I’m Junho, a CS and Data Science student at Northwestern with a focus on AI.

I grew up in Anyang, Gyeonggi. I tried a lot of things growing up: soccer, speed skating, taekwondo, swimming, violin, abacus. Most of it stuck with me. I liked math and art early on, and my mom taught English, so I started learning that pretty early too. Beyond that, it was a pretty normal childhood.

When I was in 4th grade, my mom went to North Carolina to study education, and my sister and I followed. It was my first time in the US. English was hard at first, but I picked it up over time and started making friends outside of the Korean community.

It was a big shift from Korea. North Carolina was multiracial in a way that homogeneous Korean society wasn't, and I got to experience different cultures and food through those friendships. There was also so much nature around. After school, we'd go into the woods, build tree houses, and just play outside. I think this was when my personality started to open up. I was pretty quiet and timid before. It made enough of an impression that I always wanted to come back.

We moved back to Korea and settled near Daechi-dong, the most well-known academy district in the country. I still remember my first day of 6th grade: the girl next to me was solving 11th grade math. I had never set foot in an academy before.

From then on it was school and cram school on repeat. I didn't have a clear reason for studying that hard at the time, beyond keeping up with the people around me. The one goal that did stick was getting into KMLA. It had a very different structure from typical Korean high schools, with more room for things outside academics, and I wanted out of the cram school environment to actually explore what I was interested in. Going to college in the US was already in the back of my mind, and KMLA felt like the most direct path there.

Getting into KMLA after three years of working toward it was a huge moment. I genuinely thought the hard part was over. It wasn't.

KMLA was a boarding school full of talented, driven students, and the environment pushed me in ways I hadn't experienced before. Clubs were a huge part of life there: I did musicals, band, entrepreneurship, math, overseas volunteering. The variety of things you could do was something most Korean high schools just don't offer. I made some of my closest friends during this time.

It was also where I learned to let go of always needing to be the best. I've always been a competitive person, and being surrounded by so many talented people forced me to accept that. Finding my own strengths and learning to collaborate instead was probably the most important thing I took away from those three years.

I graduated in February since KMLA follows the Korean calendar, which left me with six months of free time before college. I traveled with friends, played a band show, tutored, met new people. It was the first time in my life without any structure telling me what to do next, and that took some getting used to.

But having that much time to think turned out to be valuable. I started asking myself what I actually wanted to do, not just what was next on the checklist. I liked practical, analytical work, and I was good with numbers. Data science felt like the right fit: analytical work I could apply across a lot of different problems.

I enlisted right after high school, which is unusual in Korea. Most people go during college or later. My reasoning was simple: I didn't want a two-year break interrupting my studies, and I didn't want it weighing on me while in school.

Going in, I was nervous about being in such a different environment from what I was used to. But I learned a lot. The hierarchy taught me about leadership and responsibility in a way that school never did. I also got to live alongside people from backgrounds I'd never been exposed to growing up, which changed how I see things. In my free time, I competed in data analysis competitions and prepared for college.

Daytrip (now Exqz) was an app I used a lot during military leave to find places to visit. I had no professional experience, but I wanted to try working before starting college, so I cold-emailed the CEO. It worked out, and I joined as an intern doing global sales and marketing. It was my first look at how a startup operates. I spent a lot of time figuring out how to do things faster and more efficiently: finding better tools, organizing databases, streamlining workflows. That mindset carried forward.

I started studying CS and data science here. CS appealed to me for how practical it was, and as AI became more present across different fields, I got more interested in its applications.

I got involved on campus too:

• Sang in Mosaic Whispers (a cappella)
• TA'd CS 131 (Intro to Computer Science)
• Competed in 3 hackathons
• McKelvey Fellowship fellow

My first real experience working in the field I'd been studying. I interned at NAVER Cloud's Digital Healthcare Lab as an AI research intern, working on large language models and transformer-based foundation models in biomedical informatics. It was also my first time working in anything biology-related. I don't come from a bio background, but healthcare felt like one of the areas where AI could have the most meaningful impact, so I wanted to see what that looked like up close.

I had two main focuses:

• Reviewing recent research on LLMs in healthcare and biomedical applications
• Collaborating with a partnering pharmaceutical company to reproduce relevant literature and define AI modeling problems for their research challenges

Biomedical AI touches a wide range of modalities, so I got exposure to many different types of models and data. This was also my first time doing research, and the biggest lesson was how much the outcome depends on asking the right question. The technical work matters, but framing the problem well is what makes everything else productive.

I liked WashU, but wanted a bigger city and a stronger engineering program, so I transferred to Northwestern. Here, I've been going deeper into AI/ML. Alongside core CS and data science courses, I've taken courses in machine learning and topics like LLMs.

What I've been up to:

• Fellow at the Northwestern AI Club, working on the CASCO project
• Logistics team for WildHacks
• Research under Professor Shi, collaborating with Lawrence Berkeley National Laboratory on the GenomeOcean project

I'm still figuring out exactly where I want to take this, which is why I'm trying to get as much exposure as possible. What I do know is that I want to work on AI that directly impacts people's lives, whether that's through research or building real applications.