I did research in Artificial Intelligence, mainly on Machine Learning, Computer Vision, and its application to Decision Support Systems in medicine. I learned to ask questions, dig beyond the surface of the problem, and get obsessed with them until I thoroughly understood the process so I could make actionable decisions.Part of this process translated into multiple research papers, supervision of MSc students, and, most importantly, how I handle entrepreneurship and consulting. Every time you get a challenge, go beyond its surface and discover the patterns to solve such challenges repeatedly with consistent outcomes.Technically, some of my responsibilities at INESC TEC were:* Research on Machine Learning and Computer Vision.* Supervising MSc students and interns.* Co-organizing workshops on Machine Learning and VISUM summer school.* Writing research Grants and funds raising.

I developed several AI-based solutions for multiple industries (marketing, ticketing, education), covering domain areas such as Recommender Systems, Natural Language Processing, Machine Learning, and Social Network Analysis.I worked on the design, planning, analysis, and execution of AI-based solutions, leading a team of Data Scientists through the process.I participated in multiple acceleration processes (from nomination to the process itself), including Wayra (Telefónica), Venture Hive, and Blackbox (Google for Entrepreneurs).

During my last year at college, I co-founded MataTuTigre with two other friends. It was a nice (and naive) initial attempt at entrepreneurship and a very enriching one. Matar tigres (literally "killing tigers") is a Venezuelan expression for doing a gig (freelance work). We launched the platform and managed to acquire a few thousand users. Venezuelan job legislation changed at the time, and we would have to hire all freelancers on the platform, which turned the business model unfeasible.Three mistakes we made when working on this project:1. Focusing too much on the technology instead of validating the idea.2. Having a too-technical team. We were all in software and brought the same time of skills.3. Not being able to pilot when legislation changed.

I worked as a backend developer for Talpor for a few months at the end of my undergraduate studies. Besides backend development, the work included NLP applications (NER, sentiment analysis) to detect the public brand perception of key stores in the city.It was a great experience with a local team in Venezuela that was driving a lot of innovation. However, I quickly learned that I didn't want to get away from AI and entrepreneurship.

Teaching is learning twice. I have always loved to exchange ideas with others, and the classroom (physical or digital) is a great space to do it. You get the opportunity to think about critical challenges, draft multiple scenarios and understand how you would act in each case, training your logical reasoning and decision-making process in any field.The experience gained through this stage helped me build a learning-based consulting style, prioritizing my client's learning beyond just giving my final response. The most important outcome of any consulting session is providing the client with the tools to solve the problem himself the next time it happens.During my undergraduate studies, I worked as a Teaching Assistant in several domains, including:* Algorithms and Data Structures (Introduction to programming, data structures, and graph theory).* Artificial Intelligence Lab (mainly Machine Learning).* Symbolic logic.* Calculus.I also developed tons of academic material for the Computer Engineering department. Those materials are used as part of the course curriculum.As a result, the University recognized me for "Outstanding Work as Teaching Assistant" in 2012.