Profile
2026 edition
Computer & Information Science
PhD student · Penn Database Group
Philadelphia, Pennsylvania
Portrait / ZZ—26I build adaptive software systems that learn from execution and improve how they behave, with a current focus on database engines.
Premise
Current focus
I am a PhD student in Computer and Information Science at the University of Pennsylvania, advised by Ryan Marcus. My work sits at the intersection of database systems, machine learning, and machine programming. I am interested in software that can observe its own behavior, adapt at runtime, and evolve without losing the guarantees that make systems dependable.
Advised by Ryan Marcus (opens in a new window) at the Penn Database Group (opens in a new window).
Inquiry
Active lines of work
Three questions connect runtime adaptation, workload evidence, and program structure.
How can a database choose better physical behavior at microsecond timescales?
I study execution engines that learn from fine-grained runtime feedback and change strategy as data and workloads shift.
Piece of CAKE ↓What do production traces hide about the work users actually wanted to run?
I examine the feedback loop between platforms and users so workload data can be interpreted without mistaking survivors for demand.
Survivorship Bias ↓Can software adapt inputs, policies, and implementations instead of waiting for retraining or manual tuning?
My broader goal is to combine machine learning with program structure to build systems that improve while they run.
CodeImprove ↓Output
Selected · reverse chronological
Research spanning database execution, workload interpretation, software adaptation, and biomedical vision.
A microsecond-scale learning system selects a physical kernel for each data morsel, reducing end-to-end workload latency by up to 2×.
Shows how production traces encode a negotiation between users and platforms—and why the queries missing from those traces matter.
Adapts out-of-scope programs with semantics-preserving transformations, improving deep code models without frequent retraining.
An ontology-aware vision–language foundation model for zero-shot nuclei classification across tissues and unseen cell classes.
Timeline
Recent activity
Training
Academic record
Advised by Ryan Marcus · Penn Database Group