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Math Department Colloquium

The Math Department Colloquium features recent research of our faculty and our invited visitors.

If you wish to be added to the department colloquium mailing list, or if you wish to give a colloquium talk, please contact the organizer Partha Mukherjee (parthamukherjee@boisestate.edu).

Refreshments will be served in MB226 before each talk.

Archive of past math department colloquium abstracts

Schedule for 2018–2019

Doug Bullock
Associate Professor and Associate Dean of COAS, Department of Mathematics, Boise State University
Title: The Boise State Calculus Project
Date: September 06 (Thursday), 2018
Time: 3:00–4:00 PM
Room: ILC 303
Abstract: I will describe the origins, motivation, implementation, results and impacts of a multi-year project to reshape the Calculus sequence at Boise State. The talk will be structured as a narrative, with research results presented at the points where they naturally emerged from the evolving project. Research results will fall into two categories:
• Institutional transformation and change management issues addressed during initial implementation and across subsequent project evolution.
• Natural experiments emerging from the project, measuring the project’s impact on success in Calculus, longitudinal performance in later courses, and retention at BSU or in major.
Restructuring of the traditional Calculus content and curriculum were a major part of the project. I will describe general themes of the curriculum changes, illustrated with specific examples. Although the project is effectively concluded, sustainability and future evolution will be discussed, time permitting.

Joy Wright Whitenack
Assistant Chair, Director of Undergraduate Studies, Associate Professor
Department of Mathematics, Virginia Commonwealth University
Title: Coaching Middle School Mathematics Teachers: A Case for Emergent Professional Learning Communities
Date: October 18 (Thursday), 2018
Time: 3:00–4:00 PM
Room: MB 139 (Math Building)
Abstract: In this presentation, I used mixed research methods to highlight the important role that coaches can play in supporting teacher learning and how these supports, in turn, correlated with student learning as measured by state achievement assessments. During the presentation, I will report results from statistical methods to address how teachers’ beliefs and students’ learning were positively affected because of their work with mathematics specialists. Against this backdrop, I will present findings of one case study of one specialist’s work with teachers to reconstruct the communities of practice that emerged as she and teachers engaged in this daily work. I then use these findings to argue for how particular practices sustained and contributed to a community that both support and engender teachers’ professional learning.

Caroline Uhler
Henry L. and Grace Doherty Associate Professor
Department of Electrical Engineering and Computer Science and Institute for Data, Systems and Society
Massachusetts Institute of Technology
Title: From Causal Inference to Gene Regulation
Date: January 31, 2019 (Thursday)
Time: 4:00–5:00 PM
Room: Remote broadcast in MB 139 (Math Building)
Abstract: A recent break-through in genomics makes it possible to perform perturbation experiments at a very large scale. The availability of such data motivates the development of a causal inference framework that is based on observational and interventional data. We first characterize the causal relationships that are identifiable from interventional data. In particular, we show that imperfect interventions, which only modify (i.e., without necessarily eliminating) the dependencies between targeted variables and their causes, provide the same causal information as perfect interventions, despite being less invasive. Second, we present the first provably consistent algorithm for learning a causal network from a mix of observational and interventional data. This requires us to develop new results in geometric combinatorics. In particular, we introduce DAG associahedra, a family of polytopes that extend the prominent graph associahedra to the directed setting. We end by discussing applications of this causal inference framework to the estimation of gene regulatory networks.