Jialu Bao

Jialu Bao

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

About me

I am a postdoctoral researcher at Northeastern University, working with Prof. Steven Holtzen. I obtained my Ph.D. at Cornell University, advised by Prof. Justin Hsu. My Ph.D. research focuses on the verification of randomized algorithms. The correctness and performance of randomized algorithms are often difficult to test or reason about. I address this problem by enabling simple and compositional formal proofs of probabilistic programs. Prior to my Ph.D., I obtained Bachelor of Arts also at Cornell University, majoring in Mathematics and Computer Science. View my professional summary here.

Email: jb965@cornell.edu

Publications

Bluebell: An Alliance of Relational Lifting and Independence For Probabilistic Reasoning
Jialu Bao, Emanuele D’Osualdo, Azadeh Farzan.
Symposium on Principles of Programming Languages (POPL) 2025.
[arxiv]

A Categorical Approach DIBI models
Tao Gu, Jialu Bao, Justin Hsu, Alexandra Silva, and Fabio Zanasi.
Formal Structures for Computation and Deduction (FSCD) 2024.
[arxiv]

Mixture Languages
Oliver Richardson, Jialu Bao.
The Languages for Inference Workshop (LAFI) 2024.
[extended abstract]

Data-Driven Invariant Learning for Probabilistic Programs
Jialu Bao, Nitesh Trivedi, Drashti Pathak, Justin Hsu, Subhajit Roy.
Distinguished Paper Award, Computer Aided Verification (CAV) 2022.
Journal version appears on Formal Methods in Systems Design (FMSD).
[arxiv] [extended abstract] [slide]

A Separation Logic for Negative Dependence Jialu Bao, Marco Gaboardi, Justin Hsu, Joseph Tassarotti. Symposium on Principles of Programming Languages (POPL) 2022.
[arxiv] [5 min video] [20 min video]

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

Hidden Community Detection on Two-layer Stochastic Models Theoretical Prospective
Jialu Bao, Kun He, Xiaodong Xin, Bart Selman, John E. Hopcroft.
Theory and Applications of Models of Computation (TAMC) 2020.
Journal version appears on Transactions on Knowledge Discovery from Data (TKDD).
[conference version arxiv] [journal version arxiv]

News

  • Aug. 2025 I will attend Upstate PL Seminar 2025.
  • July 2025 I passed my B exam (thesis defense)!
  • March - May. 2025 I had the pleasure to visit Prof. Alexandra Silva’s group at University College London!
  • May - Aug. 2024 I had the opportunity to intern with a wonderful team at Basis Research Institute!
  • Aug. 2023 I attended Marktoberdorf Summer School.
  • 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! The programs welcomes aspiring and current programming languages researchers from all walks of life to serve as mentors, mentees, or both. SIGPLAN members and non-members alike are welcome, and there is no cost to participate. There are no limitations on country of residence or languages spoken.
  • Aug. 2022 I passed my A exam and became a Ph.D. candidate.
  • July 2022 I attended Oregon Programming Languages Summer School (OPLSS).

Talks

  • 2024.10 Talked about “Bluebell: An Alliance of Relational Lifting and Independence For Probabilistic Reasoning” at Portland State University’s PLV Seminar.

  • 2024.10 Talked about “Bluebell: An Alliance of Relational Lifting and Independence For Probabilistic Reasoning” at Cornell’s PLDG.

  • 2023.11 Talked about “A Separation Logic for Negative Dependence” at Boston University’s POPV seminar.

  • 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)

  • 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!

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

Teaching