Anirudh Dagar
Applied Scientist at Amazon Web Services (AWS)
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolAnirudh Dagar is an Applied Scientist at AWS based in Berlin with nine years of experience bridging machine learning research and production-grade open source engineering. An IIT Roorkee alumnus, he contributes to prominent projects like PyTorch Vision, SciPy and the widely used Dive Into Deep Learning textbook—work that often revolves around test automation, PyTorch adaptations, and dataset/model implementations. At AWS he focuses on AutoGluon and Bedrock capacity management, combining AutoML exploration with scalable cloud systems. His open-source work spans low-level numerical libraries (sparse matrix and integration fixes) to multimodal ML tooling and CI/CD for multilingual deep learning resources. Notably, he has improved testing infrastructure and portability across frameworks, reflecting a pragmatic emphasis on reproducibility and interoperability. Outside work he sustains long-term OSS involvement (Quansight Labs) that feeds back into his applied research and engineering practice.
9 years of coding experience
1 year of employment as a software developer
Delhi Public School GhaziabadIndian Institute of Technology Roorkee
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (47)
textbook10
algorithms10
pytorch10
notebook10
github-ci10
scipy10
c-language10
pytest10
numerical-integration10
python10
data-science10
storybook10
testing10
sparse-matrix10
machine-learning10
Programming languages (10)
Github contributions (5)
dsgiitr/graph_nets
Aug 2019 - May 2021
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:1 review, 47 commits, 1 PR in 1 year 9 months
Contributions summary:Anirudh primarily worked on implementing and explaining various graph representation learning papers using PyTorch. Their contributions involved modifying and documenting code related to Graph Convolutional Networks (GCNs), including the implementation of a GCN layer using PyTorch Geometric (PyG). Further contributions included code modifications related to implementing and documenting graph attention networks (GATs), and DeepWalk. They also included code related to ChebNet.
node-embeddingrepresentation-learningrepresentationgraph-attention-networksgraph-representation-learning
dsgiitr/d2l-pytorch
May 2019 - Jan 2021
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Role in this project:Data Scientist
Data ScientistContributions:107 commits, 68 PRs, 151 pushes in 1 year 8 months
Contributions summary:Anirudh contributed code to reproduce the content of a book related to deep learning, adapting the code from MXNet into PyTorch. They added code examples and documentation primarily focusing on data manipulation, linear algebra, automatic differentiation, and Naive Bayes classification. Their work involved implementing and exploring foundational concepts within PyTorch.
d2lnlppytorchmxnetpytorch-implmention
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