Carnegie Mellon University & University of Porto
I am a PhD candidate in Software Engineering at Carnegie Mellon University and the University of Porto (CMU Portugal dual degree), graduating in August 2026.
Developers naturally think and communicate in sketches, yet software is built purely through text. My research makes sketches a first-class interface to code, across three big ideas:
- Code generation from sketches — a Visual Code Assistant that turns hand-drawn ML sketches into runnable notebooks (ICSE'25);
- Code explanation with sketches — agentic systems that generate visual documentation from source code: VisDocSketcher (arXiv'25) and JupyterDraw;
- Code–sketch co-evolution — Visual Loop, a development environment that keeps code and sketches in bidirectional synchronization (FORGE'26).
Open to Research Scientist / Research Engineer roles in AI for software engineering, agents, and reasoning.
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Carnegie Mellon UniversitySchool of Computer Science
Ph.D. in Software Engineering (CMU Portugal)Aug. 2021 - Aug. 2026 -
University of PortoPh.D. in Computer Science (CMU Portugal)
Dual DegreeAug. 2021 - Aug. 2026with Rui Abreu -
University of MinhoB.Sc. + M.Sc. in Computer Science (Intelligent Systems & Data Science)Sep. 2015 - Aug. 2021
Experience
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HubsData Science Intern, Amsterdam2021 -
Instituto Superior TécnicoJunior Software Engineer2020 - 2021
Honors & Awards
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CMU Portugal Fellowship2021 - 2026
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Huawei Scholarship — Top Students in Portugal (top 1%)2023
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FCT Research Fellowships (×2)2018 - 2021
Selected Publications (view all )
Visual Loop: Bridging the Cognitive Gap in Software Development Through Visual-AI Collaboration
Luís F. Gomes, Xin Zhou, David Lo, Rui Abreu
International Conference on AI Foundation Models and Software Engineering (FORGE) 2026
A vision paper introducing Visual Loop, a continuous visual development environment that keeps code and informal sketches in bidirectional synchronization. The prototype connects a code editor with a tablet-based visualization workspace, where freehand sketches are interpreted by multimodal LLMs grounded in static analysis and visual context — turning sketching from passive documentation into an active interface for software evolution.
Visual Loop: Bridging the Cognitive Gap in Software Development Through Visual-AI Collaboration
Luís F. Gomes, Xin Zhou, David Lo, Rui Abreu
International Conference on AI Foundation Models and Software Engineering (FORGE) 2026
A vision paper introducing Visual Loop, a continuous visual development environment that keeps code and informal sketches in bidirectional synchronization. The prototype connects a code editor with a tablet-based visualization workspace, where freehand sketches are interpreted by multimodal LLMs grounded in static analysis and visual context — turning sketching from passive documentation into an active interface for software evolution.
An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks
Xin Zhou, Kisub Kim, Ting Zhang, Martin Weyssow, Luís F. Gomes, Guang Yang, David Lo
CORE A* International Conference on Automated Software Engineering (ASE) 2026
A study of LLM-as-judge metrics for software engineering tasks, calibrated to bridge the gap with human evaluation across a range of SE benchmarks.
An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks
Xin Zhou, Kisub Kim, Ting Zhang, Martin Weyssow, Luís F. Gomes, Guang Yang, David Lo
CORE A* International Conference on Automated Software Engineering (ASE) 2026
A study of LLM-as-judge metrics for software engineering tasks, calibrated to bridge the gap with human evaluation across a range of SE benchmarks.
VisDocSketcher: Towards Scalable Visual Documentation with Agentic Systems
Luís F. Gomes, Xin Zhou, David Lo, Rui Abreu
arXiv preprint 2025
An agentic LLM system that generates high-level visual documentation from source code, paired with AutoSketchEval — a reference-free evaluation framework (inspired by autoencoder reconstruction) that scores diagram quality with no ground truth, reaching AUC > 0.87 across 1,000 Jupyter notebooks.
VisDocSketcher: Towards Scalable Visual Documentation with Agentic Systems
Luís F. Gomes, Xin Zhou, David Lo, Rui Abreu
arXiv preprint 2025
An agentic LLM system that generates high-level visual documentation from source code, paired with AutoSketchEval — a reference-free evaluation framework (inspired by autoencoder reconstruction) that scores diagram quality with no ground truth, reaching AUC > 0.87 across 1,000 Jupyter notebooks.
An Exploratory Study of ML Sketches and Visual Code Assistants
Luís F. Gomes, Jonathan Aldrich, Rui Abreu, Vincent Hellendoorn
CORE A* International Conference on Software Engineering (ICSE) 2025
A VSCode assistant that turns hand-drawn ML workflow sketches into runnable Jupyter notebooks (79% structural accuracy, 49% reduction in coding), with a 19-participant developer study and an automated LLM-as-judge pipeline benchmarking sketch-to-code across GPT-4o, Gemini Pro, and Claude.
An Exploratory Study of ML Sketches and Visual Code Assistants
Luís F. Gomes, Jonathan Aldrich, Rui Abreu, Vincent Hellendoorn
CORE A* International Conference on Software Engineering (ICSE) 2025
A VSCode assistant that turns hand-drawn ML workflow sketches into runnable Jupyter notebooks (79% structural accuracy, 49% reduction in coding), with a 19-participant developer study and an automated LLM-as-judge pipeline benchmarking sketch-to-code across GPT-4o, Gemini Pro, and Claude.