Some words from students on the École Centrale Engineering program
These students talk about the École Centrale Engineering program they are currently completing.
VIDEO : https://www.youtube.com/watch?list=UU-b_Xc3XZfqOX1P41XErV-w&v=TKgQ8-PWgPc
Students of the 2016 year group discuss their studies: international double degrees, Research track, streams, options, and more, and explore all the possibilities of the École Centrale Engineering program:
VIDEO : https://www.youtube.com/watch?list=PLgSHH6boFf5tzac4Tst_Kx2VUqAMIu7Xe&time_continue=8&v=GlTAGhfTk9o
Live 2016 Session with Campus Channel: Erick Herbin, Director of the Mathematics Department, responds to an online audience alongside student Anastasia.
A passion for Data Science
Increasing numbers of students are interested in the possible uses of Big Data. Some have completed the Data Sciences track and talk positively about their enthusiasm to pursue a career in this new field.
Jules Leidelinger
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Jules Leidelinger is a student engineer, double-degree graduate of ENS Cachan (MVA Master’s, Mathematics, Vision and Learning), currently completing his 2nd Master’s degree year in applied mathematics at École Centrale Paris. He was one of the first students to have completed the Data Science track within the Applied Mathematics option in École Centrale Paris. |
“It all started with algorithmic trading.”
CentraleSupélec (CS): Jules, what got you interested in Data Science? Why did you choose the École Centrale Data Science track?
Jules Leidelinger (JL): During my gap year, I had an experience of algorithmic trading, which really requires statistical learning, optimization and programming skills. I really enjoyed this experience and so I wanted to choose a track that would give me the opportunity to consolidate my knowledge in the subject.
The creation of the École Centrale Data Science track was a great opportunity for me, as it met my needs at the right time, and the quality of the classes exceeded my expectations. The track moreover facilitates application for the ENS Cachan MVA Master’s degree that I chose to pursue in parallel, as I wanted to learn about statistical applications and machine learning beyond finance.
The MVA Master’s also seems prestigious to me as it boasts instructor-researchers who are world renowned and are pioneers of machine learning, as well as the Nikos Paragios laboratory in École Centrale Paris, specialized in computer vision.
CS: Can you tell me about your admission to University College of London to complete a doctorate? How did it happen? Why did you apply?
JL: A combination of events associated with the École Centrale track led me to apply. I had been to an MVA class on kernel methods that I thoroughly enjoyed. The technical training I received in classes in the Mathematics Applied to Functional Analysis common core gave me the skills to understand this rather technical class.
Furthermore, an École Centrale professor who is a member of the Nikos Paragios laboratory introduced me to a kernel methods specialist at University College of London (UCL), which has a reputed laboratory for machine learning applied to functional analysis. I applied without hesitation. I had an interview with the instructor-researcher and was then invited for two days of interviews.
Interviews included the oral presentation of a project and results associated with machine learning as well as more traditional interviews, with math exercises to solve on the whiteboard. I presented my option project about Criteo, which was a machine learning application for online advertising, organized by the Data Science track in the Applied Mathematics option.
I could really say that it is almost entirely thanks to the Data Science track that I was able to successfully apply for this doctorate under excellent conditions.
CS: Why did you accept an internship with Crédit Suisse? What do you do in your internship? What interests you particularly? What do you feel you are learning? How can finance and similar fields interest engineers?
JL: I accepted an internship with Crédit Suisse because I really enjoyed my gap year experience working in algorithmic trading. I work in the research and development of automatic share trading strategies. This interests me, as it is a great way to apply statistics to anticipate market dynamics. Finance can be of interest to engineers because it involves challenges requiring complex problem-solving, often under pressure, for which our training seems to be the most appropriate.
Clément Nicolle
CentraleSupélec (CS): Clément, can you summarize your academic track in a few lines?
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Clément Nicolle (CN): Last year I completed my third year at the École Centrale Paris, studying the Applied Mathematics option, majoring in Data Science. In parallel, I completed Mathematics, Vision and Learning at ENS Cachan. I am currently completing my end-of-studies internship with Dreem, a young start-up specialized in neurotechnologies and specifically in sleep. I just have to give my oral presentation to complete my degree! |
“Motivated by applications in health, travel and culture”
CS: Why did you choose the CentraleSupélec Data Science track? What attracted you to this field?
CN: I had the opportunity to discover what the term “data science” includes concretely during the course of my second internship in my 2014 gap year. The terms “big data” and “machine learning” are constantly in the media, perceived as magic new technologies to improve our daily lives and foresee the future. Once you get past the buzz, you realize that there is nothing revolutionary about it. It is basically the use of statistical methods and innovative algorithmic techniques to analyze data that has been stored on computers for decades due to the lack of adequate processing power. That is what makes it so interesting!
I was initially attracted by the innovative aspect. From a theoretical point of view, machine learning algorithms are relatively recent and are now the focus of many research studies. From a professional perspective, we are seeing increasing companies seeking data scientists, which shows needs in this area are emerging.
I really like the fact that this is about math that is useful for people. What motivates me the most is applying big data to health, travel and culture. The numerous marketing applications of big data have never interested me. I need to know that my work will be aligned with my convictions to remain enthusiastic.
CS: What were your first jobs/internships as a data scientist?
CN: My first experience as a data scientist was during the second part of my gap year, when I was working for Evaneos, a brilliant start-up that puts travelers in contact with local tourism agents. At the time I was very far from having all the skills I later gained in the 3rd year at the École Centrale Paris. I nevertheless managed to develop a travelers’ recommendation engine.
I then gave a research seminar in the Math App option about Dreem, which is designing a connected headband to improve sleep quality. I specifically worked on automatic classification of sleep states. I am currently completing my end-of-studies internship.
CS: What have you learned from these experiences? Were they what you expected? Are you still interested in data science? Have you discovered new aspects or activities in data science?
CN: First of all, these experiences helped me to make the jump between the ideal and the real, from the teaching bubble to the working environment. The difference is that in the working environment, data don’t just fall from the sky or from a professor’s Dropbox. In fact, most of my time is spent understanding, extracting and cleaning data and the information they contain. This initial processing is essential for obtaining good results at the end and requires a good understanding of the environment from which the data are derived.
The Data Science track, joined with the ENS Cachan MVA Master’s, gives a vast overview of what we mean by machine learning. It is nevertheless always interesting to explore imagined avenues or what we’ve read about in articles. Discussions I have with colleagues or other start-ups about this are always very rich.
More than in data science itself, it is in the field in which I apply it, in neuroscience, that I make the most discoveries. In this way, data science can be an interdisciplinary means to exploring other horizons.
CS: What do you plan to do once you have completed your studies? What type of job? What type of company?
CN: I will continue to work with Dreem part-time. In parallel, I am registered to begin a philosophy degree with Nanterre University. Philosophy is a field I’ve been very interested in for a long time and that I have neglected somewhat during my time at the École Centrale Paris, so I’m happy to be able to focus on it once again. The original association between math and philosophy has been eroded with the growing progression of knowledge, but the two remain close in my view. I would particularly like to study the main schools of thought from the past from the perspective of the knowledge we have today in the field of neuroscience.