Paul Krogmeier

I’m a postdoctoral fellow at Harvard advised by Nada Amin and Walter Fontana. My primary area of research touches programming languages, logic, and program synthesis. I received a PhD from the University of Illinois advised by Madhusudan Parthasarathy.

I’ll be joining the University of Colorado Boulder as an assistant professor in computer science starting Fall 2026 and am recruiting PhD students. If my research interests you then consider applying to the CS PhD program and mentioning my name in your application.

I’m also a jazz alto saxophonist and always interested in jamming with others 🎷.

Some Interests

  • Interpretability and efficiency in machine learning
  • Symbolic learning, program synthesis
  • Descriptive complexity, finite model theory
  • Reliable computation with unreliable components, fault tolerance
  • Computational capabilities of neural networks
  • Musical improvisation through an algorithmic lens ... Jazz improvisation mixes melodic and rhythmic constraints with an imperative for novelty and surprise. Precisely formalizing such constraints and developing algorithms for generating music within them is a lovely confluence of music, algorithms, and formal logic. I'd like to develop a CS + Music course around this connection.
  • What is "creativity"?

Ongoing Projects

  • Emergence of abstraction: exploring a computational model in which phenomena associated with "abstraction" and symbolic languages can emerge as outcomes of simple computations
  • DSL synthesis: theory and algorithms for automatically synthesizing DSLs that express relevant domain concepts succinctly and irrelevant ones less succinctly or not at all

Publications

Synthesizing DSLs for Few-shot Learning
Paul Krogmeier and P. Madhusudan
OOPSLA 2025
Languages with Decidable Learning: A Meta-theorem
Paul Krogmeier and P. Madhusudan
★Distinguished Paper Award at OOPSLA 2023
Synthesizing Axiomatizations using Logic Learning
Paul Krogmeier*, Zhengyao Lin*, Adithya Murali*, and P. Madhusudan
OOPSLA 2022
Composing Neural Learning and Symbolic Reasoning with an Application to Visual Discrimination
Adithya Murali, Atharva Sehgal, Paul Krogmeier, and P. Madhusudan
IJCAI 2022
Learning Formulas in Finite Variable Logics
Paul Krogmeier and P. Madhusudan
★Distinguished Paper Award at POPL 2022
Deciding Accuracy of Differential Privacy Schemes
Gilles Barthe, Rohit Chadha, Paul Krogmeier, A. Prasad Sistla, and Mahesh Viswanathan
POPL 2021
Decidable Synthesis of Programs with Uninterpreted Functions
Paul Krogmeier, Umang Mathur, Adithya Murali, P. Madhusudan, and Mahesh Viswanathan
CAV 2020
Deciding Memory Safety for Single-Pass Heap-Manipulating Programs
Umang Mathur, Adithya Murali, Paul Krogmeier, P. Madhusudan, and Mahesh Viswanathan
POPL 2020
Towards Context-Aware Data Refinement
Paul Krogmeier, Steven Kidd, and Benjamin Delaware
Intl. Workshop on Coq for Programming Languages 2018

*equal contribution