About

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About Me

• Interests
• Perspectives on Network & Internet Research
• Professional History
• Dipping in Other Fields
• Publications
• Community Involvement
• Personal Life
• How to Call My Name

Interests

Perspectives on Network & Internet Research

• I’m driven to uncover why core protocols—like BGP or DNS—were designed under past constraints, and how those legacy choices still shape today’s Internet resilience and scalability.
• Long-term, incremental protocol changes excite me: watching how anycast catchments shift or how congestion controls like BBR evolve reveals where theory meets operational reality. :contentReference[oaicite:1]1
• I view Graph Neural Networks as a powerful lens for modeling complex network topologies and predicting routing behaviors—pairing GNN insights with real-world BGP and traffic data can drive smarter, adaptive control planes. :contentReference[oaicite:2]2
• Machine-learning in networks should tackle tasks humans avoid—like rapid traffic-engineering adjustments at scale—while respecting foundational protocol constraints and ensuring backward compatibility. :contentReference[oaicite:3]3
• Integrating deep learning demands careful balance: projects like DOTE and TEAL show promise for short-term traffic adaptation, but true Internet-wide routing optimization requires novel models informed by both theory and long-haul measurements. :contentReference[oaicite:4]4

Integrating deep learning in computer networks should be carefully considered. In my early encounters with deep learning, I saw it as a master key–an almost magical tool that could unlock solutions to any computational challenge. However, as I gained practical experience, I came to understand that deep learning, while powerful, is far from an all-encompassing solution. The uses of leading papers are in the same direction with my mind. In my CSCI 656 project, I worked on a project titled “Quantifying the Robustness of ML- based Traffic Engineering Models,” focusing on two advanced ML approaches–DOTE [3] and TEAL [4]–that aim to enhance efficiency and respond quickly to changing traffic demands. Although DOTE and TEAL utilize advanced ML techniques, they are not designed to address foundational network challenges like routing optimization or large-scale network control. Rather, these models are suited for tasks that involve rapid adaptation to short-term changes and often prioritize time savings over accuracy. Through this project, I gained a clearer perspective: while envisioning machine learning as a future alternative to traditional routing, this approach requires substantial preparation and is not yet feasible or realistic to be applied in the whole Internet.

Professional History

• From Summer Camp to Freelance Hustle
  I stumbled into my first iOS coding camp, charmed my way into an internship, and by junior year I was freelancing apps—enough to feel like I’d hit the jackpot at age 20 (my ramen game officially leveled up).

• The AI Reality Check
  Just as I was perfecting the “Uber for tacos” prototype, AI models took the world by storm. Suddenly, one-off app gigs felt as outdated as flip phones. Cue existential crisis: maybe “researcher” was the next frontier.

• Humble Beginnings, Happier Outcomes
  Trading freelance paychecks for grad-school stipends meant embracing student-budget gourmet (instant noodles with a garnish, anyone?). In return, I shed social pressure and paycheck anxiety—and gained the freedom to chase projects that truly excite me.

• Building Networks and Debugging Dreams
  These days I write code that pushes network boundaries by day and dive into GNN experiments by night. I may not be a crypto millionaire, but I’m rich in curiosity—and far less stressed about deadlines.

• Honestly, I got really lucky. iOS camp -> internship -> freelancing... By junior year, I was making a big money(for 20 year old college student).

• As AI models developed, I realized I couldn't do freelancing AppDev Jobs forever. I started to get interested in researcher as a job around then.

• Now, I am happy with my career choice. It took some time to get used to humble lifestyle, but now I actually feel more free of money and social pressure.

Dipping in Other Fields

I have a soft spot for enthusiastic research labs. I often ended up doing summer research interns (for free) at various labs in undergrad, just because I liked the professors passion in the field.

• Computer Vision

• Ad-Hoc Networks

• Recommendation Systems

• NLP

• Computer Vision: from image segmentation to object tracking, I’ve dabbled in teaching machines to “see.”

• Ad-Hoc & Delay-Tolerant Networks: exploring how devices coordinate in the wild when infrastructure isn’t there.

• Recommendation Systems: tweaking algorithms to predict what users really want, even when they don’t know themselves.

• Natural Language Processing: parsing text, extracting meaning, and turning words into data we can analyze.

Publications

Community Involvement

Personal Life

I try not to take myself too seriously. Rejections used to trigger full-blown panic attacks—now I meditate, crochet, make music, or hammock in the park with a book. My motto, keepgo.in, nods to the Korean meme “nossibal keep going,” and I’m proud to be Korean: I draw inspiration from Dosan An ChangHo and 강남 전길남. These days I’m learning Malayalam, hitting the gym, and dreaming of RV life touring every U.S. national park.

My dad’s a filesystem researcher—I still remember peeking at his NTFS hex dumps when I was ten! We don’t talk code much (we’d rather share a drink), but I’m forever grateful for how he showed me the beauty in low-level detail.

https://www.google.com/url?sa=i&url=https%3A%2F%2Fm.bboom.naver.com%2Fboard%2Fget%3FboardNo%3D9%26postNo%3D4158479%26entrance%3D&psig=AOvVaw0bVxZHvBnKWYJlUxaswkyw&ust=1749934546150000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCIj--Kmk740DFQAAAAAdAAAAABBx

How to Call My Name