5 questions with Jakub Nalepa

Our guest is Jakub Nalepa, Head of Artificial Intelligence department at KP Labs, Associate Professor at Silesian University of Technology, and Machine Learning Architect at Graylight Imaging

Jakub answers Ewa’s 5 insightful questions.
– what encouraged him to choose his career path
– what he has learned from his mistakes in the past
– what advice he would give to his younger self
– what benefits and opportunities data and its analysis have to offer
– what are the advantages of peer review in the world of science and business.

Ewa Banaś: Hi, I’m Ewa Banaś. Welcome to our new IT Insights series: Five Questions. In this series, we will discuss our speakers’ experiences and lessons learned. Today my guest is Jakub Nalepa. Great to have you here.

Jakub Nalepa: Absolutely, my pleasure.

Ewa Banaś: Before we move on to the questions, could you introduce yourself a little bit to our audience?

Jakub Nalepa: Yeah, sure. So maybe a long story short, I got PhD in 2016 in machine learning and after that I actually joined the university and I try to bridge both worlds: scientific and industrial. I’m an associate professor at Sailors University of Technology and I also work at KP Labs where I am a head of AI Department trying to, you know, oversee all activities that are concerned with AI in, for instance, remote sensing, but also I… I’ve been doing medical imaging for years already, almost 15-ish, and that is also an important part of my work to work.

Ewa Banaś: Thank you for that intro. I’m really looking forward to our talk. So let’s begin. You have a vast experience in machine learning and data analysis, but if you weren’t doing this—I mean being machine… being a machine learning architect and associate professor—what would you be doing career-wise?,

Jakub Nalepa: Right. So you’re launching difficult questions for yourself?

Ewa Banaś: That’s my task here.

Jakub Nalepa: So I think I must say that I always, you know, enjoyed solving problems and I… I had a very early start. During my studies I started working on data analytics and I saw a great potential in the data to acquire every year. So even 15 years ago we observed that there is huge amount of data being acquired and stored, so I wanted to make sure that we can benefit from that. And I think if I were not to be a machine learning, you know, person, I would probably go with medical imaging field or medicine in more general because the problems that I tackle every day are in my opinion important. So I try to bring value to the end user. I want to make sure that we can benefit from the data we capture, but on the other hand it is nice to see that things are impactful, so we can, you know, make change in different fields. So that could be for instance medicine, it could be geoscience or remote sensing. I think I would just go for solving difficult problems in the base… in the best possible scenario for a good reason, and this good reason is to have impact.,

Ewa Banaś: Mm-hmm. You know, from what you’re saying I can all… I can hear is data, data, data somewhere there in the background.

Jakub Nalepa: Yeah, absolutely. And we gather data but we need to get knowledge from this data.

Ewa Banaś: Definitely. And when you look at your career, what advice would you give to the 20-years-old you?

Jakub Nalepa: I can say that I think I’m fortunate enough to say that I really enjoyed my career because, again, that was a very, very early start. I started working on this during my studies and I think science is the art of asking good questions. You need to have a good question to, you know, dig into your problem that you want to understand. And I think the most important advice would be to ask questions and not to be afraid of asking questions, even if you talk with more experienced people, because that is where you learn and you need to learn from experience. So why not to exploit the knowledge that’s already in the… in, you know, more experienced and more senior people in your field? So I think asking questions, not being afraid of asking questions, is from my point of view super important and it can, you know, make change.,

Ewa Banaś: It all starts from the good questions, doesn’t it?

Jakub Nalepa: Absolutely, yeah, it does.

Ewa Banaś: Yeah. We all learn by our mistakes and I’m curious if there was a moment in your career that, uh, when you made a mistake and you learned something really important out of it?

Jakub Nalepa: Lots of them. I think every experiment could be, you know, successful or not successful. And if we do not succeed with a single experiment, we can learn from our experience. And if we, for instance, have a hypothesis on some phenomena that we want to, you know, verify, we can get negative results of our experiment and that is also very important because we can again learn from this and we can try to fine-tune our approach to tackle the problem in an appropriate way. So there are lots of, you know, problems that you may tackle every year, every day, and during your career, but you need to learn from them. So yeah, I think that is a super important part of our learning experience: to… to be able to benefit from your failures. And you know, the failures come and this is something which is absolutely normal for junior and for senior people as well. The question is how to benefit from this.,

Ewa Banaś: Exactly. And have you got any particular example in mind that you could share? For the failure?

Jakub Nalepa: I think I won’t say it is a failure as such, but I think that might be an example from the projects that we had recently. We were designing the algorithm for segmenting lesions in brain—in magnetic resonance imaging of brain of children—and the data was very difficult, which means that it was very imbalanced, we had, you know, the quality of data was not very nice, and we need to somehow resolve that problem. And for the very start of the project, we basically failed. We had the algorithm that worked for adults and it totally failed for children. It is good because we always want to understand why things fail. And if you understand this—why they failed—you can fine-tune or redesign your approach to better solve this problem. So that was a matter of fine-tuning or, you know, updating the technique to a specific problem. And at the end of the day we succeeded and we… we do have the algorithm that works for that kind of difficult data.,

Ewa Banaś: Pretty great example. Thank you for sharing this one.

Jakub Nalepa: Yeah. There were some situations where we had the data of low quality and that was the most important part of the entire project: to make sure that we are in a very good starting point to move forward. Because afterwards we can… at the end of the day, we learn from data. Either data it’s low quality, then it is likely that your technique would be of low quality as well. Look points are very important in data but generally in life, you know, make a hard stop and think generally and also to track because you can quantify the progress of your project even if we look at the scientific projects it is perfectly fine to track and you should do that to make sure that you know where you are with your project and with your ideas.,

Ewa Banaś: Definitely. And set goals so that you know where you’re going.

Jakub Nalepa: If we can quantify them, we are in a perfect place.

Ewa Banaś: Definitely. Looking at the global situation and how it may influence IT industry, what challenges you predict you’ll have to tackle in the near future?

Jakub Nalepa: There are lots of changes in different fields. Again, we acquire a lot of data, so the amount of data is enormous. So we… every single second we gather more data, which is difficult. And this difficulty comes from the fact that we captured the data in a continuous way. The data might be multi-modal, which means that we have different, you know, features of data and different characteristics of the data. The quality of the data might be different, so we cannot assume that we have good data and we… we can get knowledge from this.,

Jakub Nalepa (continuing): And I think making sure that we can benefit from those heterogeneous data in a particular field—for instance in medicine, we know that COVID-19 outbreak was a huge thing worthwhile—so we need to be prepared for the future because it’s likely that we need to, you know, face that kind of problems in the nearest future as well. So we need to know how to utilize the data that we have, because we know that we have lots of data but this data should be utilized in a good way to make sure that we have robust and, you know, correct answers coming from data. And medicine is one example, but on the other hand we may look at remote sensing where we have, you know, you can capture images on board the satellite, which means that you can cover a lot of information of our globe, of the world, but the question is how to get the knowledge from raw pixels. Because the pixels are just the data. The question is how the user, the end user, could benefit from this. Could we have better agriculture? Could we have, you know, tracking processes across the globe? And that kind of I think questions are very important and are getting even more important nowadays.,

Ewa Banaś: Definitely. We see it by our own eyes right now. And I know that the academic context is very close to your heart, being an associate professor. I’m wondering how you perceive conducting research and doing business? What’s the alignment there?

Jakub Nalepa: I think bridging those two worlds is important because in the scientific world we publish. So we designed the algorithm, we verify it, we validate it, and then afterwards we publish. And this process of publishing is important because when you submit a paper, for instance to a conference or the journal, you know that other experts in the field would look like… would actually look at this paper and verify if it’s correct, if it makes sense. So we have this peer review process, which means that at the end of the day, if you have the paper published that was already reviewed, which means that it is a good evidence that your things work in practice. And that is something which is also important in industry, because you want to make sure that things work, but you also need to be able to prove that your things work correctly over the data you expect. For instance in medicine it’s again very important to make sure that you have your techniques already validated because they may also impact patients, especially in medicine I guess.,

Ewa Banaś: Yeah. Thank you for our talk today and for sharing your experiences with us and with the audience. And thank you our listeners for listening to yet another IT Insights episode.

Jakub Nalepa: Thank you and see you soon.