- Machine Learning
- Deep Learning
- Information Theory
- +۵ مورد دیگر
Professor Kangwook Lee is a faculty member in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison. His research focuses on machine learning, deep learning, information theory, and coding. He holds a PhD from UC Berkeley and has received numerous awards including the NSF CAREER Award and Amazon Research Awards. His work spans theoretical advancements and practical applications in AI, federated learning, and data privacy. Education: PhD 2016, University of California-Berkeley MS 2012, University of California-Berkeley BS 2010, KAIST (Korea Advanced Institute of Science and Technology) Research Interests: Machine Learning: Focuses on improving model efficiency, generalization, and robustness in large language models (LLMs) and neural networks. Information Theory: Explores coding techniques and optimization strategies for distributed systems and federated learning. Federated Learning: Develops privacy-preserving methods and algorithms for decentralized data environments. Recent Research Trends: His publications emphasize LLM capabilities, parameter-efficient fine-tuning, and novel approaches to federated learning. Recent efforts include benchmarking LLM agents in video games and advancing transformer architectures for length generalization. Awards: 2024 NSF CAREER Award 2024 Amazon Research Award 2023 MLSys Best Paper Award Multiple fellowships from Brain Korea 21, GCORE, and KSEA Advising & Teaching: Teaches graduate courses in artificial neural networks, ethics of data engineering, and research methodologies. Supervises students in machine learning and federated learning projects.
