Hi, I’m Rafael!

I’m a third-year Computer Science PhD student at Carnegie Mellon University (CMU) and Instituto Superior Técnico (IST), supported by a CMU Portugal Dual Degree PhD fellowship. I’m co-advised by Profs. José Fragoso Santos, Limin Jia and Pedro Adão. This summer I’m also interning at Amazon Web Services (AWS) as an Applied Scientist Intern, working on Automated Reasoning checks for Amazon Bedrock Guardrails.

My research primarily applies program analysis and formal methods to problems in software and web security. Most recently, I’ve been building robust analysis tools for detecting vulnerabilities in JavaScript code.

I hold an MSc in Computer Science and Engineering from Instituto Superior Técnico. I’ve also worked on fairness in Machine Learning as a Research Assistant at INESC-ID and served as a TA in multiple courses.

You can find my CV here.

PUBLICATIONS (Google Scholar)

• R. Gonçalves, F. Ramos, P. Adão, J. Fragoso Santos. Specification-Driven Generation of Summaries for Symbolic Execution. In: ESOP 2026. [DOI] [PDF] [Slides]

• R. Gonçalves, F. Gouveia, I. Lynce, J. Fragoso Santos. Proxy Attribute Discovery in Machine Learning Datasets via Inductive Logic Programming. In: TACAS 2025. [DOI] [PDF] [Slides]

• R. Gonçalves, F. Ramos, P. Adão, J. Fragoso Santos. Poster: Specification-Driven Synthesis of Summaries for Symbolic Execution. Presented at: ISSTA/ECOOP 2023. [Poster]

Theses

• R. Gonçalves. Specification-Driven Synthesis of Summaries for Symbolic Execution. MSc thesis, Instituto Superior Técnico, 2023. [Thesis]

EXPERIENCE

For Summer 2026, I’m interning at Amazon Web Services (AWS) as an Applied Scientist Intern, working on Automated Reasoning checks for Amazon Bedrock Guardrails.

Previously, I worked on the RIGA project as a Research Assistant in the Automated Reasoning and Software Reliability group at INESC-ID. Before that, I spent two months at CMU’s CyLab as a visiting scholar under the guidance of Prof. Limin Jia, working on NodeMedic.

Teaching

I’ve served as a TA in these courses at IST:

• Highly Dependable Systems (MSc) [Spring 2023, Spring 2024]
• Compilers (BSc) [Summer 2024]

PROJECTS

• RIGA: Reasoning Over Indirect Discrimination [DOI]
  The issue of fairness is a well-known challenge in Machine Learning (ML). Algorithmic bias can manifest during the training of ML models due to the presence of sensitive attributes, such as gender or racial identity. Project RIGA aims to apply automated reasoning techniques to detect indirect discrimination in ML classification algorithms. The focus is on so-called proxy attributes: those that, while not sensitive themselves, may lead to discriminatory behavior due to their correlation with sensitive attributes.

CONTACT

Want to talk? Find me at:

GHC 9223, CMU

rgoncalv@andrew.cmu.edu

Rafael Gonçalves