David Pujol Perich

My work

In this page I summarize some of the main publications, projects and accomplishments from my academic and professional career.

Selected publications

- Unveiling the potential of graph neural networks for robust intrusion detection, ACM Sigmetrics 2022

- IGNNITION: Bridging the gap between graph neural networks and networking systems, IEEE Network 2021

- NetXplain: Real-time explainability of graph neural networks applied to networking, ITU Journal 2021

- The Graph Neural Networking Challange: A Worldwide Competition for Education in AI/ML for Networks, ACM SIGCOMM 2021

Projects

IGNNITION

Resulting from my work in my Bachelor Thesis, my research lab (Barcelona Neural Networking Center) received a European grand to further develop our project, IGNNITION.

This is a framework that provides a code-free interface that permits researchers and practicioners to implement in a matter of hours (instead of days/weeks) potentially very complex Graph Neural Network models. Concretely, complex PyTorch/Tensorflow implementation is substituted by simple natural language descriptiors flexible enough to implement virtually any GNN.

To date, IGNNITION has received over 19k downloads!

Main Accomplishments

- 3rd prize in the Student Research Competition, organized by SIGCOMM '21 (sponsored by Microsoft).

- Awarded as the second-best Bachelor Thesis of my faculty (2020)

- 1st prize in RareHacks for a chatbot that helps patients with pediatric cancer (2019)

About

My name is David Pujol Perich. I am currently a Ph.D student at Hubpba lab from Universitat de Barcelona, where I study the application of efficient Transformer models to video understanding-based tasks. Find my CV here.

I obtained my Bachelors degree in Computer Science at Universitat Politècnica de Catalunya (UPC), which I combined with a study exchange at ETHZ, Switzerland. I also obtained my Masters Degree at UPC in Research and Innovation in Informatics with a strong focus on Advanced Computing. I also combined this program with a 1 year exchange at EPFL, Switzerland, where I undertook both courses on Deep Learning and my Master Thesis.

During this time I also combined my studies with professional experience. First of all, I worked as a Machine Learning Research intern at Barcelona Neural Networking Center during almost 2 years. I then worked at LIONS, EPFL as a Machine Learning Research Assistant during 1 year.

All in all, my research interests lay in the field of Deep Learning in general and their application in fields such as Computer Vision or NLP.

Contact

• Linkedin
• GitHub
• Instagram

Elements

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