Karthik Prasad
California, United States
Summary
🤩
Rockstar🎓
Top SchoolKarthik Prasad is an Applied Machine Learning researcher and engineer in California with nine years of experience focused on 0→1 research and democratizing ML. He blends deep expertise in ML/DL modeling across NLP, speech, on-device ML, personalization, federated learning and differential privacy, and has held consecutive research engineering roles at Meta spanning LLM foundation work, Privacy AI, and neural interfaces (CTRL Labs). An active open-source contributor, he integrated torchdp-based differential privacy and precision_at_recall metrics into facebookresearch/pytext and extended pytorch/opacus to better support Conv1d, non-default stride/padding, frozen layers and per-sample gradients. That mix of research rigor and production engineering helps him deliver privacy-preserving, scalable ML systems that move fast from prototype to product.
9 years of coding experience
9 years of employment as a software developer
Bachelor of Engineering (B.E.), Bachelor of Engineering (B.E.) at PES UniversityVidya Mandir Independent P.U. CollegeMaster’s Degree, Master’s Degree at University of California, Irvine
Sanskrit, Kannada, Hindi, English
Github Skills (13)
neural-network10
convolutional-neural-network10
pytorch10
neuralnetwork10
machine-learning10
nlp10
convolutional-neural-networks10
differential-privacy10
python10
ml9
back-end-development9
deep-learning9
mle9
Programming languages (3)
Github contributions (5)
pytorch/opacus
Feb 2020 - Jan 2023
Training PyTorch models with differential privacy
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:10 releases, 202 reviews, 109 commits in 2 years 11 months
Contributions summary:Karthik focused on enhancing the `pytorch/opacus` repository, a project for training PyTorch models with differential privacy. Their contributions included implementing and supporting new functionalities for `Conv1d` layers and enhancing the existing code for convolutional layers to handle non-default stride and padding. They also addressed the handling of frozen layers to improve efficiency and contributed to the core functionalities of computing per-sample gradients and the related test files.
pytorchpytorch-modelsprivacydeep-learningprivacy-preserving-machine-learning
facebookresearch/pytext
Nov 2019 - Nov 2021
A natural language modeling framework based on PyTorch
Role in this project:ML Engineer
ML EngineerContributions:5 commits, 3 PRs in 2 years
Contributions summary:Karthik implemented and extended metrics related to precision and recall for the PyText framework, including the `precision_at_recall` metric and integration into the `SoftClassificationMetrics` structure. They added API support to include the `privacy_engine` in the `report_metric()` function within the metric reporters. Also included is the addition of differential privacy to the PyText framework, through integration with `torchdp` library. Additionally, the user refactored the data sharder configuration.
pytorchnlpbertmachine-learningnatural-language
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