Yousri Kessentini | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Yousri Kessentini | Artificial Intelligence | Best Researcher Award

Senior Researcher at Digital research center of Sfax, Tunisia

Assoc. Prof. Dr. Yousri Kessentini is a computer science researcher and Associate Professor at the Digital Research Center of Sfax, Tunisia, where he leads the DeepVision research team. He holds a Ph.D. from the University of Rouen, France, and specializes in deep learning, computer vision, and document image analysis. Dr. Kessentini has coordinated numerous national and international research projects and has received several awards, including honors from NVIDIA and the National Academy of Engineering. He is a certified Deep Learning instructor and an active contributor to the scientific community through publications, supervision, and editorial roles.

Publication Profile 

Scopus

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Educational Background 

Dr. Kessentini earned his Habilitation in Computer Science from the University of Sfax in 2021. He holds a Ph.D. in Computer Science (2006–2009) and a DEA (postgraduate diploma) in Computer Science (2004) from the University of Rouen, France. He also obtained an engineering diploma in computer science from ENIS in 2003 and completed his secondary education with a Scientific Baccalaureate in Mathematics in 1998.

Professional Experience

Dr. Kessentini has accumulated rich academic and industrial experience over two decades. Since 2022, he has served as Associate Professor and Head of the DeepVision research team at CRNS. From 2017 to 2021, he was a senior researcher at the same center. Between 2013 and 2017, he was an assistant professor at ISIMA University of Monastir. He also held postdoctoral and graduate assistant roles in France, including at ITESOFT/LITIS and the University of Rouen. Since 2018, he has been a certified instructor and ambassador of the NVIDIA Deep Learning Institute, reflecting his leadership in AI education and training.

Research Interests

His research spans a variety of deep learning applications, including document image recognition, handwritten text analysis, multi-script OCR, generative models, and satellite image fusion. Dr. Kessentini also explores the intersection of AI with healthcare, smart cities, and industrial automation. His recent projects involve federated learning for medical imaging, vehicle identity recognition, Arabic script analysis, and human action recognition through remote sensing and video surveillance.

Awards and Honors

Dr. Kessentini has received numerous accolades for his contributions to AI research and innovation. In 2025, he was selected for the prestigious U.S.-Africa Frontiers of Science, Engineering, and Medicine Symposium by the U.S. National Academy of Engineering. He ranked first in Tunisia’s national recruitment competition for associate professors in 2022. He received best student paper awards at ICPR 2020 and MedPRAI 2020 and earned a Jury Recognition Award in Tunisia’s national innovation competition in 2019. His research excellence was also recognized by NVIDIA with a GPU Grant in 2018, the same year he was certified as an official instructor and ambassador.

Publications 

Title: Information extraction from multi-layout invoice images using FATURA dataset

Year: 2025

Title: STF-Trans: A Two-stream SpatioTemporal Fusion Transformer for Very High Resolution Satellites Images

Year: 2024

Title: MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition

Year: 2023

Title: Spectral-Temporal Fusion of Satellite Images Via an End-to-End Two-Stream Attention With an Effective Reconstruction Network

Year: 2023

Title: Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwriting Recognition

Year: 2022

Conclusions

Assoc. Prof. Dr. Yousri Kessentini stands out as a leading figure in the fields of artificial intelligence and computer vision. His consistent contributions to scientific innovation, mentorship, and international collaboration have had a significant impact across academia and industry. His work demonstrates not only technical excellence but also a strong commitment to applying AI for societal and industrial benefit. With an impressive track record of publications, project leadership, and academic service, he is highly deserving of recognition in competitive research and innovation awards.

Sushil Kumar | Machine Learning | Best Researcher Award

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Dr. Sushil Kumar | Machine Learning | Best Researcher Award

Assistant Professor at Central University of Haryana, India

Dr. Sushil Kumar is an Assistant Professor in the Department of Computer Science and Engineering at the Central University of Haryana, having joined on December 2, 2022. With a rich experience of 19 years in teaching, he specializes in Information Retrieval, Machine Learning, and Distributed Computing. Dr. Kumar holds a B.Tech, M.Tech, and Ph.D. in Computer Science and Engineering. He has published 7 papers in international journals and 1 book chapter, and has guided 16 Master’s students in their research. He has actively participated in 25 seminars and conferences, and organized 5 academic events. In addition, he has been recognized with the Youth Red Cross Award from the Honorable Governor of Haryana for 2016-17 and 2019-20. Currently, he also serves as the NBA Co-ordinator and NAAC Co-ordinator at the university.

Publication Profile : 

Google Scholar

Education 🎓

Dr. Sushil Kumar holds a B.Tech, M.Tech, and Ph.D. in Computer Science and Engineering, equipping him with a solid foundation in the field of technology and research.

Professional Experience💼

Assistant Professor at Central University of Haryana since 02-12-2022
With 19 years of teaching experience, Dr. Sushil Kumar has been dedicated to nurturing young minds in the area of computer science. His expertise in Information Retrieval, Machine Learning, and Distributed Computing has shaped his teaching methodology. While his focus remains on academia, he has not been involved in industry work yet. He has also taken up additional responsibilities as NBA Co-ordinator and NAAC Co-ordinator, ensuring quality assurance and accreditation standards in the department.

Research Interests 🔬

🔍 Information Retrieval
🤖 Machine Learning
🌐 Distributed Computing

Dr. Sushil Kumar’s research interests are focused on the areas of Information Retrieval, where he aims to improve search and data retrieval systems, Machine Learning, and the development of efficient algorithms for Distributed Computing systems.

Publications Top Notes 📚

  1. Kumar, S., Aggarwal, M., Khullar, V., Goyal, N., Singh, A., & Tolba, A. (2023). Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification. Agriculture, 13(5), 23.
  2. Kumar, S., & Bhatia, K. K. (2020). Semantic similarity and text summarization-based novelty detection. SN Applied Sciences, 2(3), 332.
  3. Kumar, S., & Chauhan, N. (2012). A context model for focused web search. International Journal of Computer Technology, 2(3).
  4. Gupta, C., Khullar, V., Goyal, N., Saini, K., Baniwal, R., Kumar, S., & Rastogi, R. (2023). Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram. Diagnostics, 14(1), 43.
  5. Kumar, S., & Bhatia, K. K. (2019). Clustering-based approach for novelty detection in text documents. Asian Journal of Computer Science and Technology, 8(2), 116-121.
  6. Dasari, K., Srikanth, V., Veramallu, B., Kumar, S. S., & Srinivasulu, K. (2014). A novelty approach of symmetric encryption algorithm. Proceedings of the International Conference on Information Communication and Embedded Systems (ICICES).
  7. Kumar, S., & Anand, S. (2006). Implementing Shared Data Services (SDS): A Proposed Approach. 2006 IEEE International Conference on Services Computing (SCC’06), 365-372.
  8. Singh, S., Kundra, H., Kundra, S., Pratima, P. V., Devi, M. V. A., Kumar, S., & Hassan, M. (2024). Optimal trained ensemble of classification model for satellite image classification. Multimedia Tools and Applications, 1-22.
  9. Kumar, S., & Bhatia, K. K. (2018). Document-to-Sentence Level Technique for Novelty Detection. In Speech and Language Processing for Human-Machine Communications: Proceedings (pp. xx-xx).
  10. Chawla, M., Panda, S. N., Khullar, V., Kumar, S., & Bhattacharjee, S. B. (2024). A lightweight and privacy-preserved federated learning ecosystem for analyzing verbal communication emotions in identical and non-identical databases. Measurement: Sensors, 34, 101268.
  11. Kumar, S. S. (2023). System Oriented Social Scrutinizer: Centered Upon Mutual Profile Erudition. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 2007–2017.
  12. Kumar, S. (2021). Design of novelty detection techniques for optimized search engine results. JC Bose University.
  13. Ishuka, S. K., & Bhatia, K. K. (2019). A Novel Approach for Novelty Detection Using Extractive Text Summarization. Journal of Emerging Technologies and Innovative Research, 6(6), 141-154.
  14. Pooja, K. K. B., & Kumar, S. (2019). Hashing and Clustering Based Novelty Detection. SSRG International Journal of Computer Science and Engineering, 6(6), 1-9.
  15. Kumar, S., & Bhatia, K. K. (2019). Clustering Based Approach for Novelty Detection in Text Documents. Asian Journal of Computer Science and Technology, 8(2), 121-126.