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Data Scientist - Machine Learning, Data Mining, Statistics
I am an Electrical Engineer turned into a Data Scientist. I always liked exploring, visualizing and interpreting data to better understand phenomena. I am interested in the tools used in the process of making sense from data. I have good analytical skills and an eye for detail. For seven years, I worked as a research fellow at two laboratories located at University of Porto, Portugal on topics such as signal processing, wireless networks, and information theory. More recently I worked on two machine learning projects applied to clinical data: 1) I built a model to detect heart beat anomalies, 2) I used mammography data to build predictive models for breast cancer. I also had experience working with a multidisciplinary team (people from electrical engineering to computer and medical sciences) and with researchers from top universities (Carnegie Mellon, MIT).
Pontifícia Universidade Católica do Rio de Janeiro
Ph.D, Electrical Engineering
January 1, 2003 – January 1, 2007
Pontifícia Universidade Católica do Rio de Janeiro
M.Sc., Electrical Engineering
January 1, 2001 – January 1, 2003
Pontifícia Universidade Católica do Rio de Janeiro
Diploma, Electrical Engineering
January 1, 1995 – January 1, 1999
Centro Hospitalar São João
Data Scientist
May 1, 2017 – November 1, 2018
Porto Area, Portugal
INESC Technology and Science - Associate Laboratory
Machine Learning Specialist
September 1, 2014 – November 1, 2015
Porto Area, Portugal
Instituto de Telecomunicações
Data Mining and Signal Processing Expert
June 1, 2012 – May 1, 2014
Porto Area, Portugal
Instituto de Telecomunicações
Wireless Communications and Networking Researcher
December 1, 2008 – June 1, 2012
Porto Area, Portugal
Pontifícia Universidade Católica do Rio de Janeiro
Post-Doctoral Researcher
January 1, 2008 – July 1, 2008
Rio de Janeiro Area, Brazil
Pontifícia Universidade Católica do Rio de Janeiro
Teaching Assistant
March 1, 2002 – July 1, 2008
Rio de Janeiro Area, Brazil
Machine Learning Papers
October 1, 2017 – Present
Summary of Machine Learning papers. Topics range from theory to ML applications.
SIBILA - Towards Smart Interacting Blocks that Improve Learned Advice
October 1, 2014 – June 1, 2015
The SIBILA project aims at developing a set of tools that will effectively support the decisionmaking process in organizations, and facilitate the process of building data-driven solutions. These tools will be able to process and represent complex sources of data, such as multirelational and/or web data, and to, given a user-defined task, semi-automatically select the best components at hand and compose them together. To do so, SIBILA will require progress at the level of knowledge representation and data mining techniques. Moreover, the complexity of SIBILA will require contributions in software engineering and language development. Next, we discuss in more detail the main challenges facing SIBILA: knowledge representation and inference, web data, learning technology, and system engineering.
DigiScope - Digitally Enhanced Stethoscope for Clinical Usage
June 1, 2012 – May 1, 2014
The DigiScope project aims at developing a digitally enhanced stethoscope capable of using state of the art technology in order to help physicians in their daily medical routine.
DRIVE-IN - Distributed Routing and Infotainment through Vehicular Inter-Networking
September 1, 2009 – June 1, 2012
Project DRIVE-IN (Distributed Routing and Infotainment through VEhicular Inter-Networking) is a research project approved within the 2008 Call for Proposals for the Information and Communication Technologies Institute (ICTI) and the Carnegie Mellon University - Portugal Program (Carnegie Mellon|PORTUGAL) by the Fundação para a Ciência e a Tecnologia (FCT). DRIVE-IN is included in the research program on New Generation Dependable Trusted Networks and Telecommunications Policy (NGN), addressed by the 2008 Call. The goal of DRIVE-IN project is to investigate how vehicle-to-vehicle communication can improve the user experience and the overall efficiency of vehicle and road utilization. DRIVE-IN addresses both foundations and applications of inter-vehicle communication. Concepts, methodologies and technologies will be developed in the three main research thrusts: Geo-optimized VANET protocols, intelligent and collaborative car routing, and VANET applications and services. These research thrusts shall fertilize horizontal activities covering realistic large-scale simulation and massive real-life experiments in urban environments.
N-Crave - Network Coding for Robust Architectures in Volatile Environments
December 1, 2008 – December 1, 2010
The simple, yet disruptive idea of Network Coding (NC) is that nodes will no more only forward but also process and mix the incoming independent information flows. The revolutionary paradigm has the potential of realizing multi-fold performance gains. It is thus expected to change the way we perceive, architect, organize and control networks and foretells deep impact in a wide range of areas such as network topology formation, error resilience, resource sharing, flow control interactions, and tasks such as content delivery, network monitoring and security.
Leaning from Data
edX
June 24, 2026 – Present
Data Analysis and Statistical Inference
Coursera
June 24, 2026 – Present
Networked Life
Coursera
June 24, 2026 – Present
Reproducible Research
Coursera
June 24, 2026 – Present
Exploratory Data Analysis
Coursera
June 24, 2026 – Present
Statistical Inference
Coursera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Getting and Cleaning Data
Coursera
June 24, 2026 – Present
R Programming
Coursera
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily academic and research-oriented, with projects focusing on theoretical and applied research in various domains (medical, vehicular networks, network coding). While this demonstrates intellectual curiosity and a strong foundation in data-related fields, the project descriptions lack explicit collaboration details or business impact, which are often key in corporate cultural fit. The transition from a purely research environment to a typical 'Data Analyst' role might require adaptation to business-driven objectives and faster iteration cycles. The diversity of research projects indicates a broad interest in complex problems.
Soft Skills & Operational Fit
The candidate's extensive research background suggests strong analytical and problem-solving skills. The teaching assistant role indicates an ability to explain complex concepts. However, the provided data does not offer direct insights into other soft skills like teamwork, leadership, or adaptability in a corporate setting, nor does it detail operational fit beyond technical capabilities.