My work sits at the intersection of cryptography, differential privacy, statistics, and machine learning including post-quantum cryptographic delegation and privacy-preserving machine learning. I’m broadly interested in mathematical and information-theoretic tools for understanding and bounding privacy leakage in learning systems.
About
I have a background in applied mathematics, statistics, and computer science. My research spans cryptographic protocol design, differential privacy, and statistical learning — from secure computation to delegation schemes for post-quantum cryptography.
I also enjoy teaching and writing tutorials that make technical material more approachable.
Research interests
- Differential privacy, statistics, and machine learning
- Post-quantum cryptographic delegation
- Information-theoretic approaches to privacy leakage
- Applied cryptography and secure computation