I am responsible of many things, including building the company design system, architecting, building and maintaining next-gen web-apps for our products, taking part at the recruiting process, interviewing and evaluating candidates, mentoring junior colleagues in their activities and speaking at internal meet-ups.
I am responsible of many things, including architecting, building and maintaining next-gen web-apps for our products, taking part at the recruiting process, interviewing and evaluating candidates, mentoring junior colleagues in their activities and speaking at internal meet-ups.
I was responsible to build and maintain the administrative portal for our recommender engine.
I developed an HTML5/JS audio player running on nw.js with waveform visualization and near real-time audio filters.
Web-app for monitoring the spreading of COVID-19 with daily statistics and interactive graphs.
React library written in TypeScript that adds reveal animations using the Intersection Observer API to detect when the elements appear in the viewport. Animations are internally provided by Emotion and implemented as CSS Animations to benefit from hardware acceleration.
A library for React apps that decorates the DOM with custom attributes that can be used to uniquely indentify elements in a page. The main use case is for E2E testing using tools like Cypress or Selenium.
Design and development of an automatic fake reviews generator based on neural networks.
Design and development of an automated tool to systematically alter JavaScript code for testing purposes.
Consumer reviews are an important information resource for people and a fundamental part of everyday decision making. Product reviews have an economical relevance which may attract malicious people to commit a review fraud, by writing false reviews. In this work, we investigate the possibility of generating hundreds of false restaurant reviews automatically and very quickly. We propose and evaluate a method for automatic generation of restaurant reviews tailored to the desired rating and restaurant category. A key feature of our work is the experimental evaluation which involves human users. We assessed the ability of our method to actually deceive users by presenting to them sets of reviews including a mix of genuine reviews and of machine generated reviews. Users were not aware of the aim of the evaluation and the existence of machine-generated reviews. As it turns out, it is feasible to automatically generate realistic reviews which can manipulate the opinion of the user.