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portfolio
Portfolio item number 2
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publications
Modified Kernel Density Estimators For Gridded Estimation of Precipitation Climatologies
Applied Mathematics Graduate Theses & Dissertations., 2018
Master’s Thesis outlining the development of a robust and accurate statistical model for estimating precipitation distributions at arbitrary locations. Advisor: Will Kleiber
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Function Space Distributions over Kernels
Neurips, 2019
We propose a model for determining distributions over covariance functions for use in conjunction with Gaussian processes.
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Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Pre-print, 2020
We show that many of the properties of generalization become understandable when viewed through the lens of effective dimensionality, which measures the dimensionality of the parameter space determined by the data. We relate effective dimensionality to posterior contraction in Bayesian deep learning, model selection, width-depth tradeoffs, double descent, and functional diversity in loss surfaces, leading to a richer understanding of the interplay between parameters and functions in deep models.
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Learning Invariances in Neural Networks
Neurips, 2020
We introduce a method to learn levels of invariance to augmentation in neural networks using only training data.
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Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
ICML, 2021
We introduce a way to discover volumes of low loss in the parameter space of neural networks, as well as a method to form ensembles of models taken from these low loss volumes.
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Bayesian Model Selection, the Marginal Likelihood, and Generalization
ICML, 2022
A careful look at the role of the marginal likelihood and its role in generalization and model selection in modern machine learning.
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Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
ICML, 2022
We adapt stochastic volatility models for use in Gaussian process forecasting with finance and climatology applications.
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talks
Function Space Distributions over Kernels
Published:
Oral presentation of “Function Space Distributions over Kernels” (Best Paper winner) for the time series workshop at ICML 2019. Slides here.
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembles
Published:
Oral presentation of “Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembles” at ICML 2021.
Learning Invariances in Neural Networks
Published:
Oral presentation of “Learning Invariances in Neural Networks” at Neurips 2020.
teaching
Introduction to Computing
Undergraduate Course, University of Colorado, Department of Chemical & Biological Engineering, 2016
TA for an introductory computing course covering the use of Microsoft Excel and engineering-focused programming in VBA and MATLAB.
Introduction to Decision Theory
Undergraduate Course, University of Colorado, Department of Applied Mathematics, 2016
Course Assistant for an introductory course on decision theory. Topics included basic probability and statistics as well as ethics and game theory.
Introduction to Machine Learning
Undergraduate Course on Machine Learning, New York University, Department of Computer Science, 2021
TA for a course on machine learning, covering a broad range of topics including Gaussian processes, deep learning, nearest neighbors, k-means and more. Responsible for conducting lessons and labs, as well as writing and grading homework assignments.