Work Experience

Jun 2020 - Present

Exploring the new domain of Privacy Preserving Machine Learning #PPML to bring techniques like federated learning together with differential privacy for hospital datasets.

To know more about the project - Vedio

Jun 2020 - Present

Part of the Learning Team's Technical team, recently worked as the Webinar Lead (week-2) and the Teaching assistant for the OpenMined's First BootCamp called Beta-BootCamp.

Featured Blogpost: OpenMined Featured Contributors: September 2020 -Beta Bootcamp's Tech Team

Apr 2020 - Present

You may check my most recent blog posts from here: OpenMined Blog, Medium Blog

Jul 2020 - Sep 2020

Organising Member for PriCon2020 - personally co-ordinated 60+ Speakers from many of the world’s most important privacy and AI projects around the world: EFF, Signal, MIT, Twitter, DeepMind, FAIR, UCSF, Oxford, The Economist, the Ford Foundation, Inpher, and the United Nations just to name a few.

#PriCon2020 OpenMined's First Virtual Privacy Conference 2020 held on 26 & 27 Sep. To know more please visit the website. You may also checkout the conference recording from the links shared below:

Website: https://pricon.openmined.org/
• Day 1 (Part-1)
• Day 1 (Part-2)
• Day 2

Nov 2020 - Present

Teams at OpenMined are divided into 3 Major Teams Development, Community and Research team.

RecSys Teams (Recommendation system team) comes under the Federated Learning Team of Development Team. For more info - click here

Jan 2021 - Present

The Private AI Series- series of four courses designed for students of all skill levels. All of the courses will be free to access and technical mentorship is free for our students thanks to the generosity of the partners.

• Privacy and Society: Non-Technical course to build the knowledge and skills necessary to take advantage of the opportunities provided by privacy-enhancing technology.
• Foundations of Private Computation: Teaches every major privacy-preserving technology to an intermediate level, including the math behind encrypted computations.
• Federated Learning Across Enterprises: Students develop the skills to use federated learning for analyzing private data across multiple institutions.
• Federated Learning on Mobile: Teaches students how to build mobile apps that can train models across millions of devices.