Mayowa Olaide Olapade: But does it work in the real world?

On 7 October, Mayowa Olaide Olapade defended his doctoral thesis “Human-centered autonomous drone systems”. We asked Mayowa to reflect on his doctoral journey.

If you had to describe your PhD in one sentence today, what would it be?

“How can autonomous systems work?” to, “Okay, but can they actually behave properly in the real world, around real people?”

What is the biggest misconception people have about doing a PhD?

I think people imagine a PhD is mainly about being very good at your field. I’ve discovered it’s also about being very comfortable with being wrong. You can spend weeks convincing yourself that you have a brilliant idea, only to discover that your argument has a small hole in it big enough to drive a truck through. Then you go back, rethink it, and try again.

How did you first discover the research topics that eventually led you to your PhD?

Straight out of my master’s thesis, I had a supervisor who saw something in my work and suggested that it could become something bigger. I decided to give it a shot, then doubled down on it a few times. Somehow, what started as my master’s thesis turned into something I’ve genuinely enjoyed doing for the past four years.

Why did Estonia feel like the right place to pursue this journey?

I liked that the research environment gave me enough freedom to explore my own ideas while still having people around to challenge them. And Estonia is such a digital society that technology is not just something you talk about in a lab. You see it everywhere. For someone interested in pervasive and autonomous systems, that felt like a pretty good place to be.

Why did you decide to pursue a PhD in the first place?

During my master’s thesis, I became much more fascinated by the experiments than I expected. I enjoyed collecting data, playing around with it, and then seeing an interesting pattern emerge from something that initially looked like a pile of numbers. At first, I thought that was research. Have a cool idea, run an experiment, get a cool result. Then I got the shocking revelation that research is actually much more complicated than that. There’s the question, the methodology, the things that refuse to work, the reviewers, and the very humbling experience of discovering that your “obvious” conclusion isn't quite so obvious. Somehow, I found all of that interesting enough to want more.

What excited you most before you started?

Honestly, the freedom. I liked the idea that I could wake up and spend my day investigating a question that I genuinely found interesting. I just didn't realise that the freedom also comes with reading 37 papers when you only wanted to answer one small question, rewriting the same paragraph seventeen times, and debugging something that worked perfectly yesterday.

What mindset did theory train you in that you still rely on today?

Theory basically trained me to be a professional doubter. The slightest error in your theoretical “bible” can convince you that you're doing everything right when the whole thing is built on a bad assumption. So even now, when an experiment gives me a result that looks great, my first reaction is sometimes, “Okay... false positives eeeh?”

Was moving toward interdisciplinarity planned, or did it happen gradually?

It happened gradually. I didn't start with a perfectly defined path from computer science to AI and then to real-world applications. In fact, it started much earlier, when I was interested in environmental science and curious about how different materials could be identified across different environments. That curiosity gradually pulled me toward sensing and technology, and eventually into computer science. Then I started asking a slightly different question: can these technologies actually work outside a controlled laboratory or a very sophisticated installation? That question opened a whole new door for me. Suddenly I was working with autonomous drones, small robotic platforms, sensing, AI and eventually generative AI. Looking back, the projects connect surprisingly well. At the time, though, I was basically following the next interesting problem and hoping it would make sense later.

What was most intimidating about stepping outside your original field?

Not knowing enough. You enter a new field and suddenly everyone is casually throwing around twenty abbreviations you have never heard before. But I also learned that you don't need to know everything. You need to know enough to ask good questions, recognise what you don't know, and find people who know the things you don't. That has actually become one of my favourite parts of interdisciplinary research.

What turned out to be harder during your PhD studies than you expected?

Knowing when to stop. There is always another experiment you could run, another paper you could read, another baseline you could compare against, or one more tiny thing you could improve. At some point you have to look at the work and say, “Okay, this is enough. Go home.” That sounds simple. It is not.

When did you first feel the thought: maybe I’m not good enough for this?

There were definitely moments like that, especially after an experiment completely refused to cooperate or when a paper came back with feedback that felt like the reviewer had personally declared war on my research. But eventually I realised that struggling doesn't necessarily mean you're not good enough. Sometimes it just means you're doing something difficult. And sometimes... the big bad wolf (reviewer) is right and you need to fix it.

When did you truly realise how messy and imperfect data can be?

Absolutely. And it hurts a little because by the time you realise it, you've probably already spent a lot of time convincing yourself that the idea is brilliant. Sometimes something looks fantastic on paper and then completely loses its charm when you put it on a real system or expose it to real data. I've learned that you can't be too emotionally attached to an idea. The experiment gets the final vote.

Have you ever had to abandon a “beautiful” idea because it did not work in practice?

Yes, and I think that's one of the most important lessons I've learned. Sometimes an idea looks very convincing on paper but becomes much less convincing when you put it on a real system or expose it to real data. I've learned not to become too attached to an idea just because I spent a lot of time developing it. The experiment has to be allowed to win.

What has been the most difficult moment of your PhD so far?

A year in, I felt like I was wasting my time learning tricks that would never work. It dawned on me for a period but I guess that was the same feeling Batman felt without Robin.

How do you deal with rejection—papers, grants, feedback?

I almost had a psychological war going on with my Reviewer 1s for a while. I used to wonder, “Why are you doing this to me?” Then I became a Reviewer 1 myself for a workshop track, and suddenly everything made sense. I realised that once an idea isn't interesting enough, it becomes surprisingly difficult to convince someone that it is. I guess what goes around comes around! Now I take rejection a little less personally. Sometimes the reviewer is wrong, sometimes I'm wrong, and sometimes the idea just needs to be made much more interesting.

What part of your work do you quietly enjoy the most?

I would say the design thinking part, it can be exhilarating when done right.But more importantly when your paper is accepted. The feeling is 3-4 experts at least found your contribution worthy.

What do you enjoy doing when you are not thinking about research?

Football, movies and anime. Basically anything where nobody asks me to explain my methodology.

Do you have any hobbies that help you disconnect?

Football definitely helps. Watching a match gives me two hours where I'm worrying about whether my team will score instead of whether my experiment will work. Movies and anime are also good escapes. Research can occupy your head even when you're not physically at work, so sometimes you need something completely unrelated to take over the screen.

What was your first impression of Estonia?

Coming from Nigeria, Estonia was a very different experience. I think the weather introduced itself before the people did. But I also noticed how quiet and organised things felt. Over time, I came to appreciate the calmness, the nature and especially how much of everyday life can be handled digitally.

What do you now appreciate most about living here?

The calmness. After several years here, I've become quite attached to having space to think and work without everything feeling rushed. And I've also realised how convenient Estonia is digitally. You don't appreciate it fully until you imagine having to do some of those things manually again.

Is there something Estonians do that still makes you smile?

Every one is a closed book until you meet up around 02:00am somewhere in the city center

Did you try to learn Estonian?

Yes. I completed A2, which means I have officially survived the first boss level. I'm still very much learning, though. Estonian is one of those languages where you learn a word and then discover that the word has several endings that apparently all need to be negotiated separately. But even learning a little has changed how I experience the country. You start recognising words around you instead of everything looking like one giant puzzle.

How much Estonian do you speak or understand now?

If you are native speaker, just go at X0.5 speed with the simple conversations.

How has the PhD changed the way you think?

I'm much more comfortable with not having an immediate answer. Before the PhD, I probably saw uncertainty as something I needed to get rid of quickly. Now I'm more comfortable sitting with a problem and asking, “What do we actually know here?” before rushing toward a solution. Research has basically made me slower to answer and faster to question.

Where do you feel you belong intellectually—and where do you still feel like an outsider?

I see myself somewhere around the intersection of intelligent technology and the real world. Because I am a firm believer in the fact that, the only reason why people don't appreciate new technology is because they can't access the manuals or handbook or they are rather difficult to comprehend. It's like using a tv without a manual…

Do you see yourself in academia, industry, or somewhere in between?

I'm honestly more interested in the problem than the label.I could see myself in academia, industry, or somewhere in between, as long as I'm working on interesting problems and still have the freedom to build and experiment. I enjoy research, but I also really enjoy seeing an idea leave the paper and become something tangible.

Which skills from your PhD do you want to carry forward?

The ability to take a messy problem and keep pulling at it until I understand what is actually going on. And probably the ability to say, “I don't know” without feeling embarrassed about it. In research, that sentence is often the beginning of a much better question.

If your PhD were a chapter in a book, what would its title be?

“Okay, But Does It Work in the Real World with People?” That question probably captures most of my PhD. I started with an interest in intelligent systems, but over time I became much more interested in what happens when those systems have to deal with real environments, real people, imperfect gadgets and all the things that can help you control these situations.

Looking back so far—was the uncertainty worth it?

Yes. Definitely. Not because everything went according to plan. It really didn't. Tough times never last they say but tough people do.