Francesco Pase
I obtained my PhD at the University of Padova under the supervision of prof. Michele Zorzi, with the thesis "On the Role of Information in Distributed Learning", investigating the interplay between information theory and machine learning. At NEWTWEN, I'm now working on physics-based/informed machine learning, bringing AI to real applications in the fields of engineering and science. Here you can find my CV.
Experience
Oct. 2023 - Present NEWTWEN (Padova, IT)
Lead AI Research Engineer: I'm leading the AI division of NEWTWEN to apply AI to solve physical/engineering problems, performing research and developing models to be included in the company's core technology.
Jun. 2023 - Aug. 2023 Nokia Bell Labs (Cambridge, UK)
Research Intern: Performing research on distributed and resource-constrained learning within the Device Intelligence Lab, under the supervision of Prof. Fahim Kawsar. Recognized as top 3 Bell Labs interns worldwide.
May 2023 - Present Lead The Future
Mentor at Lead The Future: LeadTheFuture is a mentorship nonprofit in Science and Engineering where top talent comes to accelerate their ideas and careers, surrounded by a world-class community of mentors and mentees rooting for you and your success.
Feb. 2022 - Aug. 2022 Imperial College London (London, UK)
Visiting Researcher: Joined the Information Processing & Communications Lab, within the Intelligent Systems and Networks group, uder the supervision of Prof. Deniz Gunduz, where I do research on Reinforcement/Federated Learning and Information Theory.
Oct. 2020 - Sept. 2023 University of Padova
PhD Student: Conducting research on machine learning and wireless communications with the SIGNET research group. I'm also involved in different projects on Intelligent Internet of Things and Deep Reinforcement Learning for information dissemination in vehicular's networks (with Toyota North America).
Jul. 2020 - Oct. 2020 InstaDeep Ltd
Research Intern: At InstaDeep, London (UK), I conducted research on Deep Reinforcement Learning, in particular on Maximum Entropy RL and on the Control as Inference framework.
January 2020 - June 2020 University of Padova
Postgraduate Researcher: Conducting research on machine learning and wireless communications with the SIGNET research group. I'm also involved in different projects on Intelligent Internet of Things and Deep Reinforcement Learning for information dissemination in vehicular's networks (with Toyota North America).
February 2019 - July 2019 Learn To Forecast - L2F (Lausanne, CH)
Master Thesis Project: I developed my master thesis in collaboration with Learn to Forecast, a machine learning startup located at the EPFL Innovation Park, Lausanne (Switzerland). By exlpoiting diffusion processes on graphs and simplicial complexes, I developed a novel method to embed nodes, edges and higher order cliques into low-dimensional spaces.
September 2018 - July 2019 École polytechnique fédérale de Lausanne (EPFL)
SEMP Scholarship Student: Exchange program (1 year) at the Ecole Polytechnique Fédérale de Lausanne - EPFL, Lausanne (Switzerland), where I completed master courses on Machine Learning, Information Theory, Graph and Distributed algorithms.
Sept. 2017 - Sept. 2019 University of Padova
MSc. in Telecommunications Engineering: Brand new master degree in ICT Engineering where I studied Wireless
Communications and Networks, Machine Learning and Data Analysis, Stochastic Processes and Network Science. Graduated Summa cum Laude.
Sept. 2014 - Sept. 2017 University of Padova
BSc. in Information Engineering: Completed theoretical courses on Mathematical Analysis, Linear Algebra, Physics,
Probability and fundamentals of Computer Science, Electronics and Control Theory. For my bachelor thesis I studied stochastic models for DNA sequences analysis. Final mark 110/110.