Students
Students
How to join, expectations, templates, and resources for capstones, projects, and theses.
Students
This page collects practical information for students working with me (capstones, projects, and theses), including how to join, expectations, and useful resources.
Prospective PhD students
I’m happy to discuss PhD topics aligned with my research areas (HPC, computer architecture, quantum & hybrid ML, and AI for healthcare). A strong application is concrete, reproducible, and grounded in a clear evaluation plan.
- Send: CV, transcript, and a one-page research sketch (problem → method → evaluation).
- Include: links to code or writing samples (GitHub / report / preprint).
- Expect: emphasis on baselines, ablations, and reproducibility from day one.
How to join my group
- Capstone / project: send a short email with your interests, transcript, and CV. Mention 1–2 capstone topics you’d like to pursue.
- MSc thesis: propose a concrete problem statement (1 page) and include a brief plan for evaluation (datasets/benchmarks + metrics).
- Research assistant / internship: include relevant code samples (GitHub) and a short summary of what you built.
What I look for
- Strong fundamentals and the ability to learn quickly (systems + ML basics).
- Reproducible experimentation: clear baselines, controlled comparisons, and honest reporting.
- Clear writing and communication (short weekly updates and a structured final report).
Expectations & working style
- Weekly check-ins (15–30 minutes) with a short written update.
- Reproducible code: version control, clean README, pinned dependencies, and scripts to rerun experiments.
- Deliverables: a final report + public/private repository + slides or demo.
Capstones and course page
For the official capstone course information and enrollment details, see:
Thesis templates & resources
- Report structure: problem statement → related work → method → experiments → discussion → limitations → conclusion.
- Evaluation checklist: define baselines, metrics, datasets, compute budget, and ablations early.
- Reproducibility: keep a lab notebook (even a simple markdown log) with decisions and results.
PhD students — Ongoing
2025
Ana Fernandes — Unlocking Sparse Processing with System-on-Chip Stream-based Acceleration
2023
Sofia Monteiro — Classifying functional impairments in Foot Clinic patients using plantar pressure distributions
2023
Hamid Moghadaspour — Highly-Performant Underwater SLAM-Based 3D Reconstruction Architectures
2021
Claudio Gomes — Quantum Computing for Sustainability
PhD students — Completed
2025
Oscar Ferraz — Towards Processing Near-Memory in Non-Binary Low Density Parity Check Decoders
2025
Joao Vieira — Enabling General-Purpose Processing-In-Memory Through a Locality-Aware Architecture and Compiler
2020
Joao Gante — The Interplay Between Positioning and Beamforming in Millimeter Wave Communications
2016
Joao Andrade — Design Space Exploration of LDPC Codes on Multicore Architectures
2015
Rui Melo — Real-Time Urban 3D Modeling by Combining Stereo from Symmetry with General Purpose Programming in the Graphics Processing Unit
Lab culture (yes, this matters)
