Daniel Kessler (he/him)

Postdoctoral Researcher, University of Washington

Email dakess@uw.edu
UW Box Number 354322
Homepage Personal Website 
ORCID iD  0000-0003-2052-025X 

Dan completed his PhD in 2023 at the Department of Statistics at the University of Michigan where he was advised by Professor Liza Levina. He is currently an NSF Mathematical Sciences Postdoctoral Research Fellow at the University of Washington where he works with Professor Daniela Witten; in addition, he is concurrently both a PIMS-Simons and UW Data Science Postdoctoral Fellow. His research interests include the statistical analysis of networks, post-selective inference, high-dimensional statistics, applications involving human neuroimaging, computational and cognitive neuroscience, and high performance computing. In 2024 he will join UNC-Chapel Hill as a tenure-track assistant professor where he will be jointly appointed in the Department of Statistics & Operations Research and the School of Data Science and Society.


Computational Inference for Directions in Canonical Correlation Analysis
Daniel Kessler, Elizaveta Levina
Canonical Correlation Analysis (CCA) is a method for analyzing pairs of random vectors; it learns a sequence of paired linear transformations such that the…

Predicting Responses from Weighted Networks with Node Covariates in an Application to Neuroimaging
Daniel Kessler, Keith Levin, Elizaveta Levina
We consider the setting where many networks are observed on a common node set, and each observation comprises edge weights of a network, covariates observed at…

Graph-aware Modeling of Brain Connectivity Networks
Yura Kim, Daniel Kessler, Elizaveta Levina
Functional connections in the brain are frequently represented by weighted networks, with nodes representing locations in the brain, and edges representing the…

Approximate Post-Selective Inference for Regression with the Group LASSO
Snigdha Panigrahi, Peter W. MacDonald, Daniel Kessler
After selection with the Group LASSO (or generalized variants such as the overlapping, sparse, or standardized Group LASSO), inference for the selected…

Network classification with applications to brain connectomics
Jesús D. Arroyo-Relión, Daniel Kessler, Elizaveta Levina, Stephan F. Taylor
While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of…

Which Findings from the Functional Neuromaging Literature Can We Trust?
Daniel Kessler, Michael Angstadt, Chandra Sripada
In their recent "Cluster Failure" paper, Eklund and colleagues cast doubt on the accuracy of a widely used statistical test in functional neuroimaging. Here,…

Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine
Takanori Watanabe, Daniel Kessler, Clayton Scott, Michael Angstadt, Chandra Sripada
Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using…