Diego Piccinotti

Diego Piccinotti has a diverse work experience in the field of Machine Learning. Diego is currently a Senior MLOps Analyst at Chattermill, a position they have held since 2022. Before that, they were a Mentee for LeadTheFuture Mentorship, a leading mentorship non-profit organization for students in STEM, from 2019 to 2022. Diego was among the few Italian students selected to be mentees, with an acceptance rate below 20%. From 2021 to 2022, they were an ML Engineer at ML cube, where they were responsible for the design, implementation and deployment of ML based solutions. Diego also participated in the development of the AD cube platform, working with AWS cloud technologies, Docker and Python. From 2019 to 2021, they were a Research Intern at Politecnico di Milano, where they implemented a Monte-Carlo Tree Search based agent for pit-stop strategic planning in Formula 1, built a ML-based model to predict drivers’ lap times in F1 races through regression, and extended the original Python code base for single-player games to work in a multi-agent setting. From 2018 to 2019, they were an Engineering Intern at Università degli studi di Parma, where they implemented a deep neural style transfer system based on CycleGAN, adapted and tested the system to work with Adidas on a proprietary dataset of images of shoes, and acquired experience in neural networks design and implementation with Keras and Tensorflow in Python.

Diego Piccinotti has an education history that includes a Master of Science in Computer Science and Engineering from Politecnico di Milano, obtained between 2018 and 2020, and a Bachelor of Science in Computer Engineering from Università degli Studi di Parma, obtained between 2015 and 2018. Diego has also obtained several certifications, including Train the Trainer from LinkedIn in October 2022, Deploying Machine Learning Models in Production from Coursera in September 2022, Machine Learning Data Lifecycle in Production from Coursera in August 2022, and Introduction to Machine Learning in Production from Coursera in June 2022.