I am a computer science Ph.D. student at Cornell, working on the verification of randomized algorithms. The correctness and performance of randomized algorithms are often difficult to test or reason about. I want to address this problem by enabling simple and compositional formal proofs of probabilistic programs. I am very fortunate to be advised by Prof. Justin Hsu to explore my interests.
Before moving to Cornell with my advisor, I spent two wonderful years at University of Wisconsin – Madison as a Ph.D. student, and prior to that, I did my undergrad at Cornell majoring in Mathematics and Computer Science.
Email: jb965 @cornell.edu (delete space)
Jialu Bao, Drashti Pathak, Justin Hsu, Subhajit Roy. “Data-Driven Invariant Learning for Probabilistic Programs.” (arxiv)
Jialu Bao, Marco Gaboardi, Justin Hsu, Joseph Tassarotti. “A Separation Logic for Negative Dependence.” Conditionally accepted to ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL), 2022. (email me for a draft and available online soon)
Jialu Bao, Kun He, Xiaodong Xin, Bart Selman, John E. Hopcroft. “Hidden Community Detection on Two-layer Stochastic Models: a Theoretical Prospective.” In International Conference on Theory and Applications of Models of Computation (TAMC), 2020. Springer. (TAMC version: arxiv) (In submission journal version: arxiv)
2021.10 Presented a “A Separation Logic for Negative Dependence” at Cornell’s PLDG.
2021.02 Presented a recent work on generating weakest pre-expectation at PPS-PIHOC-DIAPASoN Workshop!
2020.10 Led discussion of Data-Driven Inference of Representation Invariants at UW Madison’s PL seminar.
- Spring 18 & 19 CS 3110 Data Structures and Functional Programming by Prof. Nate Foster
- Spring 17 INFO 2950 Introduction to Data Science by Prof. Paul Ginsparg