Anuj Dutt
Machine Learning Engineer at Adobe
Cupertino, California, United States
Summary
👤
Senior🎓
Top SchoolAnuj Dutt is a Machine Learning Engineer with 9 years of experience building efficient on-device AI and computer vision solutions for consumer and enterprise products. Currently at Adobe and previously leading AI systems at Jabra and deep learning research at Bose, he specializes in model compression, quantization, distillation, and hardware-aware NAS to deliver low-memory, real-time inference on edge devices. He has a strong engineering toolkit spanning C/C++, Python, TensorFlow/PyTorch, CoreML/TFLite, and cloud platforms, and has shipped mobile/on-device DNNs and patented innovations focused on privacy-preserving and resource-constrained deployments. Based in Cupertino, he also advises UC Irvine’s Customer Experience Program and contributes to practical ML projects like symptom-based disease prediction, reflecting a blend of research, product delivery, and data-focused experimentation.
9 years of coding experience
4 years of employment as a software developer
Decentralized AI Applications using Blockchain, Decentralized AI Applications using Blockchain at Theschool.aiBachelor of Technology (B.Tech.) Electrical Electronics and Communications Engineering, Bachelor of Technology (B.Tech.) Electrical Electronics and Communications Engineering at Kurukshetra UniversityC++ Nanodegree Computer Science, C++ Nanodegree Computer Science at UdacityMaster of Science (MS) Computer Engineering, Master of Science (MS) Computer Engineering at University of Florida
English, Hindi, Punjabi
Github Skills (6)
scikit10
pandas10
machine-learning10
python10
numpy10
scikit-learn10
Programming languages (10)
Github contributions (5)
anujdutt9/Disease-Prediction-from-Symptoms
Apr 2018 - Sep 2022
Disease Prediction based on Symptoms.
Role in this project:
Data Scientist
Data ScientistContributions:29 commits, 3 PRs, 26 pushes in 4 years 5 months
Contributions summary:Anuj primarily worked on the project's data processing and model training. Their commits demonstrate data loading and cleaning within a Jupyter Notebook, suggesting an initial exploration and preprocessing of the dataset. The code modifications involve importing libraries like `pandas` and `numpy`, and applying data transformation techniques, which are crucial steps in preparing the data for machine learning. The user also cleaned the dataset.
pythonsymptomspredictionknn-classificationclassification-algorithm
anujdutt9/anujdutt9.github.io
Jul 2019 - Mar 2024
My Blog on ML, DL, NLP, Image Processing and a lot more.
Contributions:8 PRs, 84 pushes, 7 branches in 4 years 8 months
nlpmy-blogpythondata-sciencedeep-learning
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