Department of Statistics, University of Washington

Email: ataeb@uw.edu

CV (updated December 2022)

Google Scholar Profile

I am an assistant professor in the Department of Statistics at the University of Washington. I was a postdoctoral fellow of ETH Foundations of Data Science (ETH-FDS) at ETH Zürich, mentored by Peter Bühlmann. Previously, under the supervision of Venkat Chandrasekaran, I obtained my PhD in the Department of Electrical Engineering at Caltech.

My research interests lie at the interface of optimization and statistics. My work currently focuses on developing efficient methods for graphical modeling and latent-variable modeling, learning causal relations from data, and controlling for false discoveries in non-traditional settings. I am also interested in exploring the utility of statistical methodologies for real-world applications such as water resource management and medical imaging.

*I am currently looking for motivated PhD students with strong mathematical background and interest in methodological and theoretical development in any of the following areas: selective inference and multiple testing especially in non-standard settings, causal inference and distributional robustness, graphical modeling in the presence of latent variables, and more generally problems at the interface of optimization and statistics. If you are a UW PhD student or have just been admitted, feel free to reach out to learn more and to talk about specific research ideas!*

** Papers **

**Preprints**

- "Causality-oriented Robustness: Exploiting General Additive Interventions"

X. Shen, P. Bühlmann, A. Taeb [software][poster]

*2023.* - "Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
"

J. Gamella, A. Taeb, C. Heinze-Deml, P. Bühlmann, [software]

*2022.* - "A Fast Non-parametric Approach for Local Causal Structure Learning"

M. Azadkia, A. Taeb, P. Bühlmann

*2022.* - "Learning and scoring Gaussian latent causal models with unknown additive interventions"

A. Taeb, J. Gamella, C. Heinze-Deml, P. Bühlmann, [software] [video]

*2021.* - "Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood"

A. Taeb, P. Shah, V. Chandrasekaran

*2020.*

**Journal**

- "Model Selection over Partially Ordered Sets"

A. Taeb, P. Bühlmann, V. Chandrasekaran [software][poster]

*Proceedings of National Academy of Sciences, 2024.* - "A Look at Robusness and Stability of L1 versus L0 regularization
: Discussion of Papers by Bertsimas et al. and Hastie et al."

Y. Chen, A. Taeb, P. Bühlmann, [software]

*Statistical Science, 2020.* - "False Discovery and Its Control in Low-rank Estimation"

A. Taeb, P. Shah, V. Chandrasekaran, [software] [video].

*Journal of the Royal Statistical Society (Series B), 2020.* - "Interpreting Latent Variables in Factor Models via Convex Optimization"

A. Taeb, V. Chandrasekaran

*Mathematical Programming, 2018*. - "A Statistical Graphical Model of the California Reservoir Network"

A. Taeb, J.T. Reager, M. Turmon, V. Chandrasekaran [software] [press release] [broad audience]

*Water Resources Research, 2017.* - "Visual Stylometry using background selection and wavelet-HMT-based Fisher Information distances"

H. Qi, A. Taeb, and S. Hughes

*EURASIP Signal Processing, 2012*

**PhD Thesis**

- "Latent-variable Modeling: Algorithms, Inference, and Applications"

A. Taeb, California Institute of Technology, 2019

W. P. Carey & Co. Prize for oustanding thesis in Applied Mathematics

**Book**

- "Inverse Problems and Data Assimilation"

D. Sanz-Alonso, A. Stuart, and A. Taeb

*Cambridge University Press, London Mathematical Society Student Texts*, 2023

**Conferences and Workshops**

- "Provable Concept Learning for Interpretable Predictions using
Variational Inference"

A. Taeb, N. Ruggeri, C. Schnuck, F. Yang, [software][video][poster]

*ICML Workshop on AI4Science,2022.* - "Maximin Analysis of Message Passing Algorithms for Recovering Block Sparse Signals"

A. Taeb, A. Maleki, C. Studer, R. Baraniuk

*Signal Processing with Adaptive Sparse Structured Representations (SPARS), 2013.*

** Teaching **

- University of Washington

Multiple Testing and Modern Inference (Fall 2023). Notes can be viewed here

Statistical Machine Learning for Data Scientists (Spring 2023).

Applied Statistics Capstone (Winter 2023).

Statistical Learning: Modeling, Prediction, and Computing (Winter 2023).

- Instructor, ETH Zürich

Topic: Seminar on Multiple Testing for Modern Data Science

Co-teaching (with Matthias Löffler), 2020

- Summer School Lectures, University della Svizzera Italiana, Switzerland

Topic: Data Assimilation and Inverse Problems

Co-Instructor (with Andrew Stuart), 2018

- Graduate Teaching Assistant, Caltech

Inverse Problems and Data Assimilation (Fall 2017).

Mathematical Statistics (Spring 2016).

Mathematical Optimization (Fall 2014, 2015).