Artificial Intelligence Research Groups

Deep Learning Lab

Kenji KAWAGUCHI

Our lab aims to establish the positive feedback loop between theory and practice, to accelerate the development of the practical deep learning methods and to contribute to the understanding of intelligence.

• Machine Learning

Information Theory and Statistical Learning Group

Jonathan SCARLETT

Our group performs research at the intersection of information theory, machine learning, and high-dimensional statistics, with ongoing areas of interest including information-theoretic limits of learning, adaptive decision-making under uncertainty, scalable algorithms for large-scale inference and learning, and robustness considerations in machine learning.

• Learning Theory, Machine Learning

AIoT Group

WANG Jingxian

Adaptive Computing Laboratory

David HSU

Our long-term goal is to understand the fundamental computational questions that enable fluid human-robot interaction, collaboration, and ultimately co-existence. Our current research focuses on robust robot decision-making under uncertainty by integrating planning and machine learning.

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• Decision Making & Planning, Machine Learning, Robotics

Computer Vision and Machine Learning Group

Angela YAO

https://cvml.comp.nus.edu.sg/

STeAdS Virtual Group

Ganesh NEELAKANTA IYER

Software Engineering and Technological Advancements for Society. A virtual group that uses Software engineering practices and Technological advancements (Cloud computing, Artificial Intelligence (EdgeAI, ML)) for the benefit of various aspects of society (healthcare, education, art & culture). Looking for students to collaborate on different projects. Look at ganeshniyer.github.io for details.

• Decision Making & Planning, Machine Learning, Multi-Agent Systems & Algorithmic Game Theory

Data Privacy and Trustworthy Machine Learning Lab

Reza SHOKRI

Multi-Agent Planning, Learning, and Coordination Group (MapleCG)

LOW Kian Hsiang

Our group is multi-disciplinary: CS, math, stats, physics, eng, data science. We believe in theory & practice. Our research cover probabilistic ML (Bayesian deep learning, Gaussian process), learning with less data (autoML, Bayesian optimization, meta-learning, active learning), multi-party ML (federated/collaborative ML, privacy-preserving ML), reinforcement learning & multi-agent/robot systems.

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• Decision Making & Planning, Machine Learning, Multi-Agent Systems & Algorithmic Game Theory, Robotics, Trustworthy AI