Yannis Kalantidis
Principal Scientist at NAVER LABS Europe
Barcelona, Catalonia, Spain
Summary
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Senior🎓
Top SchoolYannis Kalantidis is a Principal Scientist specializing in computer vision and visual representation learning with a decade of experience spanning industry research labs in Europe and California. He leads projects on universal and adaptive visual representations at NAVER LABS Europe after prior research scientist roles at Facebook AI and Yahoo Research focused on video understanding, web-scale image search, and unsupervised feature learning. His PhD work and long-standing collaborations (including Visual Genome) underpin a strong track record in large-scale retrieval, deduplication, and multimodal embeddings. Comfortable bridging deep research and engineering, he has repeatedly moved ideas from web-scale experimental systems toward practical search and recommendation deployments. Based in Barcelona, he maintains an academic-facing presence through publications and a personal website that chronicles his evolving work. An uncommon strength is his repeated success applying unsupervised and scalable methods to real-world industrial search problems.
10 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD) Image Analysis and Computer Vision, Doctor of Philosophy (PhD) Image Analysis and Computer Vision at National Technical University of Athens
Greek, German, English
Github Skills (17)
weighting10
clustering8
unsupervised-machine-learning8
dimensionality-reduction8
self-supervised-learning7
spark6
deep-learning5
encoder4
python4
nearest-neighbor-search4
quantization4
3d3
vision-transformer3
computer-vision3
similarity-search3
Programming languages (2)
Github contributions (5)
skamalas/depth2time
Apr 2017 - Aug 2017
Contributions:6 commits, 8 pushes, 1 branch in 3 months
naver/tldr
Oct 2021 - Jun 2022
TLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses
Contributions:3 commits, 2 pushes, 1 comment in 7 months
dimensionality-reductionself-supervised-learningunsupervised-machine-learningpytorch-implementationmanifold-learning
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