I received an M.Sc. in Communication Systems from the Swiss Federal Institute of Technology in Lausanne (EPFL) and a B.Sc. in Telematics Engineering at Comillas Pontifical University, in Madrid.

Experience

2019 · Machine Learning Engineer Intern at Schindler

During my time at Schindler, I designed and implemented a pipeline to classify patent documents into categories that facilitate the matching of patents to specific elevator systems and components. I had the opportunity to work with a patent attorney and a product engineer during the entire process, allowing me to make sensible decisions regarding the design of the pipeline and evaluate the final results. I submitted this work as my M.Sc. thesis.

2018 · Data Scientist Intern at Dathena

I worked as an intern at Dathena, a data security company based in Singapore. At the time of my internship the company still had not exceeded 50 staff, making it possible to learn about the tasks carried out at each department. During my time at the company, I deployed new features to the data protection pipeline and worked on an OCR pipeline for noisy documents with the data science team.

University Labs

2018 · HCI in Learning and Instruction Lab (EPFL)

I analyzed writing strategies and roles taken by students during online collaborative writing tasks. As part of this project, I defined a set of features to analyze paragraph quality and consistency and calculated the semantic similarity between authors at different stages of the writing process.

2017 · Big Data Lab (Comillas Pontifical University)

As part of a collaboration grant, I deployed a pipeline using Kafka, Flume and Cassandra as streaming data sources for text analysis with Spark. In addition, I prepared a distributed programming environment with Docker and Spark and tested the capabilities of bluetooth sensors as an input to the processing pipeline.

2016 · Transp-OR Laboratory (EPFL)

I completed my B.Sc. thesis as a research assistant in the Transport and Mobility Lab. I worked on the statistical analysis of passenger satisfaction in railway networks, with the aim of identifying the factors with the greatest impact on passenger satisfaction.

Relevant coursework

2019 · M.Sc. Communication Systems (EPFL)

• Machine Learning – Analysis and implementation of fundamental techniques in ML using Python. The final project for this course consisted in the implementation of a recommender system for movies based on user ratings, for which we used an ensembled approach (see on GitHub).
• Deep Learning – Mathematical basis and use of deep learning methods, as well as applied problems with PyTorch. For this course we trained a model based on LSTM and CNN to predict finger movements from electroencephalograms (see on GitHub).
• Data visualization – Created an interactive data visualization for insights on bills introduced in US Congress with D3.js and React.js (GitHub repository, deployment).
• Distributed Information Systems – Key concepts and algorithms in information retrieval and data mining using Python.
• Automatic Speech Processing – Formalisms, models and algorithms for the implementation of advanced speech processing applications.
• Applied Data Analysis – Development of data science pipelines with emphasis in practical skills using Python and several libraries for data science, including: Pandas, NumPy, SciPy, Matplotlib, Seaborn, Scikit Learn, Spacy, NLTK, BeautifulSoup and requests.

2016 · B.Sc. Telematics Engineering (Comillas Pontifical University)

• Architecture of Network Systems – Python Flask application for language translation and visual recognition with IBM Cloud, using Travis CI (no longer hosted).
• Computer Networks – Cisco CCNA equivalent training using industry level network equipment.
• Operations Research – Optimal coverage in hexagonal grid with Linear Programming.

2016 · Exchange Program in Communication Systems (EPFL)

• Algorithms – Analysis of computational complexity, modern algorithms and data structures.
• Functional Programming – Software project implementing a LISP interpreter using Scala.
• Parallelism and Concurrency – Functional parallelism using the actor model with Scala.