[Machine Learning Applications]
computer vision, robotics, signal processing, networks, communication, security,
natural language processing, scientific AI, climate, healthcare, computational biology, etc.
[Learning, Inference, and Theory for AI]
supervised, unsupervised and reinforcement learning, bandits, online, active and continual learning,
Bayesian statistics, graphical models, variational inference, Monte Carlo methods, causal inference,
statistical learning theory, optimization theory, reinforcement learning theory, deep learning theory, etc.
[Deep Learning and Foundation Models]
deep neural networks, representation learning, generative models, large language models,
vision-language models, multimodal learning, transformers, state space models, LLM agents, etc.
[Trustworthy and Efficient AI]
robustness, uncertainty estimation, privacy, security, fairness, interpretability, AI safety,
model compression, pruning, quantization, efficient training and inference, evaluation and benchmarking, etc.
[AI Systems, Society, and Emerging Topics]
AI systems and infrastructure, data-centric AI, human-AI interaction, embodied AI, autonomous systems,
AI governance, responsible AI, AI policy, AI for education, AI deployment, etc.