Software Engineer.

👋 I am Anass Anhari Talib, a Software Engineer living in Manresa, Barcelona, Spain 🌍. I am a motivated person willing to take advantage of any opportunity and constantly learn.

I am currently focused on Machine Learning 🧠 and Data Science Research 📊, where I enjoy solving problems that don't have straightforward solutions. It's amazing to be able to obtain valuable insights from complex data, finding patterns and relationships in large and small datasets, and manipulate it in ways that regular methods cannot.

🔗🚀 Feel free to connect with me!

Experience

  1. Company
    Universitat Politècnica de Catalunya
    Role
    Assitant Professor
    Date
  2. Company
    Deloitte
    Role
    Developer Analyst
    Date
  3. Company
    Universitat Politècnica de Catalunya
    Role
    FLAIR AI/ML Research Project
    Date
  4. Company
    Universitat Politècnica de Catalunya
    Role
    5G Network Setup
    Date
  5. Company
    Freelance
    Role
    3D Printing
    Date
Resume | CVCover Letter

Flair AI/ML Research Project

A European consortium project on which I collaborate. In many AI use cases, the training stage is done on a central server, meaning that data is shared. Hence, we are developing a solution based on Federated Learning (without sharing any kind of data) integrating the VEDLIoT (also an EU-funded project) toolchain into our use case (voice recognition) within a 5G network setup.

5G Network

A project for the UPC (Universitat Politècnica de catalunya) for implementing, configuring and running a 5G end-to-end setup using SDRs and OpenAirInterface5G, allowing the UPC to conduct a wide range of experiments/studies on the 5G network.

Predicting University Enrollments with Machine Learning

So far, the procedure for university enrollments it has been done manually. In addition, it has always required a great effort and experience on the part of the team that manages them. We are facing a complex problem, that is, if we wanted to automate this procedure, we could not apply a traditional approach nor any generic rule or algorithm to determine whether or not a student will enroll because of the behavior of each student is quite unpredictable. For this reason, it is proposed to apply Machine Learning with the purpose of generating and analyzing various predictive models based on the previous academic history of all students.