Faculty Spotlight with Dae - Jin Lee

From predicting the next superstar at Athletic Club Bilbao to developing AI models that help prevent career-ending injuries, Assistant Professor Dae-Jin Lee brings sports analytics to life at IE University's School of Science and Technology. Since joining in January 2023, Dae – Jin has been showing students how the same data science techniques that optimize athlete performance can transform healthcare and solve real-world problems. Whether you're a football fan or simply curious about how numbers shape our world, Dae – Jin’s innovative approach to teaching data science might just change how you see the game – both on the field and in your future career.

Meet one of Sci - Tech’s top professors.

What brought you to IE?

My previous experience includes a postdoctoral fellowship at Australia's CSIRO and PhD in Mathematical Engineering at Universidad Carlos III de Madrid. After that I spent nearly a decade at the Basque Center for Applied Mathematics as a researcher and group leader in applied statistics and data science before I joined IE.

IE University attracted me because of its interdisciplinary approach, as well as its innovative research environment and emphasis on data science, technology, and AI. This aligned with my expertise but also it allowed me to contribute to research while teaching in what seemed to be a really dynamic and international setting.

From mortality data to wireless sensor networks, you’ve worked with diverse data types. Is there a particular application area where you believe statistical methods can make the most meaningful real-world impact?

Throughout my career, I've applied statistical methods across various domains including public health, environmental science, engineering, and sports science. While all these fields benefit from advanced statistical modeling and data science; healthcare and medical research offer some of the most meaningful societal impacts. Environmental monitoring and sustainability also demonstrate tremendous potential.

The true value of statistics lies in transforming complex data into actionable insights. Across disciplines—whether sports, medicine or environmental science—the objective remains consistent: to enable informed, data-driven decisions that generate tangible societal benefits.

How do you see statistical methods transforming sports analysis?

I am fascinated by sports data science and have focused on how statistical methods enhance performance assessment, prevent injuries and even develop talent. Statistical methods are transforming sports analysis, through sophisticated data-driven approaches like machine learning and predictive modeling that analyze real-time performance, optimize training loads, and predict injury risks.

"I’ve worked on several projects using AI-driven models to predict young athletes’ growth and reduce injuries by customizing training plans to their developmental stages. I’ve been lucky enough to collaborate with clubs like Athletic Club Bilbao to integrate AI, biomechanics, and sports medicine to provide a holistic approach to athlete well-being and development."

Only this month I participated in a collaborative session hosted by Athletic Club at their Lezama training facility, alongside clubs such as Arsenal, Benfica, Borussia Dortmund, and PSV, as well as academic and industry experts. Representing IE University, I contributed my expertise to discussions on applying AI and data science to enhance athlete performance, monitoring, and injury prevention. This kind of collaboration between academia and elite sport is key to sparking innovation in sports.

Have you ever had an “A-HA!” moment while teaching that helped you with your research?

I've had several a-ha moments while teaching that have shaped my work! There is one in particular that stands out. While discussing statistical inference with students, I reconsidered how expert insights from sports medicine could be integrated into AI models for predicting athlete maturation and injury risk. This became a key component of the SPHERES project, developing semi-parametric models that bridge traditional statistics with machine learning.

It was a powerful reminder that teaching isn’t just about imparting knowledge—it’s also a space for refining ideas through dialogue.

Looking at your career path, what advice would you give to IE students?

For IE students looking to break into data science or applied math, I’d say focus on problem-solving, hands-on experience, and finding your niche. Work across disciplines, sharpen your communication skills, stay adaptable, and build a strong network. Most importantly, stay curious—data science is all about creativity and making an impact with data.

What book do you wish your students would read before taking your class?

I’d recommend Nate Silver’s The Signal and the Noise—it shows how statistical thinking applies to real-world problems like elections and sports. It’s great for learning to spot good models, avoid misinterpreting data, and think critically about uncertainty. For a more technical read, An Introduction to Statistical Learning is a solid start for machine learning.

Dae-Jin Lee’s top tips for students

For students into statistical learning and data visualization, a mix of solid stats, coding, and communication skills will be key over the next five years.

1. Master the basics – Strong Stats and solid Python/R skills are must-haves.

2. Go beyond just running models – Understand why an algorithm works, not just how to use it. Explainable AI is a game-changer.

3. Tell a story with data – Tools like Plotly, PowerBI and Tableau are great, but the real skill is making insights clear and actionable.

4. Think ethically – AI bias, fairness, and data privacy are hot topics. Knowing how to build responsible AI is a huge plus.

5. Apply it to real-world problems – Domain knowledge (healthcare, sports, finance, etc.) makes you way more valuable.

6. Stay curious & keep learning – This field moves fast. Follow new research, build side projects, and stay engaged.

In short, blend strong technical skills with creativity and communication—that’s what will set you apart.

And finally, tell us one personal thing about yourself that none of your students know…

I really enjoy cycling (cyclotourism)—especially traveling with my Brompton folding bike and tent, exploring new places at my own pace and camping along the way. It’s a great way to disconnect and take in the scenery. I’m also a big fan of live music—there’s something special about experiencing music in the moment, whether it's a small local gig or a big festival. And, of course, I’m a passionate supporter of Athletic Club de Bilbao.

Another interest of mine is mechanical watches. I find the precision, craftsmanship, and intricate movements fascinating—there’s something captivating about how all the tiny gears and springs work together to measure time so perfectly. Maybe it’s the statistician in me, appreciating both the engineering and the way time itself can be broken down into such precise intervals.