Di Wang

Registered user since Fri 20 Apr 2018

Name:Di Wang
Bio:

I am a doctoral student in computer science at Carnegie Mellon University. I am advised by Prof. Jan Hoffmann. I am broadly interested in programming languages and software engineering, especially probabilistic programming, type systems, static resource analysis, and program synthesis. Currently, I am working on language-level integrations for Bayesian inference and probabilistic programming systems.

I completed my undergraduate at Peking University, China where I worked with Prof. Yingfei Xiong on summarization techniques to analyze programs sharing big libraries.

Country:United States
Affiliation:Carnegie Mellon University
Research interests:Probabilistic programming, Type systems, Static resource analysis, Program synthesis

Contributions

PLDI 2021 Author of Central Moment Analysis for Cost Accumulators in Probabilistic Programs within the PLDI-track
Author of Sound Probabilistic Inference via Guide Types within the PLDI-track
ICFP 2020 Author of Liquid Resource Types within the ICFP Program-track
Author of Raising Expectations: Automating Expected Cost Analysis with Types within the ICFP Program-track
POPL 2020 Committee Member in Artifact Evaluation Committee within the Artifact Evaluation-track
PLDI 2019 Author of Resource-Guided Program Synthesis within the PLDI Research Papers-track
POPL 2019 Author of Type-Guided Worst-Case Input Generation within the Research Papers-track
Committee Member in Artifact Evaluation Committee within the Artifact Evaluation-track
PLDI 2018 Author of PMAF: An Algebraic Framework for Static Analysis of Probabilistic Programs within the PLDI Research Papers-track