoctoral Researcher (2019–2022) at Zhejiang University’s Institute of Industrial Process Control, focusing on deep learning-driven process modeling and process knowledge fusion. During his postdoctoral tenure, he was awarded research grants from NSFC and the China Postdoctoral Science Foundation. His expertise spans scalable and distributed parallel modeling, soft sensor applications, and quality prediction in large-scale industrial systems. Le Yao’s research integrates advanced computational techniques with practical industrial challenges, driving innovation in smart manufacturing. His leadership in industrial data analytics and AI-driven process control has positioned him as a key contributor to the field, influencing both academic research and industry practices.

🏅 Awards and Recognition

Le Yao has been recognized with numerous prestigious awards for his academic and research contributions. He received the 2020 Outstanding Dissertation Award from the Chinese Institute of Electronics and was named an Outstanding Graduate by Zhejiang University and Zhejiang Province in 2019. His research excellence has been acknowledged through multiple National Scholarships for Ph.D. students (2017, 2018). His work has been featured in top-tier conferences, earning him Best Paper Finalist awards at IEEE DDCLS (2018) and China Process Control Conferences (2016, 2017, 2018). These accolades reflect his outstanding contributions to industrial process modeling, soft sensing, and causal analysis. His innovative approaches to quality prediction and fault diagnosis have significantly impacted the field, earning him recognition from both academic institutions and industry leaders. Le Yao’s commitment to excellence continues to drive his research endeavors, making him a prominent figure in data-driven industrial applications.

🌍 Research Skills On Computer Science

Le Yao’s research expertise spans multiple domains, including data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. He specializes in deep learning techniques for process optimization and interpretable modeling to enhance decision-making in industrial environments. His work on scalable and distributed parallel modeling has introduced novel methodologies for handling big process data efficiently. His causal analysis research integrates process knowledge with data-driven approaches, improving anomaly detection and fault diagnosis. He has developed advanced deep learning models incorporating hierarchical extreme learning machines and probabilistic latent variable regression. His research contributions have been implemented in real-world industrial applications, optimizing quality prediction and process control. With a strong foundation in control engineering, statistics, and artificial intelligence, Le Yao continues to advance the field by bridging theoretical research with industrial needs.

📖 Publication Top Notes

  • Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application

    • Authors: L Yao, Z Ge
    • Citation: 295
    • Year: 2017
    • Journal: IEEE Transactions on Industrial Electronics, 65 (2), 1490-1498
  • Big data quality prediction in the process industry: A distributed parallel modeling framework

    • Authors: L Yao, Z Ge
    • Citation: 108
    • Year: 2018
    • Journal: Journal of Process Control, 68, 1-13
  • Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure

    • Authors: B Shen, L Yao, Z Ge
    • Citation: 102
    • Year: 2020
    • Journal: Control Engineering Practice, 94, 104198
  • Scalable semisupervised GMM for big data quality prediction in multimode processes

    • Authors: L Yao, Z Ge
    • Citation: 90
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (5), 3681-3692
  • Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data

    • Authors: L Yao, Z Ge
    • Citation: 80
    • Year: 2016
    • Journal: IEEE Transactions on Automation Science and Engineering, 14 (1), 126-138
  • Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2019
    • Journal: Engineering Applications of Artificial Intelligence, 81, 450-465
  • Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2017
    • Journal: Control Engineering Practice, 61, 72-80
  • Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction

    • Authors: L Yao, Z Ge
    • Citation: 61
    • Year: 2020
    • Journal: IEEE Transactions on Industrial Informatics, 17 (6), 3782-3792
  • Online updating soft sensor modeling and industrial application based on selectively integrated moving window approach

    • Authors: L Yao, Z Ge
    • Citation: 60
    • Year: 2017
    • Journal: IEEE Transactions on Instrumentation and Measurement, 66 (8), 1985-1993
  • Parallel computing and SGD-based DPMM for soft sensor development with large-scale semisupervised data

    • Authors: W Shao, L Yao, Z Ge, Z Song
    • Citation: 53
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (8), 6362-6373