Speaker Abstracts

July 31 – August 1, 2026  •  Harvard University

Mengdi Wang

Mengdi Wang

Co-Director of Princeton AI for Accelerated Invention, and Professor of the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning, Princeton University

LabOS: The AI-XR Co-Scientist That Sees and Works With Humans

Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and Extended-Reality (XR)-enabled human-AI collaboration. By connecting multi-model AI agents, smart glasses, and robots, LabOS allows AI to see what scientists see, understand experimental context, and assist in real-time execution. Across applications — from cancer immunotherapy target discovery to stem-cell engineering and material science — LabOS shows that AI can move beyond computational design to participation, turning the laboratory into an intelligent, collaborative environment where human and machine discovery evolve together.

Martin Wainwright

Martin Wainwright

Ford Professor of EECS and Mathematics, Laboratory for Information and Decision Systems, Statistics and Data Science Center, Institute for Data, Systems and Society, Massachusetts Institute of Technology

Synthetic Data for Black-Box Validation

Obtaining inferential guarantees on the prediction error of modern black-box methods is essential in practice. This task is challenging because such methods are opaque, allowing access only to predicted values; refitting is computationally expensive and can be performed only a limited number of times; and datasets may be heterogeneous, structured, or non-i.i.d. We propose a new procedure for black-box validation under these constraints. The method uses Rademacher residual symmetrization, in the spirit of the wild bootstrap, to construct a synthetic dataset. By refitting once on the synthetic dataset, we obtain non-asymptotic upper bounds on the prediction error without requiring hold-out samples or cross-validation. We illustrate the procedure in plug-and-play inverse imaging and photometric redshift prediction in astronomy.