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

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.

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.

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.

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.

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).

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. 

    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).

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. 


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. 

     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.

     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.