Welcome Dr. Ashery Mbilinyi! (an Interview with Dr. Yun Lu)

I had the pleasure of chatting with Dr. Ashery Mbilinyi, one of the newest faculty members joining UVic’s Computer Science department. In this interview, we discuss his fascinating academic journey that took him from Tanzania, to India, Japan, Switzerland, and finally to Canada. You can also read about his experience in this blog article.

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Dr. Ashery Mbilinyi in his office

 

 

Can you give me a quick introduction of your academic journey?  

 

Ashery: I originally come from Tanzania, which is a country in East Africa. This is where I was educated until my undergrad at Dar es Salaam Institute of Technology. Then I went to India for three months and did a specialized training on web apps development. And then I went to Japan Advanced Institute of Science and Technology to do my master’s in information science. After that I joined University of Basel in Switzerland to do my PhD and then to University of British Columbia for my postdoc. Eventually I'm here as assistant professor at University of Victoria. 

Yun: Wow, so you’ve literally been going all around the world! 

Ashery: I feel incredibly fortunate to have had these opportunities to visit so many places. It wasn't easy—I had to work hard to earn each one. The best part has been experiencing different education systems and cultures; you gain so much from seeing the world from various perspectives. 

 

How did you get into computer science? 

 

Ashery: So, as you mentioned, you have read my blog article--- I grew up in a village without access to computers and didn’t encounter one until I went to university. Back in 1999, however, my dad and I visited his friend’s house to watch a TV program on Y2K, as we didn’t have a TV at home. 

At that time, many believed that the year 2000 would bring the end of the world because computers supposedly couldn’t process it. Through the program, I learned that Y2K wasn’t exactly about an apocalypse but rather a potential problem with computers being unable to distinguish between the years 1900 and 2000. This glitch could affect vital infrastructures, from air traffic control to banking. It made me think, “I want to understand computers more. They seem to touch everything.” That moment sparked my desire to study them in the future. 

 

In your blog you also mentioned something interesting about your time in Japan. Which is that you thought that because everybody is international, maybe everybody will speak English with you. It turned out everybody was speaking Japanese. How did you manage that? Did you end up learning Japanese? 

 

Ashery: When I went to Japan for my master’s, I quickly realized that most people don’t speak English. One of the great things about living there was how locals were always willing to help foreigners like me, even with the language barrier.  

I also made an effort to learn the language, and I still speak a bit of Japanese today. Plus, I joined some activities like yakyu (baseball) and sometimes played on an amateur team at the university. It was a fun way to get involved and experience life in Japan. 

 

Favourite class/activity in university? 

 

Ashery: I've always enjoyed data structures and algorithms because they teach you to think like a computer scientist. With limited resources in the world, in a way, everything feels computational. 

For example, I remember in high school, we’d create acronyms to remember things. That method of compressing information into something we could recall when needed is a kind of computational thinking. It saves both time and memory space—just like efficient algorithms do. 

Yun: I can see why that's your favorite class! It’s an interesting philosophical way of looking at computation. Because you have finite time, you do stuff that you really enjoy. And you have finite space, so you don't fill your brain with stuff you don’t need. 

Ashery: Exactly, I try to view the world through this lens—thinking about how to optimize resources within the constraints of space, time, memory, and efficiency. It's like a natural extension of computational thinking to real life. 

 

During university, did you ever feel intimidated, or didn't know how to approach professors in your class? Or are you more of an outgoing person who can pretty easily approach people? 

 

Ashery: I think I am. Back in my undergrad, I was known by almost every professor because I was always curious, constantly asking questions and staying proactive—qualities that many professors seemed to appreciate. I was also selected as a Google Student Ambassador, which meant building a team and organizing events on campus to showcase Google technologies and platforms. 

I also remember the day the national radio in Tanzania wanted to interview a student about high-performance computing after a project launched at our university. They approached our department head, and he chose me to give the interview. 

Yun: Right, because you put yourself out there. 

Ashery: Exactly. 

Yun: When you came to Switzerland, how different was the experience compared to Japan? 

Ashery: The culture was quite different. For example, in Japan and Tanzania, things are more formal—you have to address your professor by their title and last name. But in Switzerland and here, you can call professors by their first names, which took me a while to get used to. 

 

Can you tell me a bit about your research area? 

 

Ashery: My research focuses on applying AI in healthcare, primarily to support clinicians in their workflow. To achieve this, I draw on techniques from computer vision, machine learning, and information retrieval. I develop AI tools and methods designed to assist clinicians with diagnostic decisions, aiming to reduce diagnostic errors, which, as we know, have serious consequences, including fatalities. 

While my main focus is on medical imaging, I'm also working to integrate additional patient information—symptoms, vital signs, physiological signals, and similar data—to provide a more comprehensive understanding of each patient for more accurate decision-making. 

Yun: Are there specific things about medical data that's different from applying AI to other kinds of data? 

Ashery: Of course, techniques that work for natural images don’t always apply directly to medical images. Here’s a good example: if you want to classify an image of a dog, it doesn’t matter if the dog is big or small, or if it’s a pit bull or a golden retriever—a dog is simply a dog, and the model doesn’t need to worry about scale.  

Yun: Right, for natural images, if it looks like a dog, it's probably a dog. 

Ashery: Exactly, but things can be different with medical images. With medical images, the size of something, like a tumor, is crucial information for the model to capture and interpret accurately. In an image retrieval task, two images might look visually similar but aren’t necessarily clinically similar. Take chest X-rays, for instance: similar features could indicate pneumonia in one patient, but without clinical signs like fever or elevated white blood cell count, those same features might suggest conditions like pulmonary edema, atelectasis, or even lung cancer in another patient. 

Yun: And not only do you need to know to add those data, but which data to add, like the white blood cell count, right?  

Ashery: Exactly. Timing is crucial too. When you go to the hospital, they don’t just say, “Let’s gather all your information at once.” There are time constraints, and they often conduct additional tests later to confirm or rule out initial hypotheses. So, in medical settings, you need to consider not only what information to gather but also when to gather it. 

 

What’s your advice for keeping motivation in your work? 

 

Ashery: Set up a routine—something that brings discipline to your day. Try organizing the things you want to accomplish daily, building structure around your goals, and creating consistency. 

Yun: Like having a list. 

Ashery: Absolutely. A routine becomes a habit, and soon enough, you feel something’s missing if you don’t follow it. I remember finishing my PhD and heading straight into my postdoc at UBC. Initially, I was staying in an Airbnb in New Westminster while searching for the apartment and waiting to officially start. During that time, I realized just how much I missed working—it felt strange not having that structure. 

You know, during my undergrad, I used to wait and study all night before exams. Eventually, though, I realized it wasn’t sustainable, so I stopped doing it. 

Yun: Definitely. Sometimes in academia I feel like a lot of things push you to, basically, work in bursts, right? A few weeks before a deadline, you start pulling all-nighters and get tired, eat poorly, and don't sleep. So, it’s great to put yourself into a routine. 

Ashery: Exactly, and by doing that, you’re essentially freeing your mind from the pressure. Consistency creates a kind of natural motivation—it becomes a byproduct of having a steady routine. 

 

Closing Question: “If you could go back in time to give yourself a piece of advice, what would it be?” 

 

Ashery: In undergrad, I was always drawn to learning the latest, shiny new things. But I eventually realized that focusing on the fundamentals is crucial in the long run. Often, things may look different on the surface, but they’re built on the same core principles. To truly understand something deeply—enough to make a meaningful contribution—you have to go back to first principles. It definitely pays off over time!