Hello! I am a senior research scientist at Google DeepMind. My research is motivated by intelligent decision making, and currently focuses on understanding and improving the alignment between human judgements and Bayesian beliefs in AI systems.

My prior experiences are in machine learning and robotics, including Bayesian optimization, learning for task and motion planning, active learning, and Gaussian processes. I completed my Ph.D. in Computer Science at MIT, advised by Leslie Kaelbling and Tomás Lozano-Pérez. I received my S.M. in Electrical Engineering and Computer Science from MIT, advised by Stefanie Jegelka and Leslie Kaelbling. Click here for my CV.

Other roles

• Host and organizer of the Google Bayesian Optimization Speaker Series on YouTube.

• Co-organizer of the Seminar Series on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, its NeurIPS 2022 Workshop and NeurIPS 2024 Workshop.

• Co-organizer of the NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World.

• Area Chair of AISTATS 2023-2025, ICLR 2024-2025.

Bulletin

• Check out our tech report Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty.
• Our paper REDUCR: Robust data-downsampling using class priority reweighting was accepted at NeurIPS 2024!
• Our paper Pre-trained Gaussian Processes for Bayesian Optimization was accepted at Journal of Machine Learning Research (JMLR)!
• Our paper Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces was accepted at Transactions on Machine Learning Research (TMLR)!
• Our paper Gaussian Process Probes (GPP) for Uncertainty-Aware Probing was accepted at NeurIPS 2023.
• Our paper Grammar Prompting for Domain-Specific Language Generation with Large Language Models was accepted at NeurIPS 2023.

Contact

wangzi ‘at’ google ‘dot’ com

For mentorship and career opportunities, please visit Google CSRMP, GDM Scholarships and GDM Careers.

ziw ‘at’ csail ‘dot’ mit ‘dot’ edu