Meet Vora
Software Engineer at Google
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolMeet Vora is a software engineer with 11 years of experience specializing in machine learning, programming languages, and distributed systems, currently based in Zürich and working at Google. He holds degrees from IIT Roorkee and ETH Zürich and combines research-grade ML expertise with production-scale engineering. His background spans academic contributions in zero-shot learning and adversarial robustness to practical systems work like indoor localization and scalable campus services. As an open-source contributor to OpenMined's PySyft, he enhanced privacy-preserving ML by implementing minibatch gradient descent and encrypted linear classification examples. He has repeatedly moved ideas from theory to deployable solutions—whether novel clustering and density estimation for Maps or tooling that supports thousands of users at IIT Roorkee. Colleagues would describe him as someone who thrives at the intersection of principled research and reliable, scalable implementation.
12 years of coding experience
1 year of employment as a software developer
Indian Institute of Technology RoorkeeMaster of Science - MS Computer Science, Master of Science - MS Computer Science at ETH Zürich
Github Skills (9)
pytorch10
machine-learning10
python10
federated-learning10
cryptography10
linear-regression9
deep-learning9
secure-computation9
numpy8
Programming languages (3)
Github contributions (5)
OpenMined/PySyft
Oct 2017 - Oct 2017
Perform data science on data that remains in someone else's server
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 3 PRs, 2 comments in 1 day
Contributions summary:Meet primarily contributed to the development and testing of a linear classifier within the PySyft framework. Their work includes adding minibatch gradient descent with capsule support, updating the classifier tests, and integrating it with a capsule client. They also updated a notebook example demonstrating Paillier encrypted linear classification using the developed features. These contributions focus on enhancing the machine learning capabilities within the privacy-preserving framework.
data-sciencedeep-learningsecure-computationpytorchprivacy
meetvora/PoseNet
May 2019 - Oct 2019
Code for course project `Machine Perception` at ETH Zurich, Spring 2019.
Contributions:39 commits, 1 push in 4 months
3d-pose-estimationpytorch
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