Jialu Bao

Jialu Bao

My first name is pronounced as J·ah·loo.

About me

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

Drafts

Tao Gu, Jialu Bao, Justin Hsu, Alexandra Silva, and Fabio Zanasi. “An abstract approach to conditional independence in DIBI models.” (preprint)

Publications

Jialu Bao, Nitesh Trivedi, Drashti Pathak, Justin Hsu, Subhajit Roy. “Data-Driven Invariant Learning for Probabilistic Programs.” In International Conference on Computer Aided Verification (CAV), 2022. (preprint) (slide)

Jialu Bao, Marco Gaboardi, Justin Hsu, Joseph Tassarotti. “A Separation Logic for Negative Dependence.” In ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL), 2022. (arxiv) (5 min video) (20 min video)

Jialu Bao, Simon Docherty, Justin Hsu, Alexandra Silva. “A Bunched Logic for Conditional Independence.” In ACM/IEEE Symposium on Logic in Computer Science (LICS), 2021. (arxiv) (video)

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)

Talks

  • 2022.03 Presented a “Data-Driven Invariant Learning for Probabilistic Programs” at Cornell’s PLDG. (slide)

  • 2021.10 Presented a “A Separation Logic for Negative Dependence” at Cornell’s PLDG. (slide)

  • 2021.02 Presented a preliminary work on “Data-Driven Invariant Learning for Probabilistic Programs” at PPS-PIHOC-DIAPASoN Workshop!

  • 2020.10 Led discussion of Data-Driven Inference of Representation Invariants at UW Madison’s PL seminar.

  • 2020.01 Presented a poster on “A Logic for Verifying Conditional Independence and Join Dependency” at POPL 2020’s Student Research Competition.

Teaching