Experience

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  Universitat politècnica de catalunya

  Sept 2016 - Jul 2017

  Following the previous research success, we moved on to perform the electromagnetic characterization of an action potential propagating along a sensitive plant. This project constituted my bachelor's degree thesis. I joined the AntennaLAB group to develop microwave-based brain imaging techniques with the AntennaLAB group. I focused on finding biophysical mechanisms related to brain activity that would alter the dielectric properties of their environment.

  • Research Fellow

    Mar 2017 - Jul 2017

  • Research Fellow

    Sept 2016 - Feb 2017
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  Telefonica i+d

  Jan 2018 - Jul 2018

  Research intern

  Brain-computer interface (BCI) technology development using machine learning (ML) to process EEG signal in real time. The Telefonica research and development team opened a research line in BCI. I joined the team as their first student and we set up the project from scratch. My role was to become familiar with the signal acquisition device and develop work pipelines that would allow the company to use the technology in the future. I designed a multi-purpose data acquisition protocol and built a database of EEG signals from company employees and external volunteers. Finally, I trained various ML models, including feature-based classifiers and deep learning models, to process the acquired EEG signal with the goal to play the pong game. Mostrar menos

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  Queen mary university of london

  Feb 2019 - Jan 2019

  Visiting researcher

  International research stay under the supervision of Prof. Dr. Lucas Lacasa. We engaged on a collaboration with Tooso Inc (a startup) using machine learning to successfully predict user behavior in e-commerce websites.

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  Icfo

  Sept 2019 - now

  PhD in the Quantum Optics Theory group lead by Prof. Dr. Maciej Lewenstein.During my thesis, I develop machine learning algorithms to tackle problems in quantum and statistical physics. Some of the research lines I follow are:- I successfully developed and implemented novel machine learning models to study diffusion processes.- I successfully designed and implemented a reinforcement learning agent to obtain relaxations from hard quantum many-body problems. - I supervise a master thesis in reinforcement learning for quantum games.In have presented my results with 8 talks and 3 posters on international conferences, in addition to my publications (see my google scholar). Mostrar menos

  • PHD Student

    Mar 2020 - now

  • PhD trainee

    Sept 2019 - Mar 2020
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  Universitat politècnica de catalunya

  Sept 2019 - Sept 2021

  Adjunct professor

  I taught physics to first year engineering students. I received outstanding scores (4.7/5) in the official student survey.In 2020, I also taught part of the machine learning subject in the MSc in Photonicshttps://photonics.masters.upc.edu/en

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  Universitat de barcelona

  Sept 2020 - now

  Invited lecturer

  Since 2021, I am responsible for part of the machine learning subject in the MSc in Quantum Science and Technology https://quantummasterbarcelona.eu/In 2021 I taught part of the statistics and data analysis in the MSc of Multidisciplinary Research in Experimental Sciences. I received excellent student feedback (9.4/10) in the official survey. In 2020, I taught python for data analysis in the same master.https://mmres.bist.eu/

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  Xanadu

  May 2023 - Jan 2023

  Software development intern

  During my 4 months of residency, I first focused on developing PennyLane, an open-source quantum computing Python library. I mainly contributed in two areas: the development of novel features, and the performance optimization. For instance, I enabled the creation of projector operators in any arbitrary basis, and optimized the way sparse operators are created and operated with by exploiting properties of Pauli matrices, yielding speedups of several orders of magnitude. See these PRs for further details: https://github.com/PennyLaneAI/pennylane/pull/4192https://github.com/PennyLaneAI/pennylane/pull/4411During the latter stages of my residency, I conducted a brief research project on deterministic error supression with reinforcement learning. The main principle is to design a reinforcement learning calibrator that can learn about the specific properties of the qubits during training. This allows it to optimize the pulse shapes that execute the different gates in the quantum hardware to reach high gate fidelities with low noise. Mostrar menos