Department of Statistics, University of Washington

Email: ataeb@uw.edu

CV (updated October 2024)

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 and latent-variable modeling, learning provably optimal causal models from data, domain adaptation, and false positive error control in non-traditional settings. I am also interested in exploring the utility of statistical methodologies for real-world applications, especially in the earth sciences.

Funding: I am grateful for support by the National Science Foundation with grant DMS-2413074 (PI) and by the Royalty Research Fund at the University of Washington (PI).

PhD students: I am currently looking for highly motivated PhD students. 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!

Service for new researchers: I am passionate about giving young researchers in data science a platform to grow as researchers and individuals. Together with Yuan Jiang and Pragya Sur, I organized the 2024 IMS New Researcher Conference. I am now serving as the president of the IMS New Researcher Group. If you have any suggestions for how to help young researchers, please do not hesitate to email me!

** Papers **

**Preprints**

- "A Spectral Method for Multi-view Subspace Learning Using the Product of Projections
"

R. Sergazinov, A. Taeb, I. Gaynanova

*2024.* - "An Asymptotically Optimal Coordinate Descent
Algorithm For Learning Bayesian Networks from
Gaussian Models
"

T. Xu, S. Küçükyavuz, A. Shojaie, A. Taeb [software]

*2024.* - "Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
"

T. Xu*, A. Taeb*, S. Küçükyavuz, A. Shojaie [software]

*2024.* - "Extremal Graphical Modeling with Latent Variables"

S. Engelke, A. Taeb [software]

*2024.* - "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.*

**Journal**

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

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

*Journal of Machine Learning Research, 2024.* - "Model Selection over Partially Ordered Sets"

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

*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.*

**Technical notes**

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

M. Azadkia, A. Taeb, P. Bühlmann

*2022.* - "Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood"

A. Taeb, P. Shah, V. Chandrasekaran

*2020.*

** 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, 2024).

Statistical Learning: Modeling, Prediction, and Computing (Winter 2023, 2024).

- 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).