Gianluca Scarpellini

Sr Research Scientist at Genesis Molecular AI

New York, New York, United States
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Summary

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Rockstar
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Top School
Gianluca Scarpellini is a Senior Research Scientist based in New York with seven years of experience pushing the frontier of AI for scientific problems, particularly diffusion models and robotics. He holds a PhD and has contributed state-of-the-art graph-based diffusion formulations and practical pipelines that solved large puzzles and advanced event-camera 3D pose estimation and object detection. At Genesis Molecular AI he focuses on diffusion approaches for protein folding and molecular docking, scaling pretraining and optimizing inference with consistency models and distillation. His DeepMind internship produced an ICLR-accepted contribution that sped up offline policy evaluation on real robots by up to 10x, and he has a strong track record of shipping reproducible open-source tools (notably repositories from IIT-PAVIS). He combines rigorous academic research with hands-on engineering across distributed systems and multi-GPU training, and uniquely blends curiosity-driven robotics exploration with molecular AI.
code7 years of coding experience
job5 years of employment as a software developer
bookNanodegree, Deep Reinforcement Learning, Nanodegree, Deep Reinforcement Learning at Udacity
bookM.s. in Computer Science, Computer Vision, M.s. in Computer Science, Computer Vision at Università degli Studi di Milano-Bicocca
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Github Skills (110)

python10
simulator10
pose10
image-processing10
infrastructure10
deep-learning10
gpu10
robotics10
ai10
object-detection10
computer-vision10
deep-reinforcement-learning10
lightning10
artificial-intelligence10
data-science10

Programming languages (4)

C++Jupyter NotebookPythonEmacs Lisp

Github contributions (5)

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gianscarpe/blog

Sep 2020 - Nov 2024

Contributions:81 pushes, 1 branch in 4 years 1 month
gianscarpe/gianscarpe

Oct 2020 - Oct 2024

Contributions:49 pushes, 1 issue in 4 years 1 month
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Gianluca Scarpellini - Sr Research Scientist at Genesis Molecular AI