Meet Kyler Rosen, MS in Applied Data Science Alumnus

After graduating from the University of Chicago’s MS in Applied Data Science program, Kyler Rosen found himself at the intersection of AI, healthcare, and entrepreneurship as founding engineer at Slideflow Labs, a healthcare AI startup focused on rapid cancer diagnostics.

In June, Slideflow Labs was awarded the $575,000 First-Place Prize at the 30th Annual Edward L. Kaplan New Venture Challenge (NVC), one of the nation’s top startup accelerators. As a member of the winning team, Rosen has helped the company translate cutting-edge AI research into tools that could improve cancer diagnosis and patient care.

In this Q&A, Rosen reflects on building in the startup world, the future of healthcare AI, and how the MS in Applied Data Science program influenced his career journey.

Q&A with Kyler Rosen

Before joining the MS in Applied Data Science program, what was your background? 

In undergrad, I studied computer science at Case Western and concentrated mainly in AI. A lot of traditional computer science education is very theoretical, it’s focused on the mathematical frameworks behind neural networks and building models from scratch.

When I interned at Microsoft on the GitHub team, one of the things I learned was that technical skills alone aren’t enough. If you want to build and deploy something, you need to be able to justify why it should exist and how it creates value. That experience was part of what pushed me toward the MS in Applied Data Science program. I wanted to focus more on the business and applied side of data science.

What drew you to the MS in Applied Data Science program?

First of all, Chicago itself was a huge appeal. But beyond that, UChicago is known for its rigor, and I thought it was exciting to be in an environment where people are working on very theoretical and difficult problems while also being next door to the business school, so there’s this strong combination of technical depth and applied thinking. 

When you started the program, did you imagine yourself working at the intersection of AI, healthcare, and startups?

Not at all, actually. Originally, I thought I would go back into big tech. I had worked on foundational language modeling before, and the plan was to complete the master’s degree and return in a more research-focused role.

What changed everything was meeting the two co-founders of Slideflow Labs while I was in the program. At the time, they had developed a state-of-the-art model and were looking for someone to help evaluate deployment options. I started working on deployment strategies with them, and eventually the company hit an inflection point and really took off. I never looked back after that.

For readers unfamiliar with Slideflow Labs, what kinds of problems is the company trying to solve?

We work in rapid cancer testing. When a patient has cancer, they have to get the tumor removed. It’s then sent to pathology, where the tumor is cut up and small tissue samples are placed onto very thin glass slides and analyzed by pathologists under microscopes to determine how the cancer is growing and what treatments may be effective.

The challenge is that these images are incredibly detailed. The files we work with are roughly four gigabytes in size, about the equivalent of two HD movies back-to-back. Traditionally, hospitals have to send samples to external labs for additional testing, which is expensive, labor intensive, and can take weeks.

What we’re building is the ability to analyze these slides directly on site using AI and computer vision. We provide a scanner, which scans super high resolution images. Hospitals then can scan the slide, and our models can help predict things like recurrence risk and even the likelihood that certain treatments will be effective on the cancer. Some of these capabilities simply haven’t been possible until now.

As the founding engineer at an early-stage startup, what does your day-to-day work look like?

It’s a lot of wearing many different hats! 

At 9 AM, I’m on a call with the hospital understanding what they need, and then at 10 AM I’m trying to fix a bug, and by 11 AM, I’m working with a different hospital, trying to figure out how to get them integrated in. 

It’s definitely a lot of fun. You get to decide how things are built, then basically just guide the direction of the product, deciding what features are going to get built and what features aren’t. So, it’s a very nice intersection of many things.

Slideflow Labs has also participated in accelerator programs through organizations such as the Polsky Center. What has that experience been like?

We’ve participated in multiple accelerators, including Techstars and Alchemist, and we also went through the Polsky Transform program as a UChicago spinout.

It was exciting to see how much innovation is coming out of UChicago because there are so many talented scientists developing ideas across fields ranging from agriculture and pharmaceuticals to satellite connectivity, and all of them are looking for engineers to help bring those ideas to life.

How have those startup and accelerator experiences influenced your own professional development?

I’ve had opportunities that most new graduates wouldn’t normally expect this early in their careers.

When I first joined, I was the sole engineer. Now we’re growing the team, and I’m managing engineers and helping guide major technical decisions. Being exposed so early to founders, executives, and decision-makers has really shaped the way I think about leadership and strategy.

It’s helped me develop a much stronger understanding of how companies operate and how important decision-making becomes as organizations grow.

Were there any courses or experiences in the program that had a major impact on your career path?

One class that really shaped my perspective early on was the Leadership and Consulting course. At first, I didn’t fully understand why it was positioned so early in the curriculum, but looking back, I think it set the tone for the entire program.

The course focused less on whether a model technically works and more on how to think about deploying technology from a business perspective. In the startup world, time and resources are limited, so you constantly have to ask yourself how to use your time most effectively. That class changed the way I think about technical problems.

Another experience that stood out was a project in Professor Batu’s class. A lot of academic projects focus on building from scratch, but one thing I appreciated about Batu’s course was the emphasis on how things actually work in industry. Nobody starts from zero, you start with a base model and build off of that. Practicing how to search for and adapt existing systems before building something from scratch was important. 

What excites you most about AI in healthcare right now?

Accessibility.

For a long time, one of the barriers in healthcare technology was that most doctors don’t know how to program, and most programmers don’t know much about medicine. That made it difficult for the two fields to work together.

What’s exciting about the era of large language models is that we’re starting to build tools that allow medical professionals to interact with AI systems more naturally, without needing deep programming knowledge. 

What advice would you give to current MS in Applied Data Science students interested in startups or healthcare AI?

For startups, you have to be comfortable wearing many hats. One day you might be working on engineering deployment, the next on data science, and the following day helping with marketing.

If that kind of fast-moving environment excites you, there are so many opportunities out there. A lot of startups need talented people, but because they’re small, you often have to proactively seek them out.

Healthcare AI is almost the opposite. It’s an extremely careful and validation-focused field. Every decision has to be justified, tested, and well thought out because the stakes are so high.

But, if you enjoy solving difficult problems, then I don’t think there’s any other field where your work can directly impact people’s lives as much as you can in healthcare.