Manaal Faruqui
Research Scientist at Google
New York, New York, United States
Summary
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RockstarManaal Faruqui is a Research Scientist based in New York with 13 years of experience building and refining machine learning models, particularly in neural sequence modeling. She contributed to the well-known DyNet toolkit, implementing an encoder-decoder morphology prediction model in C++ and iterating it with architectural improvements like integrated decoders, bias transforms, and beam search to boost prediction quality. Comfortable working close to the metal, she blends research rigor with production-minded engineering to move models from prototype to robust implementations. Her background suggests deep expertise in recurrent architectures and practical NLP tasks, plus a knack for improving open-source ML infrastructure.
13 years of coding experience
Hindi, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
neural-network10
machine-learning10
recurrent-neural-networks10
c-language10
deep-learning10
cprogramming-language10
dynet10
beam-search9
Programming languages (4)
Github contributions (5)
clab/dynet
Oct 2015 - Nov 2015
DyNet: The Dynamic Neural Network Toolkit
Role in this project:
ML Engineer
ML EngineerContributions:11 commits, 3 PRs, 4 pushes in 1 month
Contributions summary:Manaal appears to be developing a morphology prediction model using recurrent neural networks within the DyNet framework. The initial commit introduces an encoder-decoder architecture implemented in C++. Subsequent commits refined the model by moving the prediction module inside the decoder and adding a whole neural network class to encapsulate the encoder and decoder components. Further improvements involve adding bias transformations and better modules and then added beam search for improved prediction quality.
dynetdynamic-neural-networkdeep-learningneural-networksmachine-learning
mfaruqui/word-clustering
Sep 2012 - Mar 2013
Contributions:70 commits in 5 months
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