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

Orcid

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.

Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

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Assoc. Prof. Dr. Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

Associate Professor at Northwestern Polytechnical University, China

Dehong Gao is a distinguished expert in the fields of natural language processing, machine learning, and large language models. With over a decade of research and practical experience, he has made significant contributions to advertising technologies, multimodal AI models, and AI-driven e-commerce solutions. Gao currently leads groundbreaking work on multimodal pre-training models, including the award-winning 70B-FashionGPT model. As an associate professor at Northwestern Polytechnical University and a distinguished researcher at Zhejiang University of Technology, Gao continues to advance AI research, particularly in the areas of large language models, multimodal learning, and cross-lingual search. 🚀📚💡

Publication Profile : 

Scopus

Educational Background 🎓

  • Ph.D. in Computer Science from Hong Kong Polytechnic University (2014-2022), under the supervision of Li Wenjie.
  • Master’s in Automation from Northwestern Polytechnical University (2010).
  • Bachelor’s in Automation from Northwestern Polytechnical University (2007).

Professional Experience 💼

Dehong Gao’s career spans both academia and industry. He is currently an Associate Professor at the School of Cyberspace Security at Northwestern Polytechnical University. He also serves as a Distinguished Researcher at the Zhejiang University of Technology Artificial Intelligence Innovation Institute. Gao previously held the position of Senior Algorithm Expert (P8) at Alibaba Group, where he led a team of over 20 full-time algorithm engineers and contributed to the development of large-scale machine translation and AI-driven e-commerce solutions. As an expert in Alibaba AIR Project, Gao has been instrumental in technical breakthroughs related to large model technologies, fine-tuning multimodal models, and advancing AI-based search and advertising systems. 💻📈

Research Interests 🔬

Gao’s research interests are focused on:

  • Large Language Models (LLMs) and Multimodal Learning 🌐🤖
  • Natural Language Processing: Information retrieval, recommendation systems, sentiment analysis, and automated summarization 📑🔍
  • E-commerce AI: Developing search algorithms and multilingual representation learning for cross-border e-commerce applications 🌏🛒
  • Federated Learning and AI-driven personalization in business settings 🔒🤖

He has authored and co-authored several influential papers and has been a leading figure in the development of multimodal AI models for industries such as fashion, e-commerce, and healthcare. His work continues to push the boundaries of AI application in real-world environments. 🏆📚

Publications 📚

  1. Gao, D., Chen, K., Chen, B., et al. (2024). LLMs-based Machine Translation for E-commerce. Expert Systems with Applications, Volume 258 (SCI Zone 1, Top Journal).
  2. Chen, K., Chen, B., Gao, D., Dai, H., et al. (2024). General2Specialized LLMs Translation for E-commerce. The Web Conference (WWW), short paper (CCF-A).
  3. Shen, G., Sun, S., Gao, D., Yang, L., et al. (2023). EdgeNet: Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising. The 32nd ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
  4. Gao, D., Ma, Y., Liu, S., Song, M., Jin, L., et al. (2024). FashionGPT: LLM Instruction Fine-tuning with Multiple LoRA-adapter Fusion. Knowledge-Based Systems, Volume 299 (SCI, Top Journal).
  5. Chen, B., Jin, L., Wang, X., Gao, D., et al. (2023). Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval. Industry Track of The Web Conference (WWW), (CCF-A).
  6. Mei, X., Yang, L., Jiang, Z., Cai, X., Gao, D., et al. (2024). An Inductive Reasoning Model Based on Interpretable Logical Rules Over Temporal Knowledge Graphs. Neural Networks, Volume 174, Pages (SCI Zone 1, Top Journal).
  7. Liang, Z., Chen, B., Ran, Z., Wang, Z., Gao, D., et al. (2024). Self-Renewal Prompt Optimizing with Implicit Reasoning. The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) findings (CCF-A).
  8. Yang, Z., Gao, H., Gao, D., Yang, L., et al. (2024). MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction. The 18th ACM Conference on Recommender Systems (RecSys), (CCF-B).
  9. Zhang, X., Wang, D., Gao, D., Jiang, W., et al. (2022). Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN. The 31st ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
  10. Zhang, F., Zhang, Z., Gao, D., Zhuang, F., et al. (2022). Mind the Gap: Cross-lingual Information Retrieval with Hierarchical Knowledge Enhancement. The 36th AAAI Conference on Artificial Intelligence (AAAI), (CCF-A).

 

 

 

Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

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Mr. Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

PhD Student at Politecnico di Torino, Italy

Rafael Natalio Fontana Crespo is a dedicated and sociable Ph.D. student specializing in Computer and Control Engineering at Politecnico di Torino. With a strong academic background in mechatronics and practical experience in electrical energy analysis, he is passionate about tackling complex challenges through innovative solutions. 🌐💡

Publication Profile : 

Orcid

 

🎓 Educational Background :

Rafael is currently pursuing a Ph.D. in Computer and Control Engineering at Politecnico di Torino, Italy, since May 2022. He previously obtained a Master’s Degree in Mechatronic Engineering from the same institution, graduating with 110/110 cum laude in July 2022. His master’s thesis focused on designing and developing a distributed software platform for additive manufacturing. Rafael studied Electromechanical Engineering at the Universidad Nacional de Córdoba, Argentina, where he also completed a double degree program.

💼 Professional Experience :

Rafael gained practical experience during his internship at EPEC (Empresa Provincial de Energía de Córdoba) in Argentina, where he worked in the Statistics and Technical Department from May 2020 to May 2021. He was involved in analyzing thermal images of electrical components to prevent failures, contributing to the overall safety and efficiency of electrical systems.

📚 Research Interests : 

Rafael’s research interests lie at the intersection of computer engineering, control systems, and mechatronics, particularly focusing on additive manufacturing, machine learning applications in energy systems, and the optimization of neural networks.

📝 Publication Top Notes :

      1. Fontana Crespo, R.N., E. Patti, S. Di Cataldo, D. Cannizzaro. (2022). Design and Development of a Distributed Software Platform for Additive Manufacturing. Master’s Thesis, Politecnico di Torino.
      2. Fontana Crespo, R.N. (2023). Machine Learning in Energy Applications. Course Exam Paper, Politecnico di Torino.
      3. Fontana Crespo, R.N. (2023). IoT Platforms for Spatial Analytics in Smart Energy Systems. Course Exam Paper, Politecnico di Torino.
      4. Fontana Crespo, R.N. (2023). Optimized Execution of Neural Networks at the Edge. Course Exam Paper, Politecnico di Torino.
      5. Fontana Crespo, R.N. (2023). Adversarial Training of Neural Networks. Course Exam Paper, Politecnico di Torino.