Hi! My name is Daniel. I am a software engineer at Google in Zürich. I am currently working on making the Google Assistant smarter using machine intelligence technology.
My research interests cover recommender systems, information retrieval, machine learning and natural language processing. Before working at Google, I did my PhD at the Information Retrieval Lab of the University of A Coruña. My PhD thesis explored the relationships between information retrieval and recommender systems fields.
Updated: Jun 21, 2020.
from June 2019 to present day
Working on machine intelligence technology to make the Google Assistant smarter.
from September 2015 to present day
Worked on my PhD thesis under the support of a FPU fellowship, the most competitive grant from the Spanish Government that funds the development of a PhD thesis during four years. During this period, I was also a Lab Instructor for several BSc and MSc courses: Information Retrieval, Information Retrieval and the Semantic Web, Operating Systems, Software Developer Tools, and Software Methodologies.
from May 2018 to August 2018
During this software engineering internship, I worked with the Meeting Room Intelligence team developing solutions to improve Google's meeting technologies.
from April 2017 to June 2017
3-month research stay funded by the Spanish Government at the Information Retrieval Group (UAM) under the supervision of Pablo Castells. I studied the applicability of information retrieval evaluation metrics to recommendation.
from April 2016 to June 2016
3-month research stay funded by the Spanish Government at the High Performance Computing Lab (ISTI-CNR) under the supervision of Raffaele Perego. I worked on novel recommendation tasks and on the adaptation of learning to rank techniques to recommendation.
from April 2014 to April 2015
Hired by the IRLab to work on distributed recommender systems for big data environments using MapReduce and NoSQL technologies. I also focused on studying and improving accuracy, diversity and novelty of different approaches to recommendation.
from November 2013 to July 2014
Competitive grant from the Spanish Government for outstanding students that are interested in pursuing an academic career in the last year of their degrees.
Developed a distributed recommendation platform with several collaborative filtering algorithms using Hadoop and Cassandra, Django, Lucene, Memcached and Varnish.
from November 2014 to May 2019
PhD thesis: Information Retrieval Models for Recommender Systems.
Grade: Sobresaliente cum laude (highest grade).
Distinction given by the University of A Coruña to the best PhD theses defended at this university.
Won the Young Researcher Modality of the Research Awards given by the Spanish Computer Science Society and the BBVA Foundation in 2020: press note.
from October 2009 to July 2014
Grade: 9.32/10 (with honours)
A distributed recommender platform capable of making personalised recommendations using collaborative filtering techniques in a big data environment.
Grade: 10/10 (with honours)
Valedictorian distinction given by the University of A Coruña to the students, one for each degree, with the best marks among all the graduates that finished their studies in 2014 at the University of A Coruña.
Valedictorian distinction given by the Reginal Government of Galicia to the students, one for each degree, with the best marks among all the graduates that finished their studies in 2014 in the Galicia region.
Distinction was given by the Spanish Ministry of Education to those students with the best marks among all the graduates that finished their studies in 2014 in Spain.
Coursera - deeplearning.ai specialization on Deep Learning taught by Prof. Andrew Ng in 2017-2018.
Stanford Coursera specialization on Probabilistic Graphical Models taught by Dr. Daphne Koller in 2016-2017.
Stanford Lagunita course on MWriting in the Sciences taught by Dr. Kristin Sainani in 2015.
Stanford Coursera course on Mining of Massive Datasets taught by Dr. Jure Leskovec, Dr. Anand Rajaraman and Prof. Jeffrey Ullman in 2015.
2014 edition of the Machine Learning Summer School at Carnegie Mellon University (Pittsburgh, PA) organised by Prof. Alex Smola and Dr. Zico Kolter.
Stanford Lagunita course on Statistical Machine Learning taught by Prof. Trevor Hastie and Prof. Rob Tibshirani in 2014.
Caltech edX course on theoretical and applied Machine Learning taught by Prof. Yaser S. Abu-Mostafa in 2013.
Stanford Coursera courses on algorithm design and analysis taught by Dr. Tim Roughgarden in 2013.
Michigan Coursera course on model design taught by Prof. Scott E. Page in 2012.
Berkeley Coursera course on Quantum Computing taught by Prof. Umesh V. Vazirani in 2012.
Stanford Coursera course on Machine Learning foundations taught by Dr. Andrew Ng in 2012.
Udacity course on Artifical Intelligence foundations taught by Prof. Peter Norvig and Prof. Sebastian Thrun in 2011.