news

Oct, 2026 Our paper ACS has been accepted to Journal of the American Statistical Association. This paper enables reliable and interactive deployment of foundation models. It allows partial data reuse, model fine-tuning, and updates to alignment evaluators while preserving valid selection, and can flexibly incorporate real-life considerations such as diversity.
👉 An earlier paper, Conformal Alignment, appeared at NeurIPS 2024.
Jun, 2026 Our paper Distributionally robust risk evaluation with an isotonic constraint has been accepted to Information and Inference: A Journal of the IMA. This paper offers an approach that utilizes side information (e.g. shape constraints) to balance the misspecification of sample reweighting and the over-pessimism of distributionally robust learning!
May, 2026 New preprint Adaptive discovery of effect modification in matched observational studies: a finite-sample valid procedure to identify covariate-interpretable subgroups with exact subgroup-level FDR control, robust to unmeasured confounding via sensitivity models and powered by multiple matched controls.
Sep, 2025 Multi-modal contrastive learning adapts to intrinsic dimensions has been accepted to NeurIPS 2025.
  • We present a theoretical analysis of CLIP, showing how temperature optimization enables adaptation to the intrinsic dimension of shared features in multi-modal data (poster).
  • A more recent work proposes IndiSeek which learns modality-specific features that are independent of shared features.
Sep, 2025 I’m honored to be awarded the IMS Lawrence D. Brown Ph.D. Award!
Apr, 2025 Our paper Conformal prediction: a data perspective has been accepted to ACM Computing Surveys.
Jun, 2024 I’m happy to be awarded the William Rainey Harper Dissertation Fellowship!
Dec, 2023 Presented Conformalized matrix completion at NeurIPS 2023 and ICSDS 2023 (poster).