Over the last couple of weeks, we have been very busy expanding our Data Science team. Today, I would like to introduce our three latest additions to the data science army, Enrico Di Lello, Giuseppa Agueci and Karel Vanhoorebeeck.
What experience and skills are you bringing to the (data science) table?
Enrico: I graduated with a thesis about autonomous robots. I started my Ph.D. working with industrial robots, then moved to human motion analysis for clinical applications and I also worked with gas sensors. I guess one could say that I am not afraid to step out of my comfort zone, which is, at the moment, inference and learning methods for Bayesian time-series models. This means that I can bring some useful insights in the Data Science team, but I am really excited about what I can learn from them.
Karel: I’m a Junior profile, so I will have to shift to a higher gear to keep up with my colleagues. I have a strong theoretical background and some experience with several machine learning techniques. I’m confident that I will be able to learn fast and contribute to the company.
Giusy, you hold a professional doctorate degree in engineering, with a focus on smart energy buildings & cites. How do you think behavioral profiling could contribute to smart cities?
Giusy: I strongly believe that there is an urge to re-think our cities, to make them a better place to live and optimize resources at the same time. Behavioral profiling is key in this respect, as it provides a global picture of a city and allows a wise tailoring of the supply of goods and services. Thanks to an equilibrated management of resources and infrastructures, cities can maximize the quality of our lives, making people more connected and ensuring economic growth.
What is your definition of a data scientist?
Giusy: I like the description of a data scientist as “part analyst, part artist”. A data scientist is a hybrid figure with a unique melange of skills that allows him/her to translate raw data into quantitative understanding of human behavior, hence creating a business value.
Enrico: I think that Data Science is, unfortunately, a buzzword right now (like, for example “Big Data”). It lacks a precise definition, but since you ask, I’d rather put the emphasis on the word “Science”. I mean the Data is there. It has to be intelligently collected, handled and stored, but that’s not “where the money is”. The value is in the application of the scientific method to this wealth of information. This means that the first thing about Data Science, to me, is the question being asked. Before jumping into the last tool or software or algorithm, a data scientist should have a clear idea of what the hypothesis is that he/she is trying to validate.
Karel: A data scientist has to be an all-rounder with a strong theoretical foundation since data science is where a lot of different areas of computer science come together. The strong mathematical foundations of machine learning algorithms, the complexity of big data processing infrastructures, making sense of sensor data and so on: the data scientist has to be able to deal with it all, and be good at it too!
There is currently quite a lot of buzz around women (or rather the lack thereof) in the tech world. What is your view on the (unfortunately still) prevailing gender imbalance in this industry?
Giusy: I believe that Math, Science, Engineering and Computer Science are absolutely not male subjects, and are only social and environmental factors contributing to the under-representation of women in science and engineering. I am a strong supporter of gender balance in any environment, including of course the tech word that would definitely benefit from a larger female presence. I believe that things might quickly change if everyone who shares my same view would play an active role in the society, fostering the removal of cultural barriers and encouraging women in pursuing scientific and technical careers.
Enrico: I’ll try to give you a short answer, but I realize this is a delicate and complex matter. Should women be encouraged to work in STEM? Yes. But I do think that the policies to balance the current situation are often too late in the process. They should take place in primary schools, and even those can’t be sufficient. Society in general, and families in particular, have the responsibility to motivate young girls to follow their inclinations, regardless of gender stereotypes.
Karel: It’s unfortunate that not more women are active in the tech industry, and computer science in particular. Our economy will probably always have an extreme lack of technically skilled people, so the more the merrier, I would say.
What will your main priority be in the next couple of weeks?
Giusy: My focus in the next couple of weeks will be the quality evaluation of the attributes that define the behavioral profiling such as for instance the time of the first morning activity or the most frequented city or the most visited supermarket, …
Enrico: In the next couple of weeks I will try to get up to speed with the current software architecture. Once that is done, I will be working on context predictions.
Karel: Getting up to speed with everything happening at Sentiance and to learn as much as possible in order to be able to contribute to the team as fast as possible.
Have you installed our demo app already? What do you think your sensor data will say about you?
Giusy: I was very curious to install Journeys and to learn from it what type of person I am I assume my sensor data will confirm my dynamic and energetic lifestyle!
Enrico: I just installed it. It will probably say that I move around the city using a bike and public transports, and that I visit the closest Crossfit box quite often. I will probably leave the phone at home during the weekends :-).
Karel: When I get my new smartphone, installing the app is on the top of the to-do list. I expect the app to find out that I’m an early bird and that I like to alternate sportive, outdoor activities with relaxing at home or at a bar.
Welcome to the Sentiance team!