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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Chris Glynn
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
A new fully automated approach for aligning and comparing shapes
Boyer, D. M., Puente, J., Gladman, J. T., Glynn, C., Mukherjee, S., Yapuncich, G. S., and Daubechies, I.Published in The Anatomical Record, 2015
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Detailed anatomical orientations for certain types of morphometric measurements can be determined automatically with geometric algorithms
Boyer, D. M., Winchester, J. M., Glynn, C., and Puente, J.Published in The Anatomical Record, 2015
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Single neurons may encode simultaneous stimuli by switching between activity patterns
Caruso, V. C., Mohl, J. T., Glynn, C., Lee, J., Willett, S. M., Zaman, A., Ebhira, A. F., Estrada, R., Freiwald W. A., Tokdar, S. T., and Groh, J. M.Published in Nature Communications, 2018
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Bayesian analysis of Dynamic Linear Topic Models
Glynn, C., Tokdar, S. T., Howard, B., and Banks, D. L.Published in Bayesian Analysis, 2019
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Sequential demand forecasting of bursty count data
Irie, K., Glynn, C., and Aktekin, T.Published in (Submitted for review), 2019
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Dynamics of homelessness in urban America
Glynn, C. and Fox, E. B.Published in The Annals of Applied Statistics, 2019
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Bayesian inference for Polya Inverse Gamma models
Glynn, C., He, J., Polson, N. G., and Xu, J.Published in (Submitted for review), 2019
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Inflection points in community-level homeless rates
Glynn, C., Byrne, T. H., and Culhane, D. P.Published in (Submitted for review), 2019
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Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure
Glynn, C., Tokdar, S. T., Zaman, A., Caruso, V., Mohl, J. T., Willett, S., M., and Groh, J. M.Published in (Invited revision), 2019
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
DS 768 - Forecasting Analytics
Undergraduate course, Paul College of Business and Economics, 2019
This course explores elements of statistical forecasting with applications in business decision-making. The course is project-based and relies on hands-on activities in statistical software and applied data analyses. Students analyze time series data from by a wide range of applications, including finance, marketing, hospitality management, and human resources.
Paul 598 - Artifex
Undergraduate course, Paul College of Business and Economics, 2019
The primary purpose of this course is to deliver an experiential learning opportunity in business analytics to students at Paul College, building skills required of analytics professionals. The course delivery is a mix of lectures and project-based learning. In one course period per week, lectures on modern statistical tools and practice are given. In the second course period per week, students apply those tools to address challenges in real data provided by industrial partners. In addition, industrial partners deliver guest lectures both in-person and remotely. In Fall 2019, students are working to automate the forecasting systems of Peak Organic Brewing, a major New England brewery.
ADMN 420 - Business Statistics
Undergraduate course, Paul College of Business and Economics, 2019
This course introduces students to data analysis with applications in business decision-making. Fundamentals of exploratory data analysis, probability models, and statistical inference are covered. Students are expected to develop literacy in data analysis, understand key concepts such as the distinction between correlation and causality, and build linear regression models.