Prof. Lotfi Chaari | Artificial Intelligence | Best Researcher Award

Institut National Polytechnique de Toulouse (Toulouse INP),France🎓

Education 🎓:

• 2017: Habilitation à Diriger la Recherche (HDR), Toulouse INP, France
• 2010: PhD in Signal and Image Processing, University of Paris-Est Marne-la-Vallée, France
• 2008: Master of Science in Telecommunication, SUP’COM, Tunisia
• 2007: Telecommunication Engineering Degree, SUP’COM, Tunisia

Work Experience 💼:

• 2024 – Present: Full Professor, Toulouse INP, France (Ipst-Cnam)
• 2012 – 2024: Associate Professor, Toulouse INP, France (Ipst-Cnam)
• 2010 – 2012: Post-doctoral Fellow, INRIA Grenoble-Rhône Alpes, France

 

Skills 🔍:

• Artificial Intelligence & Machine Learning: Proficient in deep learning, anomaly detection, and Bayesian optimization.
• Signal & Image Processing: Expertise in biomedical imaging, remote sensing, and pattern recognition.
• Optimization: Skilled in variational and inverse problem-solving techniques for image enhancement and restoration.

Awards and Honors 🏆:

• 2023: HOPE Best Workshops Paper Award
• 2022: Nutrients Best Paper Award
• 2019: Elevated to IEEE Senior Member status

Memberships 🤝:

• Editorial Positions: Associate Editor for Digital Signal Processing Journal and IEEE Open Journal of Signal Processing
• Conference Leadership: Founder and General Chair, International Conference on Digital Health Technologies (ICDHT)
• Technical Program Committee Member: Contributed to renowned conferences like IEEE ICIP, IEEE ICASSP, and ISIVC

Teaching Experience 👩‍🏫:

Dr. Chaari is a passionate educator who has developed advanced courses in signal processing, machine learning, and artificial intelligence. He actively supervises PhD students and promotes interdisciplinary research.

Research Focus 🔬:

Dr. Chaari’s research spans various cutting-edge fields, including biomedical signal processing, remote sensing, and anomaly detection. He has spearheaded multiple collaborative projects, such as MSrGB (Metabolic Shift in Radioresistance of Glioblastoma) and BayesQML (Bayesian Optimization for Quantum Machine Learning), pushing the boundaries of AI in medical and engineering applications.

📚 Publilcation 

• Title: “mid-DeepLabv3+: A Novel Approach for Image Semantic Segmentation Applied to African Food Dietary Assessments”
  Topic: Semantic segmentation for dietary assessments
  Year: 2023
  Journal: Sensors
  DOI: 10.3390/s24010209
• Title: “Non-smooth Bayesian learning for artificial neural networks”
  Topic: Bayesian learning in neural networks
  Year: 2022
  Journal: Journal of Ambient Intelligence and Humanized Computing
  DOI: 10.1007/s12652-022-04073-8
• Title: “Bayesian Optimization Using Hamiltonian Dynamics for Sparse Artificial Neural Networks”
  Topic: Bayesian optimization for sparse neural networks
  Year: 2022
  Conference: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
  DOI: 10.1109/isbi52829.2022.9761469
• Title: “A Convolutional Neural Network for Artifacts Detection in EEG Data”
  Topic: CNN for detecting artifacts in EEG data
  Year: 2022
  Source: Lecture Notes in Networks and Systems
  DOI: 10.1007/978-981-16-7618-5_1
• Title: “Bayesian Optimization for Sparse Artificial Neural Networks: Application to Change Detection in Remote Sensing”
  Topic: Bayesian optimization for sparse neural networks in remote sensing
  Year: 2022
  Source: Lecture Notes in Networks and Systems
  DOI: 10.1007/978-981-16-7618-5_4
• Title: “Efficient Bayesian Learning of Sparse Deep Artificial Neural Networks”
  Topic: Bayesian learning in sparse deep neural networks
  Year: 2022
  Source: Lecture Notes in Computer Science
  DOI: 10.1007/978-3-031-01333-1_7
• Title: “Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning”
  Topic: Drowsiness detection using EEG and ECG data
  Year: 2021
  Conference: 2021 29th European Signal Processing Conference (EUSIPCO)
  DOI: 10.23919/eusipco54536.2021.9616046