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
• 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
  • Supervisor: Dr. Yu Fang

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

Publications

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.

DPT-based seismic liquefaction triggering assessment in gravelly soils based on expanded case history dataset

Pirhadi, N., Wan, X., Lu, J., Fang, Y., Jairi, I., & Hu, J. (2022). DPT-based seismic liquefaction triggering assessment in gravelly soils based on expanded case history dataset. Engineering Geology, 311, 106894.

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.

Seismic gravelly soil liquefaction assessment based on dynamic penetration test using expanded case history dataset

Pirhadi, N., Hu, J., Fang, Y., Jairi, I., Wan, X., & Lu, J. (2021). Seismic gravelly soil liquefaction assessment based on dynamic penetration test using expanded case history dataset. Bulletin of Engineering Geology and the Environment, 80, 8159-8170.

Teaching

International Biomedical Engineering M2 - Machine Learning and Deep Learning Course

MIAS M2 - Machine Learning and Deep Learning Course

MIAS M1 - Data Science: Part 2

MIAS M1 - Machine Learning: Part 2

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