Somshubra Majumdar
Senior Deep Learning Engineer at NVIDIA
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSomshubra Majumdar is a Senior Deep Learning Engineer with 12 years of experience, currently building scalable ASR and generative AI systems at NVIDIA from San Jose. He blends research and production: reproducing and improving papers like Neural Style Transfer in Keras while contributing to high-profile open-source projects such as NVIDIA NeMo and keras-contrib (DenseNet, NASNet, GroupNormalization). His work spans model design, decoder improvements (beam search for RNN-T), data pipeline optimizations, and integration efforts (Hugging Face datasets, DALI) that reduce training costs at scale. Earlier research and entrepreneurial experience includes algorithmic contributions in parallel and meta-sorting (AdaSort) and founding a software solutions startup, reflecting a pragmatic focus on both novel algorithms and robust implementation.
12 years of coding experience
3 years of employment as a software developer
Masters Computer Science, Masters Computer Science at University of Illinois ChicagoBachelor of Science (B.Sc.) Computer Engineering, Bachelor of Science (B.Sc.) Computer Engineering at Bhavans CollegeJamnabai Narsee Monjee SchoolBachelor of Science (BSc) Computer Engineering, Bachelor of Science (BSc) Computer Engineering at Dwarkadas J. Sanghvi College of Engineering
English, Hindi, Japanese
Github Skills (30)
pytorch10
convolutional-neural-networks10
preprocessing10
python10
load-data10
preprocess10
machine-learning10
voice-recognition10
automatic-speech-recognition10
super-resolution10
data-preprocessing10
keras10
data-loading10
densenet10
dataprep10
Programming languages (7)
Github contributions (5)
keras-team/keras-contrib
Feb 2017 - Feb 2019
Keras community contributions
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:107 commits, 76 PRs, 18 pushes in 2 years
Contributions summary:Somshubra contributed to the `keras-contrib` repository by adding implementations of the DenseNet and NASNet deep learning models. Their work involved creating the necessary code, including model definitions and examples, as well as fixing issues related to model loading and parameter configurations. The user also made code style improvements, such as PEP8 fixes, to enhance the code quality of the implemented models. Further improvements were made by implementing the GroupNormalization layers, enhancing the models supported by the repository.
pythondata-sciencedeep-learningtheanoneural-networks
titu1994/Image-Super-Resolution
Jun 2016 - Mar 2018
Implementation of Super Resolution CNN in Keras.
Role in this project:ML Engineer
ML EngineerContributions:1 release, 107 commits, 4 PRs in 1 year 9 months
Contributions summary:Somshubra made several corrections and improvements to the image super-resolution model, modifying the model architecture and code within the `img_utils.py` and `ImageSRModel.py` files. They created a centralized `models.py` to manage different model types and added the expanded super-resolution model (ESR). Further contributions include implementing a Denoise Auto Encoder SR model, and the ability to upscale large images on the CPU by splitting them.
deep-learningresolutionsuper-resolutiontensorflowkeras
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