Michelangelo Conserva
Research Scientist at Google
London, England, United States
Summary
👤
Senior🎓
Top SchoolMichelangelo Conserva is a research scientist specializing in remote sensing, climate and food-security forecasting with eight years of experience bridging academic rigor and production-grade research at Google and DeepMind. Currently finishing a PhD in Computer Science at Queen Mary University of London, he has delivered applied ML solutions ranging from high-resolution aerial imagery models to graph-based foodshed detection and forestry mapping from satellite data. His work combines time-series forecasting, graph neural networks and computer vision to tackle environmental and humanitarian problems, often moving prototypes toward real-world impact within Google research teams. Notably, he has repeatedly transitioned internship projects into sustained research roles, reflecting a rare ability to scale experiments into ongoing programs. Based in London, he pairs strong statistical training (UCL MSc, Sapienza background in statistics) with practical engineering at one of the world’s largest ML organizations.
8 years of coding experience
1 year of employment as a software developer
MSc Computational Statistics and Machine Learning, MSc Computational Statistics and Machine Learning at UCL EngineeringStatistics Economy Finance Insurance, Statistics, Statistics Economy Finance Insurance, Statistics at Sapienza Università di RomaDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Queen Mary University of London
Italian, English
Stackoverflow
31reputation
773reached
3answers
0questions
Github Skills (132)
language-server-protocol10
bayesian-methods10
playing10
anaconda10
pyqt10
ide10
spyder10
qt9
python-development9
statistics9
documented9
solar-energy9
deep-reinforcement-learning9
image-processing9
unreal-engine-49
Programming languages (8)
Github contributions (5)
MichelangeloConserva/Colosseum
May 2022 - Oct 2022
A pioneering Python package that creates a bridge between theory and practice in tabular reinforcement learning with an eye on the non-tabular setting.
Contributions:31 commits, 1 PR, 48 pushes in 5 months
MichelangeloConserva/MichelangeloConserva.github.io
May 2020 - Oct 2023
Contributions:3 PRs, 44 pushes, 2 branches in 3 years 6 months
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