Stefano Cappellini - Wizbii Stefano Cappellini a publié son profil professionnel sur Wizbii. S C

Stefano Cappellini

Software Engineer and Machine Learning Engineer

29 ans • Milan


I am a Machine Learning engineer and a Computer Scientist. After being a Software Engineer specialized in Web Development for some years, I decided to dive into the wonderful world of Machine Learning in order to combine my computer science background with my passion for Maths, artificial intelligence, probability and statistic. I am really passionate about Deep Learning, mostly applied to computer vision. I like building complex, smart systems and I am looking for interesting and challenging opportunities. See my full resume at:


Machine LearningDeep LearningTensorFlowConvolutional Neural NetworkSupervised LearningUnsupervised LearningReinforcement LearningPythonScikit-LearnScikit-ImagePandasNumPySciPyMatplotlibData MiningKnimeAlgorithmsMathsProbabilityStatisticsCryptographyWeb DevelopmentSoftware EngineerPhpJavaJavascriptPrologLispObject Oriented AnalysisObject-oriented DesignObject Oriented ProgrammingTestingDesign PatternsAntipatternsMySQLRefactoringSASSLESSGruntJS


Full Stack Web Developer

Développement informatique


Udacity Inc.

2017 - 2017 Diploma di specializzazione Mountain View, Santa Clara CountyÉtudes / Statistiques / Data, Développement informatiqueMachine Learning Engineer Nanodegree. Il mio progetto finale è stato pubblicato come technical report qui:

Università Degli Studi Di Milano - Bicocca

2016 - 2019 Laurea magistrale Milan, Metropolitan City of MilanDéveloppement informatique, Études / Statistiques / Data, Développement informatique, Infra / Réseaux / Télécoms

Università Degli Studi Di Milano - Bicocca

2010 - 2015 Laurea Milan, Metropolitan City of MilanDéveloppement informatiqueLaurea triennale in informatica, 110L, focus su Machine Learning

Mes qualités


Langues parlées

  • Italien

    Langue maternelle

  • Amharique


Expériences Extra Professionnelles

Technical Report

Facial Landmark Detection: A Modern Approach

The aim of this paper is to evaluate the impact of some of the research advances in Deep Learning on the everyday development and their usefulness to practitioners. A novel Convolutional Neural Network model is presented to solve a particular instance of the Facial Landmark Detection Problem, the one obtained by considering only five landmarks (Left and Right Eye center, Nose tip, Mouth Left and Right corner). The proposed solution is a single Convolutional Neural Network, with a conceptually simple architecture, employing some of the most recent techniques: inception modules, residual connections, batch normalization, dropout and ELU units. This solution has been compared with the work by Sun et al. and the obtained results clearly show the positive impact of those techniques: it is possible for a practitioner, without a previous domain knowledge and with an average machine, to quickly build a simple model, with competitive computational performance, and able to obtain predictive performance comparable with the ones obtained by more complex, state-of-the-art models presented in the last few years. In addition, the proposed solution has proven to be relatively cheap to train. This reveals the usefulness of the research advances to practitioners. See it here:

Personal Project

Saho: A Safer Url Shortener

An URL shortening service that puts the security first. Whenever a user clicks on a shortened link, a page is opened, showing where that particular link is pointing to and asking the user if it is ok to go there. In addition, it uses the MyWOT API, warning the user if a destination looks suspicious or dangerous. See it here:

Personal Project


Small JQuery utility that makes the handling of the resize event easier. In particular, (1) it is possible to register a callback which will be executed only during true resizing events (when the size of the window changes). The throttling limits the number of times that callback will be called during the resizing. (2) It is also possible to register a callback that will be executed when the resizing event is over. This is probably the greatest contribute of this utility. See it here:

Personal Project


Java utility that allows to explore, step by step, how various sorting algorithms (Bubble sort, Selection sort, Shaker sort, Insertion sort, Gnome sort, Merge sort, Quick Sort and Heap sort) work. During the execution, it shows how the various data structures are built, the various indexes used, the indexed cells, the swaps performed, the order of the recursive calls and all the additional memory needed by the various algorithms. See it here:

Mes souhaits

Un jour

I would like to apply my skills to real world problems that matter, for example in the medicine field, in the astronomy field or in the environmental engineering.