Research

Causal ML

Causality provides a formal language to analyze interventions and distribution shifts.

Robust ML

We focus on out-of-distribution (OOD) robustness and fairness, viewed as robustness to a sensitive attribute.

Explainable AI

This explores the interplay of ML explanations and robustness.

Robust Collaborative Learning

We aim for collaborative learning that is robust to the imperfect conditions of distributed edge device networks.

Selected Publications

Spurious correlations can cause model performance to degrade in new environments. Prior causality-inspired work aim to learn invariant …
Preprint Project Project

Answering counterfactual queries has important applications such as explainability, robustness, and fairness but is challenging when …
Preprint PDF Project Code

While prior federated learning (FL) methods mainly consider client heterogeneity, we focus on the Federated Domain Generalization (DG) …
Preprint PDF Project Project Code

A distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly …
Preprint PDF Project Project Code

Shapley values have become one of the most popular feature attribution explanation methods. However, most prior work has focused on …
PDF Project Code

Publications

Distribution matching (DM) is a versatile domain-invariant representation learning technique that has been applied to tasks such as …
Preprint Project

When each edge device of a network only perceives a local part of the environment, collaborative inference across multiple devices is …
Preprint Project

Text-to-Image (T2I) Diffusion Models have achieved remarkable performance in generating high quality images. However, enabling precise …
Preprint Project

In high-stake domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness …
Preprint Project Code

As Diffusion Models have shown promising performance, a lot of efforts have been made to improve the controllability of Diffusion …
Preprint Project

There has been a growing excitement that implicit graph generative models could be used to design or discover new molecules for …
PDF Project Code

Distribution matching can be used to learn invariant representations with applications in fairness and robustness. Most prior works …
Preprint PDF Project

Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are …
PDF Project Project Code

A central theme in federated learning (FL) is the fact that client data distributions are often not independent and identically …
PDF Project Project Code

Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned …
PDF Project Code

While normalizing flows for continuous data have been extensively researched, flows for discrete data have only recently been explored. …
PDF Project Code

The unsupervised task of aligning two or more distributions in a shared latent space has many applications including fair …
PDF Project Project Code

While previous distribution shift detection approaches can identify if a shift has occurred, these approaches cannot localize which …
PDF Project Project Code

In practical applications of machine learning, it is necessary to look beyond standard metrics such as test accuracy in order to …
PDF Project Code

We consider objective evaluation measures of explanations of complex black-box machine learning models. We propose simple robust …
PDF Project Code

We propose a unified framework for deep density models by formally defining density destructors. A density destructor is an invertible …
PDF Project Code

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of …
Preprint PDF Code

Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of …
PDF Poster Code

We propose a novel distribution that generalizes the Multinomial distribution to enable dependencies between dimensions. Our novel …
PDF Poster Code

We develop a fast algorithm for the Admixture of Poisson MRFs (APM) topic model and propose a novel metric to directly evaluate this …
PDF Poster Code

This paper introduces a new topic model based on an admixture of Poisson Markov Random Fields (APM), which can model dependencies …
PDF Poster Slides Code

Other Publications

Distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing …
PDF Project

Project

Existing research on label noise often focuses on simple uniform or classconditional noise. However, in many real-world settings, label …
PDF Code

Owing to the sheer volume of text generated by a microblog site like Twitter, it is often difficult to fully understand what is being …
PDF DOI

Noise cancellation in an MRI environment is difficult due to the high noise levels that are in the spectral range of human speech. This …
PDF DOI

Due to the sheer volume of text generated by a micro log site like Twitter, it is often difficult to fully understand what is being …
PDF DOI

Teaching

An introduction to artificial intelligence (AI).
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An introduction to artificial intelligence (AI).
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A graduate-level project-based introduction to artificial intelligence (AI) with a primary focus on unsupervised learning. The lecture …
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An introduction to artificial intelligence (AI).
Last updated on

A graduate-level project-based introduction to artificial intelligence (AI) with a primary focus on unsupervised learning. The lecture …
Last updated on

A graduate-level project-based introduction to artificial intelligence (AI) with a primary focus on unsupervised learning. The lecture …
Last updated on

This course introduces Python programming to students through data science problems. Students learn Python concepts as well as …
Last updated on

A graduate-level project-based introduction to artificial intelligence (AI) with a primary focus on unsupervised learning. The lecture …
Last updated on

This course introduces Python programming to students through data science problems. Students learn Python concepts as well as …
Last updated on

A graduate-level project-based introduction to artificial intelligence (AI) with a primary focus on unsupervised learning. The lecture …
Last updated on

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