I graduated with a PhD from Brown CS. My thesis advisors are Stan Zdonik and Seny Kamara. My committee is George Kollios and Moti Yung.

I am interested in the theories and designs of big data systems that are intelligent and safe. I draw on techniques from cryptography, machine learning, and relational databases.

• Resume
• Google scholar
• DBLP
• LinkedIn
• Github
• Research

Updates

• Paper “ACID-V: Towards a New Class of DBMSs for Data Sharing” accepted at Polystores VLDB’21.
• Paper “Structurally Encrypted Relational Database” accepted at CIDR’21.
• Submitted An Optimal Relational Database Encryption Scheme to eprint.

Experiences

• Technical University of Darmstadt, 2021
• Los Alamos National Laboratory, 2019
• Microsoft Research & AI, 2017
• Intel Labs, 2015
• Hadapt (Acquired by Teradata), 2013-14
• WalmartLabs, 2012

Current Projects

• Oblivious relational databases
• Secure decentralized federated learning
• Trustable data sharing

Past Projects

• KafeDB: End-to-End Structurally-Encrypted Relational Database
• ML framework for Cyber-physical systems
• Searchable encryption for mobile messaging in Signal
• Macau: statistical hypothesis testing based on resampling
• Machine learning algorithms in Spark
• Consistency control for machine learning algorithms
• R-tree in Rust
• Spark performance analysis tool
• VoltDB on non-volatile memory

Certificates

• Deep Learning Specialization, Coursera / deeplearning.ai
  • Neural Networks and Deep Learning
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Structuring Machine Learning Projects
  • Convolutional Neural Networks
  • Sequence Models