Hello, I'm
Walid Iguider.

Senior Data Scientist and researcher

© Iguider 2023 - Viale Trieste 30 Cagliari, CA 09123, Italy


MY MISSION IS TO Lead, DESIGN & DEVELOP Innovative AI/ML products

Walid Iguider, Ph.D. is a senior data scientist at Stackhouse (Spindox S.P.A. company) . He has about a decade of total experience, with more than 4 years in Data Science, more than 5 years in Software Engineering and 3 years of Agile Project Management. As a Senior Data Scientist, Dr. Iguider helps business owners reach their goals using AI based technologies. He leads, designs & develops innovative AI/ML products, based on Recommender Systems, Sentiment Analysis, Decision support systems...

Prior to joining Stackhouse, Dr. Iguider was a senior data scientist at Athlos. Earlier, he was a doctoral researcher at the Artificial Intelligence and Big Data Laboratory and collaborating with Abinsula Srl and the Data Science and Big Data Analytics research group of Eurecat.

Dr. Iguider has a master's degree in Software Quality from Sidi Mohamed Ben Abdellah University of Fez and completed his doctoral program in Computer Science, with a focus on Data Science, at the University of Cagliari. In particular, the goal of his Ph.D. was to levrage Machine Learning to support decision-making in sports remote coaching plateforms.

His experience includes collaborating with national and international companies in Emirates, France, Italy, Morocco, and Spain.
He is fluent in Arabic, English, French, and Italian.

Data Sciecnce 80%
Web Development 90%
Project Management 70%
Active Learning

Open source projects.

Active Learning for Text Classification in Python and Small-Text

This project consists of a notebook illustrating a simple example of how to perform Active Learning for text classification in python, leveraging on small-text (a library that provides state-of-the-art Active Learning for Text Classification) using sklearn models and the rotten tomatoes dataset. The main active learning loop queries the unlabeled pool and thereby decides which documents are labeled next. We then provide the labels for those documents and the active learner retrains the model. After each query, we evaluate the current model against the test set and save the result.

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Open source projects.

Convolutional Autoencoders for Image Reconstruction in Python and Keras

This project introduces an example of a convolutional (variational) autoencoder that reads an input image, encodes the image into a lower dimensional latent representation, then decodes the latent representation to reconstruct the imput image. The autoencoder is implemented using the Keras, and it is based on convolutional neural networks leveraging Conv2D layers in the encoder, and Conv2DTranspose layers in the decoder. The autoencoder is trained using the Fashion-MNIST dataset. Each image in this dataset is 28x28 pixels. For this reason, the input shape of the encoder was set to (28, 28, 1) as well as for the output shape of the decoder.

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Autoencoder Schema

Scoliosis Subject

Open source projects.

An Ensemble Deep Learning Approach to Classify Scoliosis and Healthy Subjects In Python and Keras

In this project we constructed and trained an Ensemble Neural Network to classify scoliosis and healthy subjects. The model predicts the probability that a subject suffers from AIS basd on the VRS data. The Ensemble Neural Network is implemented using the Keras, and it is averaging the prediction of 16 multi-layer perceptron (MLP) neural networks. Each neural network is made of two hidden (Dense) layers and each layer is composed of 64 neurones. The Ensemble Neural Network performs quite well achieving a balanced accuracy over 86%.

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  1. Industrial Ph.D.

    University of Cagliari
    2018 - 2022

    My mission during the Ph.D. was to support decision-making in eCoaching platforms using tailored state-of-the-art technologies. My research interests consisted of Algorithmic Fairness, Recommender Systems, and User Modeling. Besides accademic research work, I was contributing to the Product Management and Software Development of the U4FIT platform. My Ph.D. was supported by a grant from the National Operational Programme ESF-ERDF “Research and Innovation” 2014-2020, promoted by the Italian Ministry of Education, University and Research.

  2. Master of Science, Computer Software Engineering

    Sidi Mohamed Ben Abdellah University of Fez
    2012 - 2014

  3. Bachelor of Science, Mathematics and Computer Science

    Mohammed V University of Rabat
    2009 - 2012

Recent Experience

  1. Stackhouse

    Senior Data Scientist
    Apr 2022 - Continued

    As a Senior Data Scientist, I help business owners reach their goals using AI based technologies.

  2. Athlos

    Senior Data Scientist
    Oct 2022 - Apr 2022 (7 months)

    Reporting to the CTO, I led the development of web applications and machine learning based projects for privates and public administrations.

  3. AIBD Unica

    Doctoral Researcher
    Apr 2018 - Oct 2021 (3 years 7 months)

    In collaboration with Eurecat and Abinsula, while reporting to the CEO of U4FIT (the company of Abinsula's group that is focused on health platforms), my mission focused on the leadership, design and development of tailored state-of-the-art web and ML SaaS products to help personal trainers support and guide people towards a healthy and active lifestyle.


Recent Publications

Iguider, Walid; Machine Learning Models for Sports Remote Coaching PlatformsPh.D. Thesis2022Università degli Studi di Cagliari
Boratto, Ludovico; Carta, Salvatore; Iguider, Walid; Mulas, Fabrizio; Pilloni, Paolo; Fair performance-based user recommendation in eCoaching systemsUser Modeling and User-Adapted Interaction2022Springer Netherlands
Boratto, Ludovico; Carta, Salvatore; Iguider, Walid; Mulas, Fabrizio; Pilloni, Paolo; Predicting Workout Quality to Help Coaches Support SportspeopleProceedings of the Third International Workshop on Health Recommender Systems co-located with Twelfth ACM Conference on Recommender Systems (HealthRecSys’18)2018CEUR-WS
Iguider, Walid; Reforgiato Recupero, Diego; Language independent sentiment analysis of the shukran social network using apache sparkSemantic Web Evaluation Challenge2017Springer, Cham

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