Even Oldridge
Director Of Applied Research, Nemo Retriever at NVIDIA
Vancouver, British Columbia, Canada
Summary
🤩
Rockstar🎓
Top SchoolEven Oldridge is a director-level applied research leader with nine years in industry and a seven-year track record turning petabyte-scale user and metadata into production-ready recommender systems and deep learning models. Based in Vancouver, he leads NVIDIA teams focused on GPU-accelerated RecSys and research into deep tabular embeddings, denoising autoencoders, and transfer-learning solutions that address item and user cold-start. He combines hands-on model building (PyTorch, FastAI) and architecture design with team leadership and product integration, having moved ideas from KDD/RecSys talks to deployed systems at realtor.com and NVIDIA. His background spans classical ML, NLP and vision work, plus niche interests like neural art and GAN explorations, reflecting a habit of cross-pollinating research techniques. Notably, he has a long history of bridging research and engineering—accelerating RecSys on GPUs while shepherding ablation studies and publications that push state-of-the-art for tabular deep learning.
9 years of coding experience
10 years of employment as a software developer
Practical / Cutting Edge Deep Learning For Coders, Practical / Cutting Edge Deep Learning For Coders at fast.aiPhD Electrical & Computer Engineering, PhD Electrical & Computer Engineering at The University of British Columbia
Github Skills (124)
python10
feature-extraction10
feature-engineering10
huggingface10
deep-learning10
gpu10
tabular10
cpp10
gpu-acceleration10
nbdev10
acceleration10
ml10
numpy10
fastai10
merlin10
Programming languages (7)
Github contributions (5)
rapidsai/deeplearning
Jun 2019 - Sep 2019
Contributions:48 commits, 41 pushes, 1 branch in 3 months
EvenOldridge/fastai
Jan 2018 - Apr 2018
Contributions:13 pushes, 2 branches, 1 comment in 3 months
pytorchpythondeep-learning-aideep-learningmachine-learning
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