Massimiliano Patacchiola
CEO & Founder at Fondazione Randstad AI & Humanities
Dubai, Dubai, United Arab Emirates
Summary
🤩
Rockstar🎓
Top SchoolMassimiliano Patacchiola is an AI researcher-turned-founder with 15 years' experience building efficient machine learning and computer vision systems, from robotics and SLAM to billion-user iris recognition at Tools for Humanity. He now leads Sapiente Education, a selective online school pairing elite mentors and ambitious students, while advising initiatives that fuse AI with the humanities at Randstad and the Human Economic Forum. His academic work spans postdoctoral research at Cambridge and Edinburgh on few-shot, self-supervised and Bayesian methods, with publications including a NeurIPS spotlight, and practical reinforcement-learning code shared on GitHub. Comfortable moving between hand-on engineering, academic rigour, and educational entrepreneurship, he brings a rare combination of large-scale production vision and deep probabilistic ML expertise.
11 years of coding experience
11 years of employment as a software developer
Master's degree, Neuroscience, Master's degree, Neuroscience at Sapienza Università di RomaDoctor of Philosophy - PhD, Machine Learning and Robotics, Doctor of Philosophy - PhD, Machine Learning and Robotics at University of Plymouth
English, French, Italian
Github Skills (11)
machine-learning-algorithms10
python10
reinforcement-learning10
numpy9
implement9
algorithm9
algorithms9
actor-critic8
neural-network7
artificial-neural-networks7
deep-reinforcement-learning7
Programming languages (4)
Github contributions (5)
mpatacchiola/dissecting-reinforcement-learning
Dec 2016 - Sep 2020
Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
Role in this project:
ML Engineer
ML EngineerContributions:79 commits, 8 PRs, 64 pushes in 3 years 10 months
Contributions summary:Massimiliano contributed Python code, including files related to policy iteration, transition matrix generation, and value iteration, which are core concepts in reinforcement learning. Their contributions demonstrate a focus on implementing and experimenting with algorithms like policy iteration and value iteration within the context of a gridworld environment. The code includes functionalities for policy evaluation and improvement, demonstrating practical engagement with reinforcement learning principles. The user also added code to include plotting graphs and visualizing the results.
pythonreinforcement-learningdeep-reinforcement-learningmarkov-chaintemporal-differencing-learning
mpatacchiola/Y-AE
Mar 2019 - Oct 2020
Official Tensorflow implementation of the paper "Y-Autoencoders: disentangling latent representations via sequential-encoding", Pattern Recognition Letters (2020)
Contributions:16 commits, 6 pushes in 1 year 6 months
autoencoderpattern-recognitiontensorflowautoencodersmnist
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