Daolang Huang

Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization.

Education:

• Bachelor's degree in Engineering and Technology, Jinan University (2020)

Research Interests:

• Bayesian methods and amortized inference
• Robust statistics under model misspecification
• Continuous control and neural process architectures
• Optimization algorithms with decision-theoretic foundations

Recent Research Trends: His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts.

Labs/Teams: Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.