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About

Qin Lu is a machine learning researcher whose work centers on Bayesian optimization, active learning, and uncertainty quantification — areas critical to making intelligent systems more efficient and reliable. Lu's most recognized contribution, "Surrogate Modeling for Bayesian Optimization Beyond a Single Gaussian Process" (2023), has garnered 53 citations and advances the field by moving beyond traditional single-model surrogates to richer ensemble-based frameworks, addressing key limitations in optimizing expensive black-box functions. This work has direct implications for high-stakes applications including drug discovery, hyperparameter tuning, and robotics. Complementing this, Lu has developed adaptive ensemble strategies for both Bayesian optimization and active learning, tackling the persistent challenge of acquiring informative labeled data under tight resource constraints across domains such as medical imaging and wireless networks. More recently, Lu has explored scalable Gaussian processes combined with conformal prediction to provide rigorous, coverage-guaranteed uncertainty estimates in safety-critical settings. Collectively, Lu's research portfolio reflects a coherent and impactful research vision: building principled, adaptive, and scalable machine learning methods that perform reliably when data and evaluations are costly, positioning them as a meaningful contributor to modern probabilistic machine learning.