San Francisco Bay Area
I am interested in finding efficient and secure solutions to widely used problems and use-cases in cloud computing. So far, I have addressed security and privacy risks in database (or storage), infrastructure, and classification-as-a-service models. I enjoy adapting cryptographic techniques to real-life problems, in which efficiency is as important as security. I also enjoy attacking problems in data mining and machine learning.
Working in Trust Org to build the infrastructure to better detect account take overs and fake accounts.
During my internship, I investigated if, in a multi-tenant setup, misbehaving tenants (i.e., those who behave different from their initial declarations) introduce hard-to-recover effects on LinkedIn's in-house key-value store, Espresso. In order to benchmark Espresso and other database solutions (e.g., MySQL, RocksDB, etc.), I developed a multi-tenant benchmarking tool that is highly modular and easily extensible to different setups. The source code is open-sourced under LinkedIn's git hub account, https://github.com/linkedin/MTBT.
I participated in a government project, in which we aimed to develop a fully-dynamic open-source image processing application. The application would allow integration of custom-developed image filters. I mainly worked on the GUI design, Help section, and custom filter integration with the application.