Invited Speakers

jenn berg

Fitchburg State University

An (incomplete) history of my teaching mistakes.

Distinguished Teacher Lecture: Saturday November 23, 2018, 9:45 AM

Abstract: Using teaching mistakes as the object of our attention this talk will explore the craft of teaching. I’ll highlight some of the mistakes I’ve made as a teacher (of both the large and small variety) and use these examples to illustrate what I’ve learned about teaching collegiate mathematics.

Bio: jenn berg wanted to be a lawyer when she grew up; on the way to getting a good LSAT score she fell in love with mathematics. Teaching became a perfect way to express her love of both language and mathematics. Since joining the faculty at Fitchburg State University in 2008 she has worked on honing her teaching craft with the help of her colleagues at the college and at secondary schools in the north central Massachusetts region. As chair of the department she currently also uses her love of language to entertain others during meetings and move the college towards more inclusive policies, curriculum, and teaching practices.

Steven Miller

Williams College

The German Tank Problem: Math/Stats At War!

The Christie Lecture: Friday November 22, 2018, 7:30 PM

Abstract: During World War II the German army used tanks to devastating advantage. The Allies needed accurate estimates of their tank production and deployment. They used two approaches to find these values: spies, and statistics. In this talk we describe the statistical approach and its generalization. Assuming the tanks are labeled consecutively starting at 1, if we observe k serial numbers from an unknown number N of tanks, with the maximum observed value m, what is the best estimate for N?

This is now known as the German Tank Problem, and is a terrific example of the applicability of mathematics and statistics in the real world. We quickly review some needed combinatorial identities (which is why we are able to obtain clean, closed form expressions), give the proof for the standard problem, discuss the generalization, and show how if we were unable to do the algebra we could guess the formula by an application of linear regression, thus highlighting its power and applicability. Most of the talk only uses basic algebra and elementary knowledge of WWII.

Bio:

Steven J. Miller is a professor of Mathematics at Williams College. He received a bachelor degree in Mathematics and Physics from Yale in 1996 and a PhD in Mathematics from Princeton in 2002. His research interests cover a vast range of mathematical areas such as Additive, Analytic, Combinatorial and Computational Number Theory, Probability Theory, Random Matrix Theory, Random Graphs, Benford's law, Cryptography and Operations Research. He has also collaborated on various projects in Accounting, Computer Science, Economics, Marketing, Physics, Sabermetrics and Statistics. Dr Miller has written over 140 research articles and 6 books. He has also supervised more than 400 high school, undergraduate and graduate students in research projects. He is a Fellow of the American Mathematical Society, a Senator at Large for Phi Beta Kappa, and serves on the Mt Greylock Regional School Committee, where he has applied mathematics to advance a $60 million dollar middle/high school building project and to successfully regionalize three school districts.

Lorin Crawford

Brown University

Statistical Framework for Identifying Features that Differentiate Classes of 3D Shapes

Saturday, November 23, 2018, 8:30 AM

Abstract: The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global-patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). We present SINATRA: the first statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our novel algorithm takes in two classes of shapes and highlights the physical features that best describe the variation between them. We use a rigorous simulation framework to assess our approach. Lastly, as a case study, we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.

Bio: Lorin Crawford is the RGSS Assistant Professor of Biostatistics, and a core faculty member of the Center for Statistical Sciences and Center for Computational Molecular Biology at Brown University. His scientific research interests involve the development of novel and efficient computational methodologies to address complex problems in statistical genetics, cancer pharmacology, and radiomics (e.g. cancer imaging). Dr. Crawford has an extensive background in modeling massive data sets of high-throughput molecular information as it pertains to functional genomics and cellular-based biological processes. His most recent work has earned him a place on Forbes 30 Under 30 list, The Root 100 Most Influential African Americans list, and recognition as an Alfred P. Sloan Research Fellow.

Before joining Brown, Dr. Crawford received his PhD from the Department of Statistical Science at Duke University where he was formerly co-advised by Drs. Sayan Mukherjee and Kris C. Wood. He also received his Bachelor of Science degree in Mathematics from Clark Atlanta University.

David Oury

True Bearing Insights

Ten easy steps from statistics to autonomous machines learning: a data science incentive framework for academia and industry

Friday, November 22, 2018, 4:00 PM

Abstract: These are exciting times in data science and machine learning, especially for those with skills in statistics, mathematics, computer science and business. There is an abundance of opportunities to integrate business acumen and academic research. In this talk I'll describe ways to learn and teach these skills, to integrate business and academia through data science, and to create an engaging future for each of us in our work.

Bio: David Oury is Head of Research & Development at True Bearing Insights, a Boston data science startup that specializes in forecasting, and lead the Data Science initiative at Bentley University as it's Development Director. He has a M.S. in Mathematics from McGill University in Montreal, Canada and a Ph.D. in Mathematics from Macquarie University in Sydney, Australia.