Resume

Education

• 2019 - 2022 : Ph.D in AI, Applied Mathematics and Computer Science at Institut Clément Ader and ISAE-SUPAERO (Toulouse, France).
• 2017 - 2019 : General Engineering Degree, specialization in Data Science and Finance, École des Mines de Saint-Etienne (French National Graduate Engineering School).
• 2016 - 2017 : M.S. in Applied Mathematics to Statistics, Computer Science and Actuarial Science, Paris Dauphine University PSL.
• 2016 - 2017 : B.S. in Applied Mathematics and Computer Science, Paris Dauphine University PSL.

Work experience

• 2019 - 2022 : Ph.D. Student in Artificial Intelligence and Applied Mathematics (Aeronautics), Institut Clément Ader and ISAE-SUPAERO (Toulouse, France)
  • Research topic : “Can Deep Self-Supervised Learning improve Few-Shots learning in Prognostics ?” (Proof-of-Concept)
  • Programming : Linux (Ubuntu), Python, API Rest with Flask, LaTeX, High Performance Computing (HPC), Git, Shell Scripts
  • Part-time Lecturer (ISAE-Supaero, Department of Mathematical Modeling & AI in INSA Toulouse) : probabilities, statistics and programming (Python, R).
  • Training and supervising 2 students (research project 2nd year ISAE-Supaero) and 1 student (end-of-study internship) in the context of my thesis (Deep Learning research and Python programming).
  • Supervisors: Prof. Christian Gogu, Prof. Rob Vingerhoeds and Prof. Michel Salaün.
• April 2019 - September 2019 : Société Générale, Intern, Data Scientist – Algorithmic Forex Trading (Paris, France)
  • Developed forecasting Machine/Deep Learning model for price fluctuations in the Forex market (Python)
  • Coaching in Python programming for various professionals of the bank.
• September 2018 - March 2019 : Mazars, ‘Research Project’ in parallel with the academic curriculum, Data Scientist (Lyon, France)
  • Compared and benchmarking of the most advanced Machine Learning techniques for anomaly detection in unsupervised manner (Python).
  • Developed a robust deep learning method for the anomaly detection in a semi-supervised manner (Python).
• June 2018 -August 2018 : Seoul National University, Research Intern in Deep Learning, (Seoul, South Korea)
  • Filtered noisy financial time series using Singular Spectrum Analysis (Python)
  • Explored the most cutting edge Machine and Deep Learning algorithms for forecasting of stock prices (on Python), using also Sentiment Analysis (NLP)
  • Developed automatic trading algorithm using neural networks (deep reinforcement learning, equity curve)

Skills

• Languages : French (Native), Arabic (Native), English (Fluent), Spanish (Limited professional proficiency)
• Programming Languages : Python (Pytorch, Tensorflow, Scikit-learn, Flask, BigQuery, PySpark, etc.), R, Git, SQL, C, Java, VBA Excel, PowerBI
• Operating Systems : Linux (Ubuntu), Windows, Mac

Publications

• Akrim, A., Gogu, C., G. de Nerville, T., Strähle, P., Waffa Pagou, B., Salaün, M., & Vingerhoeds, “"A Framework for Generating Large Data Sets for Fatigue Damage Prognostic Problems,"” 2022 IEEE International Conference on Prognostics and Health Management (ICPHM), Detroit (Romulus), MI, USA, 2022, pp. 25-33, doi: 10.1109/ICPHM53196.2022.9815692.

• A. Akrim, C. Gogu, R. Vingerhoeds and M. Salaün, "Remaining Useful Life prediction with a Deep Self-Supervised Learning Approach." in: eccomas2022. URL : https://www.scipedia.com/public/Akrim_et_al_2022a

• Akrim, A., Gogu, C., Vingerhoeds, R. and Salaün, M. (2023) “"Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem"”, Engineering Applications of Artificial Intelligence, doi : doi.org/10.1016/j.engappai.2023.105837
• Akrim, A. “"Prediction of the remaining useful life of aircraft components with deep learning methods,"”, Thesis.

Talks

• 2022 IEEE International Conference on Prognostics and Health Management (ICPHM), Detroit (Michigan), United States, 6-8th June 2022
• The 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) Congress 2022, Oslo, Norway, 5-9th June 2022

Teaching

• 2020 - 2022 : ISAE-SUPAERO, Complex Systems Engineering Department, Part-time lecturer, Toulouse, France.
• 2019 - 2022 : INSA Toulouse, Department of Mathematical Modeling & AI, Part-time lecturer, Toulouse, France.

Courses I have taught include:

• Statistics and Probability
• Machine Learning
• Applied Mathematics and Optimization
• Signal Processing
• Linear programming
• Programming in Python, R and Matlab