Ethan Chu

I am a first-year PhD student in the Principles of Programming (PoP) group in Carnegie Mellon University’s Computer Science Department (CMU CSD), advised by Jan Hoffmann. My research areas are programming languages and verification, with a focus on type systems and static resource analysis of programs.

Currently, I am developing Resource Aware ML (RaML) 2, a tool that automatically and statically derives worst-case resource-use bounds for SML and OCaml programs, and successor to RaML. This involves designing, verifying, and implementing various Automatic Amortized Resource Analysis (AARA) type systems.

Before my PhD, I received my MS from CMU in 2023, and my BS from CMU in 2022.

news

Jan 2025	attended POPL 2025 in Denver, my first time attending a CS conference
Aug 2024	started my PhD at CMU CSD, advised by Jan Hoffmann
Dec 2023	graduated my MS at CMU CSD, defending MS thesis on RaML 2
Jun 2023	interned at Galois Inc
Jan 2023	started my MS at CMU CSD, advised by Jan Hoffmann

selected publications

RaML 2
The Theory and Implementation of Resource Aware ML 2

Ethan Chu

Carnegie Mellon University, 2023

Abs Bib PDF

Resource Aware ML (RaML) is a tool that can compute the resource use of programs in the ML family (SML, OCaml, etc) through a type-based technique known as automatic amortized resource analysis (AARA). Existing implementations of RaML have numerous shortcomings, such as lacking support for regular recursive types and being difficult to maintain. This thesis presents the theory and implementation of a new and improved version of RaML, Re- source Aware ML 2. Using work by Grosen et al, RaML 2 can analyze programs with regular recursive types, non-monotone resource analysis, and more. Furthermore, it presents the univariate AARA theory that the implementation is based on; univariate AARA’s expressive power is a subset of Grosen et al’s multivariate AARA, but it is still a unique variant. Additionally, this thesis presents supporting features for a full RaML implementation. This includes a new Intermediate Representation (IR) that the resource analysis operates on; this IR is implemented efficiently with an infrastructure known as Abstract Binding Trees. Other contributions include an SML frontend implementation that can translate arbitrary SML code to the RaML IR, as well as a linear programming solver backend.
@mastersthesis{chuthesis, author = {Chu, Ethan}, school = {Carnegie Mellon University}, title = {{The Theory and Implementation of Resource Aware ML 2}}, year = {2023}, }