Alberto Chiappa
Research Engineer at Flexion Robotics
Lausanne, Vaud, Switzerland
Summary
👤
Senior🎓
Top SchoolAlberto Chiappa is a research engineer and PhD candidate at EPFL focused on bringing human-level motor control to embodied AI through deep reinforcement learning and curriculum-based approaches. With a decade of experience spanning academic research, industry R&D leadership at Schindler, and a recent internship at Sony, he has published and won awards at top venues (NeurIPS, Neuron) and led prize-winning auto-bidding work at KDD/NeurIPS. He builds scalable learning systems for high-dimensional control and exploration, supervises many student projects, and now applies this expertise at Flexion Robotics. Notably, his work bridges practical deployments (patented elevator sensing app, ML MVPs for business) and cutting-edge generalist transformer agents for motor control.
10 years of coding experience
2 years of employment as a software developer
Master of Science (MSc), Computational Science and Engineering, Master of Science (MSc), Computational Science and Engineering at Ecole polytechnique fédérale de LausanneMaster of Science (MSc), Mathematical Engineering - Computational Science, Master of Science (MSc), Mathematical Engineering - Computational Science at Politecnico di Milano
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
stable-baselines10
robotics10
toolbox10
algorithms9
python9
reinforcement-learning9
machine-learning9
pytorch9
openai8
deep-learning6
biology4
computational-neuroscience3
git2
Programming languages (3)
Github contributions (5)
amathislab/dmap
Oct 2022 - Nov 2022
[NeurIPS 2022] DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body
Contributions:7 commits, 1 PR, 4 pushes in 1 month
pytorchpolicydmapdistributed-reinforcement-learningdeep-learning
amathislab/myochallenge
Mar 2023 - Dec 2024
[NeurIPS 2022, Neuron 2024] Winning code for the Baoding ball MyoChallenge at NeurIPS 2022
Contributions:2 pushes in 1 year 8 months
curriculum-learningmusclesreinforcement-learning
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