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Raffaello Camoriano

Post Doc
Postdoctoral Researcher

Research Line

IIT@MIT

Contacts

Via Morego, 30, 16163 Genova
+390103536981
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Social profiles

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About

Research Interests

My research interests lie at the intersection between the Science and the Engineering of artificial learning systems.

In particular, my work is chiefly focused on devising theoretically-grounded Machine Learning methods capable of scaling up to large datasets, adapting to changing conditions in time, and efficiently interacting with their environments to learn faster and generalize better.

I also find Robotics to be an almost unlimited source of ‒ and a particularly well-suited testing platform for ‒ challenging and inspiring research problems, often calling for the development of novel Machine Learning methods. My work in this direction mainly spans visual object recognition and detection, system identification, and locomotion control.

Keywords: Large-scale Machine Learning, Robot Learning, Lifelong Learning, Kernel Methods, Reinforcement Learning

Short Bio

I received the B. Sc. cum laude in Computer Engineering (2011) and the M. Sc. with top grades in Robotics Engineering (2013) from the University of Genoa (UNIGE), Italy. During the B. Sc. and M. Sc., I was awarded a 5-year ISICT/ISSUGE scholarship for the highest-ranking EECS students in Genoa. I completed my Ph. D. at UNIGE in 2017, working at the Italian Institute of Technology (IIT) under the supervision of Prof. Giorgio Metta and Prof. Lorenzo Rosasco. I was awarded the IEEE Computational Intelligence Society Italy Section Chapter’s 2017 Best Ph. D. Thesis Award for my Thesis "Large-scale Kernel Methods and Applications to Lifelong Robot Learning".


Learn More

Download CV: https://www.iit.it/people/raffaello-camoriano?view=download&format=raw&docId=415

GitHub: https://github.com/raffaello-camoriano

Twitter: https://twitter.com/raf_camo

LinkedIn: it.linkedin.com/in/raffaellocamoriano/en

LCSL (IIT @ MIT) page: http://lcsl.mit.edu/#/people

Interests

Large-scale Machine Learning Robot Learning Lifelong Learning Kernel Methods Reinforcement Learning

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