Ahmet Iscen
Staff Research Scientist at Google DeepMind
France
Summary
👤
Senior🎓
Top SchoolAhmet Iscen is a Staff Research Scientist at Google DeepMind with nine years of experience specializing in image retrieval and computer vision. He progressed from research roles at Inria and the Center for Machine Perception to research scientist and senior research scientist positions at Google, demonstrating a steady track record of advancing visual recognition research into production-scale projects. His work includes concrete contributions to the tensorflow/datasets ecosystem—adding and curating image classification datasets such as iNaturalist2018, ImageNet-LT and Controlled Noisy Web Labels—which underscores his commitment to reproducible, dataset-driven research. Holding a PhD in Computer Science, he blends strong academic foundations with practical engineering, having also shipped mobile apps and worked as a software engineer early in his career. Based in France, he combines deep metric-learning expertise with hands-on dataset engineering to tackle long-tail and noisy-label challenges in modern vision systems.
9 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Université de Rennes IMaster of Science (M.S.), Computer Science, Master of Science (M.S.), Computer Science at Bilkent ÜniversitesiBachelor of Science, Computer Science, Bachelor of Science, Computer Science at State University of New York at Binghamton
English, Turkish, Spanish, French
Github Skills (10)
data-model10
machine-learning10
tensorflow10
python10
image-classification10
user-data10
data-set10
datasets10
numpy9
jax9
Programming languages (3)
Github contributions (5)
tensorflow/datasets
Nov 2021 - Jun 2022
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
Role in this project:
ML Engineer
ML EngineerContributions:4 commits in 6 months
Contributions summary:Ahmet primarily contributed to the `tensorflow/datasets` repository by adding and modifying datasets related to image classification. They implemented the iNaturalist2018 dataset, converting methods in ImageNet2012, and adding the ImageNet-LT and Controlled Noisy Web Labels datasets. The contributions suggest a focus on expanding the dataset library with new image classification datasets.
datanumpydeep-learningdatasetmachine-learning
ahmetius/diffusion-retrieval
Apr 2017 - May 2018
Contributions:4 commits, 1 PR, 3 pushes in 1 year 1 month
compactrepresentationsdeep-learningregionmanifolds
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.