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Ethnomathematics: Computations among Maya calendars

Monday, November 4 at 4pm in Olin 201. Ximena Catepillán, PhD, professor emerita of Millersville University will talk about computations among Maya calendars. Mesoamerican calendars were many and complex. A good number of studies have been done to decipher them. By the arrival of Hernan Cortes in 1519 in what current day Mexico is, there were 21 calendars in use while 4 of them were extinct. Using astronomical observations, the Maya developed an elaborate system of calendars, among them the Tzolkin Calendar, the Haab Calendar, the Round Calendar, and the Long Count. Which operations did the Maya use to perform their calendrical computations? While they used a vigesimal system to write the numbers, this system was never used in connection with days. No inscriptions use vigesimal numbers but rather quasi-vigesimal numbers. In spoken numbers, a mix of decimal and vigesimal notation appears.  They also needed to divide to do some of the calendar conversions. Ximena will illustrate calendri
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Graphs and Hypergraphs and Topology, Oh My!

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Monday October 21 at 4pm in Olin 201. Emilie Purvine, of the Pacific Northwest National Lab will talk about how mathematical structures and concepts can be great models of real-world data. For example, differential equations have a long history of success in applied mathematics to model dynamics found in rivers and oceans, the atmosphere, and molecular systems (just to name a few!). Network science is an area of applied math that uses graph structures to model relational systems like social, collaboration, and transportation networks. Graphs, however, are limited to modeling pairwise relationships among entities. Hypergraphs and topological spaces provide alternate models of relational systems that allow for arbitrary sized and structured relationships. In this talk, Dr. Purvine will introduce the mathematical concepts of graphs, hypergraphs, and topology and show how they are used to model real-world data from a variety of applications including biological systems, chemi
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Learn as you go! How I went from Mathematics to Biology.

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Monday May 8 at 4pm in Olin 201. An interdisciplinary approach only requires an open mind and a sprinkle of serendipity. This talk will be a brief overview of my journey through STEM and how I was able to approach biological problems by starting with faint recollections from my mathematics training. Each problem required me to learn new concepts in biology and relearn mathematics to solve a problem. In this talk I will discuss three vignettes on how I applied mathematics to solve a protein geometry problem, to simplify a complex network based on gene-to-gene relationships, and to determine the processes that underlie the decay of biological molecules. All three stories share a central theme: learn as you go and be open to ideas! Bio: Matthew Tien is an Assistant Professor of Biology at Whitman College. His research interests center on finding novel gene-regulatory systems in bacteria and engineering microbes for bioremediation efforts. Matthew was an undergraduate at the University of
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From Sports Analytics to Business Analytics

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Monday April 24 at 4pm in Olin 201. Riley Foreman '15 will talk about business analytics, as part of her role within Hippo . Riley partners with various business units to solve highly analytical and operational problems. She will be sharing one of her recent analyses that helped inform the day-to-day activities of Hippo's new Account Management team and efforts to retain customers within the Hippo Agency. Riley will also discuss how her liberal arts and mathematics background helps her navigate the highly-regulated and complex insurance industry and understand how other areas of the company, such as the actuarial team, affects overall strategy and operations.   Bio: Riley Foreman '15 is currently the manager of business operations and finance at Hippo Insurance. After completing a combined degree in Economics-Mathematics from Whitman College, she began her career in ESPN's Stats & Info Group as a Production Researcher, providing stats-driven storylines to b
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Business Intelligence: Gathering, Visualizing, and Analyzing Big Data

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Monday April 10 at 4pm in Olin 201. Brooke Taylor will discuss a career path in Business Intelligence, a technical role combining mathematics and computer science to deliver key insights to organizations on trends, drivers, and areas of opportunity through working with data. She will walk through the skills required in the role and how her experiences at Whitman and in internships played a role in her success, provide examples of business problems she has worked on at Amazon, and also outline similar roles and how they differ (e.g. Data Science, Business/Data Analyst, Data Engineer).   Bio: Brooke graduated from Whitman in 2018, majoring in Mathematics and minoring in Computer Science and Physics. She is currently a Senior Business Intelligence Engineer at Amazon, and over the past five years has worked on business problems in Alexa, Customer Service, and Supply Chain. While she began her Amazon journey out in Boston, she is now back in the Seattle area living with two cat
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Support Vector Machines, Convolutions, and Careers in Data Science

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Monday March 27 at 4pm in Olin 201. Speaker: Dr. Elly Farnell. Support vector machines are one of many techniques within the realm of classification problems in machine learning – given labeled training data that contains multiple classes (e.g. pictures of cats and dogs with associated labels), we define an algorithm to determine which class a novel data point belongs to (in the cats and dogs example, we’d like our algorithm to be able to tell us whether a new picture contains a cat or a dog). I’ll talk about theoretical results related to support vector machines that we recently published in Foundations of Data Science, which use a classical theorem from topology to characterize when a linear classifier is optimal. I’ll also talk about some of the work I do at Amazon Web Services (AWS) and will highlight how a particular type of function called a convolution has played a particularly useful role. Finally, as someone who has transitioned from academia to a data science c
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A Path to Applied Mathematics at the DOE

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Monday February 27 at 4pm in Olin 201. Dr. Rick Archibald will discuss pathways to careers in applied mathematics at the Department of Energy (DOE). This talk is part of the Computational Research Leadership Council (CRLC) Seminar Series 2022-2023. This presentation will highlight different applied mathematical research that is supported at the laboratory complex at the Department of Energy (DOE). It will focus on data analytics and provide information on various programs designed to foster engagement with the DOE.  BIO: Dr. Archibald received his Ph.D. in Mathematics, from Arizona State University in 2002. He works in the Computational and Applied Mathematics Group at Oak Ridge National Laboratory .  Dr. Archibald 's research interests lie in data reconstruction and analysis, high-order edge detection, large scale optimization, time integration, and uncertainty quantification.  
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