Q. Please give us a brief introduction of yourself.

I am Professor Wonjun Hwang, and I joined the School of Electrical Engineering at Korea University in March 2025. I completed my bachelor’s and master’s degrees at Korea University and earned my Ph.D. in Electrical Engineering from KAIST (Korea Advanced Institute of Science and Technology). My professional journey began at Samsung Advanced Institute of Technology (SAIT), where I worked as a senior researcher focusing on the industrial application of deep learning-based signal processing technologies, including facial recognition, gesture recognition, and medical image processing.
Afterwards, I served as an assistant professor in the Department of Software at Ajou University, solidifying my academic foundation. Currently, I lead the Signal Learning Lab at Korea University, where I concentrate on developing efficient signal learning methodologies that integrate deep learning and signal processing. With years of experience across diverse research environments and interdisciplinary projects, I look forward to creating synergy with the exceptional faculty and students at Korea University.
 

Q. What research will you be conducting at Korea University?

My lab, the Signal Learning Lab (SLL), is fundamentally focused on exploring the concept of “learning.” We aim to develop models capable of continuous adaptation in changing environments through research in areas such as continual learning, source-free domain adaptation, test-time adaptation, and knowledge distillation.
Recently, I have taken a deep interest in adaptive learning techniques based on foundation models, particularly those that build cooperative structures with vision-language models like CLIP. The core question we pursue is how to optimize the generalization ability of pretrained models for target domains while preserving existing knowledge.
Ultimately, our goal is to develop efficient and robust signal learning methodologies under the so-called "three-zero" constraints—zero data, zero training, and zero model assumptions.

Q. What inspired you to pursue this research field, and how do you view its future?

My experience at SAIT had a profound impact on both my research direction and educational philosophy. Working on AI technologies such as facial and gesture recognition and medical image analysis in real-world applications helped me realize that the value of deep learning lies not only in its theoretical advancement but also in its practical utility.
In the industrial context, performance indicators go far beyond accuracy—they include computational efficiency, real-time responsiveness, reliability, and user experience (UX). I have since made it a priority to reflect these practical concerns in my research.
Moreover, I came to understand the importance of bridging theory and practice to foster mutual growth between academia and industry. I encourage my students to think not only about how to build models, but also about the social value and practical impact of their technologies. This philosophy continues to shape my research, which aims to address meaningful real-world challenges.

Q. Do you have any messages for students?

I believe that learning technologies applicable to real life, as well as researching futuristic technologies not yet widely adopted, is among the most rewarding aspects of being an engineer.
As the word “research” suggests—“re-search”—research is a continuous journey of revisiting questions without definitive answers. Even an existing solution may lose relevance over time, or require a different approach in new environments.
Whereas high school education often focuses on finding the one correct answer, university education teaches you how to discover the most appropriate solution among many possibilities. You’ll also learn how to define problems yourself and take ownership of the entire problem-solving process.
Through this journey, you will gain the ability to identify and solve new problems that have yet to be defined by the world. What seems like a small curiosity now may one day evolve into a meaningful research topic—or even a breakthrough that opens new fields.
My research lab aspires to be a place where students can grow by pursuing such questions and embarking on their own path of discovery.