About me
I am a doctoral student in the CMU-Portugal dual-degree PhD program in Software Engineering at Carnegie Mellon University (CMU) and Computer Science and Engineering at Instituto Superior Técnico (IST) - Universidade de Lisboa.
I am being kindly advised by Prof. Paolo Romano at IST and Prof. David Garlan at CMU. I am currently working as a graduate research assistant at Software and Societal Systems Department (S3D) where I integrate the ABLE Group, and as a researcher at IST and in the Distributed, Parallel and Secure Systems (DPPS) Group at INESC-ID Lisboa.
Research Interests
My research and main areas of interest are focused on Optimization, Machine Learning, Adversarial Training, Artificial Intelligence, Distributed Systems, Cloud Computing, Virtualization, and Computer Networks.
Publications
Information also available on Google Scholar.
Articles in Proceedings
Hyper-parameter Tuning for Adversarially Robust Models. Pedro Mendes, Paolo Romano, David Garlan. 2023.
HyperJump: Accelerating HyperBand via Risk Modelling. Pedro Mendes, Maria Casimiro, Paolo Romano, David Garlan. In AAAI 2023.
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling. Pedro Mendes, Maria Casimiro, Paolo Romano, David Garlan . In MASCOTS 2020.
Thesis
Leveraging Subsampling Techniques to Optimize Machine Learning Jobs in the Cloud. Pedro Mendes (supervised by Professors Paolo Romano and João Nuno Silva). MSc. Thesis, IST, Universidade de Lisboa, November 2019.
Other Articles
Exploring the Trade-offs to Train Robust Neural Models. Pedro Mendes, 2022.
Hyper-Parameter Tuning using Bayesian Optimization. Pedro Mendes, 2021.
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