Mohamed Amine Ketata

Data Scientist

https://talent.gravityer.com/mohamed-amine-ketata

Student in M.Sc "Robotics, Cognition, Intelligence" at the Technical University of Munich. Very passionate about Machine Learning, especially DL and RL.

Munich, Bavaria, Germany

Key Strengths

Strong foundational knowledge in machine learning and deep learning, particularly in diffusion models and reinforcement learning, as evidenced by multiple research-oriented projects.
Proficiency in Python and PyTorch for implementing complex AI/ML models.
Experience with scientific computing and data processing, including MRI reconstruction and molecular graph generation.
Demonstrated ability to contribute to academic research, with multiple projects leading to paper submissions (ICLR, NeurIPS).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's project portfolio is heavily focused on academic research and personal projects, primarily in advanced AI/ML domains. While this demonstrates strong technical curiosity and self-motivation, there is no information regarding collaborative work environments, team projects, or industry experience. This makes it difficult to assess cultural fit for a typical industry Data Scientist role without further information.

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

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Projects

rdb-diffusion

October 22, 2025 – Present

Official Implementation of the Paper "Joint Relational Database Generation via Graph-Conditional Diffusion Models" (NeurIPS 2025)

View Project

SyCo

July 10, 2024 – February 28, 2025

Official Implementation of our Paper "Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space" (ICLR 2025)

View Project

DiffDock-PP

April 7, 2023 – December 29, 2023

Implementation of DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models in PyTorch (ICLR 2023 - MLDD Workshop)

View Project

diffdock-protein

November 15, 2022 – December 19, 2023

DiffDock modified for protein-protein. Prototype version of DiffDock-PP.

View Project

Chess-game

October 3, 2021 – October 3, 2021

C++ implementation of a 2-player Chess game.

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Exploring-Munchausen-Reinforcement-Learning

October 3, 2021 – October 3, 2021

PyTorch implementation of Munchausen Reinforcement Learning based on DQN and SAC. Handles discrete and continuous action spaces

View Project

multi-domain-unet-fastMRI

June 16, 2021 – October 3, 2021

Multi domain U-Net, multi-channel data standardization and GRAPPA preprocessing for Magnetic Resonance Imaging (MRI) reconstruction.

View Project

Key Strengths

Strong foundational knowledge in machine learning and deep learning, particularly in diffusion models and reinforcement learning, as evidenced by multiple research-oriented projects.
Proficiency in Python and PyTorch for implementing complex AI/ML models.
Experience with scientific computing and data processing, including MRI reconstruction and molecular graph generation.
Demonstrated ability to contribute to academic research, with multiple projects leading to paper submissions (ICLR, NeurIPS).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

Mohamed Amine Ketata

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit