UW Data Science Seminar: Armeen Taeb


4:30 pm – 5:20 pm


Please join us for a UW Data Science Seminar on Wednesday, April 17th from 4:30 to 5:20 p.m. PST. The seminar will feature Armeen Taeb, an Assistant Professor in the UW Department of Statistics.

The seminar will be held in the Physics/Astronomy Auditorium (PAA), Room A118 – campus map.


Model Selection and False Positive Error Control in Complex Modeling Paradigms

Abstract: Controlling the false positive error in model selection is a prominent paradigm for gathering evidence in data-driven science.  In model selection problems such as variable selection and graph estimation, models are characterized by an underlying Boolean structure such as presence or absence of a variable or an edge.  Therefore, false positive error or false negative error can be conveniently specified as the number of variables/edges that are incorrectly included or excluded in an estimated model.  However, the increasing complexity of modern datasets has been accompanied by the use of sophisticated modeling paradigms in which defining false positive error is a significant challenge.  For example, models specified by structures such as partitions (for clustering), permutations (for ranking), directed acyclic graphs (for causal inference), or subspaces (for principal components analysis) are not characterized by a simple Boolean logical structure, which leads to difficulties with formalizing and controlling false positive error.  We present a generic approach to endow a collection of models with partial order structure, which leads to systematic approaches for defining natural generalizations of false positive error and methodology for controlling this error.  (Joint work with Peter Bühlmann, Venkat Chandrasekaran, and Parikshit Shah)

Bio: Armeen Taeb is an assistant professor in the Department of Statistics at the University of Washington. His research interests lie at the interface of optimization and statistics. His 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. He is also interested in exploring the utility of statistical methodologies for real-world applications, especially in the earth sciences. Prior to coming to UW, Armeen 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, he obtained my PhD in the Department of Electrical Engineering at Caltech.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.

The 2023-2024 seminars will be held in person, and are free and open to the public.