Bio

Hello! I'm Donghyun Sohn, a Ph.D. candidate in Computer Science at Northwestern University, working with Professor Jennie Rogers. My research focuses on building secure and efficient data processing systems, especially at the intersection of databases and cryptography.

I develop practical query engines that support secure execution using cryptographic techniques such as secure multi-party computation (MPC) and fully homomorphic encryption (FHE). My work tackles real-world challenges in private data analytics—ensuring that even sensitive queries can be executed efficiently without compromising security.

While my core expertise lies in secure data infrastructure, I'm also broadly interested in making AI systems more efficient and resource-aware. This includes optimizing the dataflow and execution of AI workloads, especially under privacy or deployment constraints. I often explore how systems techniques—like query optimization, cost-based planning, and hardware-aware execution—can improve the performance and scalability of AI and analytics workloads alike.

I'm passionate about systems that actually run—my work is implemented in open-source projects and regularly evaluated on cloud environments like AWS. I enjoy bridging research and practice, working across the full stack from low-level execution details to high-level design decisions.

Email: donghyun.sohn@u.northwestern.edu