Willie Neiswanger (original) (raw)

Prospective students: I am recruiting PhD students (current application cycle) and postdocs who wish to do work at the intersection of machine learning, decision making, generative AI, and AI-for-science! If you are interested, please feel free to reach out to me via email at neiswang@usc.edu.

Research: I work at the intersection of machine learning, decision making, generative AI, and AI-for-science. I also develop methods for efficient optimization and experimental design in costly real-world settings, where resources are limited, and work on uncertainty quantification in machine learning. I apply these to problems in science and engineering, for example in the physical sciences, biology, and machine learning systems.

I have also worked on distributed algorithms for scalable machine learning, and I develop/maintain software libraries for multilevel optimization, uncertainty quantification, AutoML, and Bayesian optimization.

Education: I completed my PhD in Machine Learning at Carnegie Mellon University, where I was advised by**Eric Xing** and collaborated with**Jeff Schneider** and**Barnabas Poczos**. I then did a postdoc in computer science at Stanford University, working with Stefano Ermon.

Previously, I studied at Columbia University, where I worked with**Chris Wiggins** and Frank Wood.

News

• Feb 11, 2025 New paper onLiveBench, a challenging, contamination-free LLM eval, in ICLR 2025 (spotlight).
• Feb 11, 2025 New paper on decision making under uncertainty with LLMs (DeLLMa) in ICLR 2025 (spotlight).
• Jan 6, 2025 Released**METAGENE-1** a metagenomic foundation model designed for pandemic monitoring.
• Mar 25, 2024 New paper on uncertainty quantification for deep learning PDE surrogates in AAAI 2024.
• Feb 23, 2024 New paper on experimental design for determining safe tokamak rampdowns in Nuclear Fusion.
• Oct 20, 2023 New paper (+code) on algorithms and systems for scalable meta learning in**NeurIPS 2023**.
• Oct 20, 2023 New paper on offline model-based optimization through co-teaching in NeurIPS 2023.
• July 28, 2023 Co-organized the**_Differentiable Almost Everything Workshop_** at ICML 2023.
• Mar 23, 2023 Co-organized the**_Modern Adaptive Experimental Design and Active Learning_** Online Reading Group.
• Jan 20, 2023 New paper on automatic differentiation for multilevel optimization in**ICLR 2023 (notable-top-5%)**.
• Jan 20, 2023 New paper on policy identification for active reinforcement learning in**ICLR 2023 (notable-top-5%)**.
• Jan 20, 2023 New paper on a framework to combine weak supervision and generative modeling in ICLR 2023.
• Jan 1, 2023 New paper on offline imitation learning with suboptimal demonstrations in**AAAI 2023**.
• Dec 7, 2022 New paper on uncertainty quantification with pre-trained language models in**EMNLP 2022**.
• Dec 2, 2022 Invited talk at the Workshop on Gaussian Processes and Decision-making Systems at**NeurIPS 2022**.
• Oct 10, 2022 New paper (+code) on trajectory information planning for exploration in RL in**NeurIPS 2022**.
• Oct 10, 2022 New paper on decision-theoretic entropies for generalizing Bayesian optimization in**NeurIPS 2022**.
• July 22, 2022 Co-organized the**_Real World Experiment Design and Active Learning Workshop_** at ICML 2022.
• May 15, 2022 New paper (+website) on likelihood-free Bayesian optimization in ICML 2022 (long talk).
• May 15, 2022 New paper on a modular conformal calibration framework for UQ in ICML 2022.
• Jan 28, 2022 New paper (+blog post) on experimental design and reinforcement learning in ICLR 2022.
• Jan 1, 2022 New paper (+website) on large-scale object counting in satellite images, in AAAI 2022 (oral).
• Oct 15, 2021 New paper (+code) on quantile methods for calibrated uncertainty quantification in**NeurIPS 2021**.
• Oct 15, 2021 Two papers on explainable machine learning and personalized benchmarking in NeurIPS 2021.
• July 14, 2021 Our paper on Pollux was awarded the Jay Lepreau Best Paper Award at OSDI'21.
• June 10, 2021 New paper (+website) on Bayesian Algorithm Execution (BAX) and InfoBAX, in ICML 2021.
• June 1, 2021 I co-organized the**_Machine Learning for Data (Creation, Privacy, Bias) Workshop_** at ICML 2021.
• Apr 1, 2021 New paper (+AdaptDL) on Pollux, a deep learning cluster scheduler/tuner, in OSDI 2021.
• Mar 16, 2021 New paper (+code) on uncertainty quantification with martingales for GPs in**ALT 2021**.
• Mar 9, 2021 New paper on active classification for catalyst discovery in the Journal of Chemical Physics.
• Jan 12, 2021 New paper (+code) on a framework for interactive weak supervision in ICLR 2021.
• Dec 22, 2020 Released**Uncertainty Toolbox**, for predictive UQ, calibration, metrics, and visualization.
• Dec 2, 2020 New paper (+code) on BANANAS, a method for neural architecture search, in AAAI 2021.

Projects

• METAGENE-1 A 7B parameter metagenomic foundation model designed for pandemic monitoring.
• LiveBench A challenging, contamination-free LLM benchmark.
• LLM360 Fully open-source LLMs for transparency, trust, and collaborative research.
• Betty An automatic differentiation library for multilevel optimization and meta-learning.
• Bayesian Algorithm Execution (BAX) Extending Bayesian optimization to computable function properties defined by algorithms.
• Uncertainty Toolbox A toolbox for predictive uncertainty quantification, calibration, metrics, and visualization.
• Naszilla A python library for neural architecture search.
• AdaptDL A resource-adaptive cluster scheduler for deep learning training.
• CASL Project An open toolkit for composable, automatic, and scalable learning.
• ProBO A framework for using probabilistic programming in Bayesian optimization.
• Bayesian Optimization and DOE NASBOT for neural architecture search, MPS for design of experiments, and Dragonfly.
• Prior Swapping Efficient algorithms for incorporating prior information, post-inference.
• Embarrassingly Parallel VI Communication-free distributed variational inference in nonconjugate models.
• Embarrassingly Parallel MCMC Asymptotically exact, communication-free distributed posterior sampling.
• Fast Function-based Regression Fast distribution-to-real and function-to-function nonparametric regression.
• GPU for Time-varying PYPs Generalized Polya urn for time-varying Pitman-Yor processes.
• Parallel Frank-Wolfe Optimization Asynchronous parallel block-coordinate Frank-Wolfe optimization algorithm.
• LRO Models for Link Prediction Latent random offset model for interpretable citation prediction and exploration.
• DDP Object Tracking and Modeling Dependent Dirichlet process mixtures for unsupervised object detection and tracking.
• Cell Motility Analysis TIAM: the tool for integrative analysis of cell motility.

Publications

A full list of my publications can be found here.