Kritika Prakash
Graduate Teaching Assistant
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolKritika Prakash is a PhD-trained computer scientist and graduate teaching assistant at the University of Chicago with a decade of experience bridging research and applied machine learning. She has contributed to privacy-preserving data science projects like OpenMined’s PySyft—implementing remote tensor transfer and debugging data-science notebooks—while interning and researching at Microsoft and Google on AI-for-good initiatives. Her background spans research roles in academia and industry, mentoring open-source contributors, and teaching core CS courses from databases to data science. Known for combining rigorous academic training with hands-on systems work, she often operates at the intersection of ML research, privacy, and education. A less obvious strength is her sustained commitment to community-building, evidenced by mentoring at OpenMined and leadership at Lean In India.
9 years of coding experience
3 years of employment as a software developer
High School Diploma, 10.0/10.0, High School Diploma, 10.0/10.0 at Delhi Public School, Bangalore EastPhD, Computer Science, PhD, Computer Science at University of ChicagoMaster of Science - MS, Computer Science and Engineering, Master of Science - MS, Computer Science and Engineering at International Institute of Information Technology
English, Hindi, French
Github Skills (7)
pytorch10
python10
data-science9
jupyter-notebook9
federated-learning7
pandas7
deep-learning5
Programming languages (6)
Github contributions (5)
OpenMined/PySyft
Oct 2020 - Oct 2021
Perform data science on data that remains in someone else's server
Role in this project:
Data Scientist
Data ScientistContributions:100 commits, 14 PRs, 39 pushes in 11 months
Contributions summary:Kritika's commits primarily involve testing and debugging notebooks within the `openmined/pysyft` repository. They are working with Syft, a framework for privacy-preserving data science, and are implementing the `send` method to send a tensor to a remote machine (identified by the URL/email login). The commits show exploration of the Syft framework's capabilities, including the use of the `uk` client to send tensors to a remote worker and inspect the data stored. The code includes data loading, and demonstrates data manipulation and analysis for the UK trade dataset, indicating a focus on data science tasks using the PySyft framework.
pytorchcryptographyacquiringpythonscience
Kritikalcoder/ResearchPapers
Sep 2018 - Sep 2019
Contributions:17 commits, 15 pushes, 1 branch in 1 year
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