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 also 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.”

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. Distinguished Paper Award. (arxiv) (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 (TAMC version: arxiv) (Unpublished extended version: arxiv)

News

• March 2023 I am humbled to receive an honerable mention of Jane Street Graduate Research Fellowship this year.
• Nov. 2022 I am excited about joining the Operations Team of SIGPLAN Long-Term Mentoring Committee! We warmly invite anyone who is interested in programming language research, regardless of their background, to join us as mentors, mentees, or both.
• Aug. 2022 I passed my A-exam and became a Ph.D. candidate.

Talks

• Led discussion of Impossibility of Distributed Consensus with One Faulty Process (the seminal FLP impossibility result) in CS 6410 Advanced Systems. (slide)

• 2022.08 Proposed “Formally Reasoning about (In)dependencies in Probabilistic Programs” for my A-Exam. (slide)

• 2022.06 Presented “Data-Driven Invariant Learning for Probabilistic Programs” at OPLSS 2022 Participants Talk.

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

• 2022.02 Led discussion of “Sometimes” and “not never” revisited: on branching versus linear time temporal logic at Cornell PL’s Great Works seminar. (slide)

• 2021.10 Presented “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

• Spring 22 CS 3110 Data Structures and Functional Programming by Prof. Nate Foster and Justin Hsu

• Fall 21 CS 6182 Foundations of Probabilistic Programming by Prof. Justin Hsu

• Spring 18 & 19 CS 3110 Data Structures and Functional Programming by Prof. Nate Foster

• Fall 18 CS 4820 Introduction to Analysis of Algorithms, by Prof. Eva Tardos and Xanda Schofield

• Fall 17 CS 2850 Networks, by Prof. Jon Kleinberg and David Easley

• Spring 17 INFO 2950 Introduction to Data Science by Prof. Paul Ginsparg