I am Idriss JAIRI, a Ph.D. student in machine learning at the University of Lille (CRIStAL Laboratory). Having a background in software engineering and data science.
Education
- Ph.D. in Machine Learning, University of Lille (CRIStAL Laboratory), Lille, France, 2022-Now
- Supervisor: Prof. Hayfa Zgaya
- Thesis: Design and development of intelligent performance indicators to help preserve the environment based on the so-called “water-air-soil” strategy
- M.S. in Computer Science and Technology, Southwest Petroleum University, Chengdu, China, 2019-2022
- Supervisor: Dr. Yu Fang
- Final year thesis: Neural transfer learning for soil liquefaction tests
- B.S. in Computer Science, Faculty of Sciences Ibn ZOHR, Agadir, Morocco, 2018-2019
- DUT (Diplôme universitaire de technologie) in Computer Science, Higher School of Technology, Agadir, Morocco, 2016-2018
Research experience
- 2022-10 to Now: PhD Student
- University of Lille (CRIStAL Laboratory)
- Design and development of intelligent performance indicators for environmental preservation support based on the “water-air-soil” strategy
- 2020-09 to 2022-06: Research Assistant (Machine Learning Researcher)
- Southwest Petroleum University
- Duties included: Exploring and Developing new techniques to evaluate the liquefaction of soil using machine learning techniques
Internships
- 2019-02 to 2019-04: Internship - Web Developer
- Vala-orange
- Duties included: Developing web application and SEO (Search Engine Optimization)
- 2018-03 to 2018-06: Internship - Web Developer
- WOHLSTANDFURALLE SARL
- Duties included: Developing E-commerce web application
Skills
- Areas of Expertise:
- Machine learning
- Deep learning
- Data visualization, Data preparation
- Data science, Statistics
- Software engineering
- Web development
- Problem-solving
- Programming Languages:
- C, C++, JavaScript, HTML/CSS, Python, Java, PHP, SQL, Latex
- Frameworks/Systems:
- MVC, Django, Bootstrap, WordPress, VueJS
Highlighted Publications
Enhancing particulate matter risk assessment with novel machine learning-driven toxicity threshold prediction
Idriss Jairi, Amelle Rekbi, Sarah Ben-Othman, Slim Hammadi, Ludivine Canivet, and Hayfa Zgaya-Biau
Enhancing air pollution prediction: A neural transfer learning approach across different air pollutants
Idriss Jairi, Sarah Ben-Othman, Ludivine Canivet, and Hayfa Zgaya-Biau
Explainable based approach for the air quality classification on the granular computing rule extraction technique
Idriss Jairi, Sarah Ben-Othman, Ludivine Canivet, and Hayfa Zgaya-Biau
Neural transfer learning for soil liquefaction tests
Fang, Y., Jairi, I., & Pirhadi, N. (2023). Neural transfer learning for soil liquefaction tests. Computers & Geosciences, 171, 105282.
Application of Logistic Regression Based on Maximum Likelihood Estimation to Predict Seismic Soil Liquefaction Occurrence
Jairi, I., Fang, Y., & Pirhadi, N. (2021). Application of logistic regression based on maximum likelihood estimation to predict seismic soil liquefaction occurrence. Human-Centric Intelligent Systems, 1(3-4), 98-104.
Teaching
Supervision
- Master M1 Project: Machine learning-based decision support system in resuming driving with visual field deficits.
Grants and Awards
- Sichuan Provincial Government Scholarship for Foreign Students in Southwest Petroleum University, 2021-2022
- Sichuan Provincial Government Scholarship for Foreign Students in Southwest Petroleum University, 2020-2021