Youngki Lee

I am an associate professor in the Department of Computer Science and Engineering of Seoul National University. I received my PhD from the computer science department of KAIST in 2013 and worked at Singapore Management University as an assistant professor until 2018. I am leading Human-Centered Computer Systems Lab. My group works at the intersection of mobile and pervasive computing, applied machine learning, and human-computer interaction. We are broadly interested in building experimental and innovative software systems, which involve multi-dimensional considerations across systems, applications, and users. Our current research interests are summarized below:

Edge AI Systems

• On-device and edge deep learning systems
• Optimizations for resource-constrained devices
• Power efficiency and concurrency support
• Privacy-preserving deep learning
  

Human-Centered Sensing

• Behavior sensing and analytics
• Emotion and health sensing
• Healthcare, wellness, and accessibility applications
• Social science computing

Mixed Reality Systems

• Augmented and virtual reality systems
• Multi-modal interactions
• Mixed reality applications and usability
• 3D media streaming

Education

• Ph.D., Computer Science, KAIST, Korea, Feb. 2013
• B.S., Computer Science, KAIST, Korea, Feb. 2004

Research Topics

• Mobile and Pervasive Computing
• Mobile Sensing and Context Monitoring
• Large-scale Urban Computing
• OS, Networking, and Distributed Systems

Teaching Topics

• Software Engineering
• Mobile Systems and Applications
• Interactive Design & Prototyping

Research Interests

• Mobile context sensing (location, activity, social contexts, etc.)
• Human-centric context sensing (attention, distractability, emotion, stress, depression, etc.)
• Collaborative sensing and social context inference
• New life-immersive applications (well-being, childcare, education, ads, ...)
• Energy efficiency and concurrency support
• Programming model for continuous sensing applications
• IoT and cyber-physical systems for smart spaces
• Innovative user experiences using wearable devices
• Large-scale mobile testbed
• Behavioural experimentation platform
• Urban context analytics (queue, groups, crowds, etc.)