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Interview with David Cournapeau, Head of the MLE Team-Career①

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David Cournapeau joined Cogent Labs in August 2017.
As head of the Machine Learning Engineering Team, he plays a pivotal role at the interface between cutting-edge research and product development. Beyond his work at Cogent Labs, David is also well known in the open source community as the original author of scikit-learn, and a major contributor to NumPy and SciPy.

We sat down with David to learn more about his experiences and insight as a scientist, an engineer, a manager and an active member of the open source community.

In this first installment of a three-part interview series, David discusses his career to date and his work in the open source space. In the second and third installments, David shares his thoughts on developing products from research, and on working at Cogent Labs, respectively.

 

Part 1: Career – The voyage from Paris to Tokyo

Q: To start things off, could you please tell us about your academic background?

DC: After high school, I followed a fairly typical engineering path. In France, we have two years of something like a cram school. Then I went to an engineering school called Télécom ParisTech specializing in computer science and electronics. I studied signal processing, which is the mathematical underpinnings of how to treat signals.

At the end of that, because I was always interested in music, I did another master studying the different kinds of technologies that are applied to music, such as audio signal processing, acoustics, etc. I did that for one year.

During my master, I had to do a six-month internship to get my degree. This tends to be kind of boring. You usually just work in a Parisian suburb somewhere. I was really interested in Japanese culture so I thought it would be more interesting if I could do it in Japan. That is how I first ended up coming to Japan.

For my internship, I worked at the Advanced Telecommunications Research Institute International (ATR). This was a group of advanced research labs funded by the Japanese government aimed at fostering collaborative relationships with international researchers. I worked at a lab near Nara specializing in things like communication, neuroscience, and virtual reality. My internship was for six months, and because I enjoyed it, I stayed for one more year as a research engineer. Overall, I stayed there for around two years.

Then I got interested in doing a PhD. Through this work at ATR, the head of the lab recommended me to a professor at Kyoto University. I earned a scholarship from the Japanese government and did my PhD at Kyoto University from 2006 to 2009.

 

Q: What did you work on during your PhD?

DC: I worked on speech recognition. This was quite interesting time-wise because speech recognition is actually one of the first fields – if not the first field – that used what it is now called “machine learning.” A lot of the techniques that are now very common for image processing or a lot of what Cogent Labs or other AI companies do was used and pioneered by state-of-the-art research in speech recognition. From the 80s, state-of-the-art speech recognition research was about using statistical methods and a lot of data to train systems to do speech recognition. I think it was the first field to do this on a big scale.

 

Q: After completing your PhD, did you already have an idea of what kind of a career you wanted to pursue?

DC: After six years, for various reasons I wanted to do something else. In looking for a new job, for me, the chance to work on products was the most important factor. I did not want to do consulting anymore. Also, I knew that AI and deep learning were pretty big, and I did a PhD in a field that pioneered those techniques, so if I could work on products related to AI it would be even better. And, if I could do it in Japan, it would be even better still. That naturally led to my next position here at Cogent Labs.

I joined Cogent Labs in summer 2017 and I initially joined as a Senior Research Engineer. That is again a little bit similar to my past positions in that I am not as good at the science as the co-heads of the research department here but I have more of an engineering background, so the idea was that I could help bridge the gap between the researchers and engineering.

 

Q: So that’s how you ended up moving to industry?

DC: Yes. So, I joined a small company in Osaka, Silveregg, working on recommendation systems, in 2009. At the time I did a bit of algorithmic research. I worked a lot on the backend. It is a fairly common theme in my career. I tried to mix what you might call “pure” engineering and more machine-learning, statistics-based things.

I did that for a year and a half. It was an interesting experience. It was my first job where I had any kind of responsibility and I learned a lot, but after a year and a half I was a bit worried about finding a lot of job opportunities in Osaka as a foreigner and was a bit worried about my career. Ten years ago, there were not that many positions available, not even in Tokyo.

That is why I decided to go back to Europe. I had the opportunity to join another company, Enthought, where I knew a lot of people already. I worked there for six years. It’s an American company but they chose to open a small, ten-person office in Cambridge in 2011. I joined more or less when that started.

Enthought was a small and very technologically-oriented company in the sense that the founder had a PhD and 70% of the team had PhDs. The idea was that we would do scientific consulting for companies. A typical example is that a company would have a domain expert who has developed a nice proof-of-concept software. The company would then ask us, based on that, to develop a software that other people could use. They wouldn’t want the domain expert to do that and they couldn’t just ask a random software to do it because you needed to understand the science behind it.

We were kind of always at the interface between pure software engineering and science. The idea was that because most of us had a strong background in science, we could speak the same language as the scientists, but we were much better at software engineering than most scientists, so we could develop something that could be used not just by one or two people, but maybe across the company. That was the main value proposition of what we did.

During that time, I also took over more infrastructural backend work – work that could be reused across all our customers, particularly to make it easier to develop software.

 

Q: After that you joined Cogent Labs. Could you explain how that came about?

DC: After six years, for various reasons I wanted to do something else. In looking for a new job, for me, the chance to work on products was the most important factor. I did not want to do consulting anymore. Also, I knew that AI and deep learning were pretty big, and I did a PhD in a field that pioneered those techniques, so if I could work on products related to AI it would be even better. And, if I could do it in Japan, it would be even better still. That naturally led to my next position here at Cogent Labs.

I joined Cogent Labs in summer 2017 and I initially joined as a Senior Research Engineer. That is again a little bit similar to my past positions in that I am not as good at the science as the co-heads of the research department here but I have more of an engineering background, so the idea was that I could help bridge the gap between the researchers and engineering.

 

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