Past Events

Math
November 30, 2023

Math Colloquium

Let’s Talk about Jobs, Internships, & Overseas, Oh My!

Math
November 9, 2023

Math Colloquium

Speaker: Jens Mache, L&C

Jens will tell us the story of how he came to do computer science at Lewis & Clark.

Math
October 26, 2023

Math Colloquium

Speaker: 

Robert Chang, Reed College

Robert’s work examines questions at the intersection of mathematical physics, probability, and complex analysis.

Math
October 12, 2023

Math Colloquium

Speaker: Renata Gerecke, NYC Mayor’s Office

Renata is a quantitative policy advisor for the New York City Mayor’s Office of Operations.

(this will be a Zoom talk- link provided closer to event)

Math
September 28, 2023

Math Colloquium

Speaker: Colin Starr, Willamette University

Colin is a combinatorist with expertise in graph theory and matroids. This talk will likely focus on recent work with visibility graphs.

math
September 14, 2023

Mathematical Scientists are Social! Social

Please join us  in front of SQRC!
Math Department would like to welcome new students and visit with the returning students! Come see why the Math Department is a great department. 

Pi Day 2023
March 14, 2023

Pi Day Celebration!

Come grab a slice of pie to celebrate pie day!

December 1, 2022

Data Visualization Workshop: December 1st, 4pm

Want to learn best practices for creating data visualizations? Do you like pizza? Watzek Library is hosting a Data Visualization workshop on Thursday, December 1st, from 4-6pm in the Library Classroom. Stop by anytime for help with a visualization you are working on. Pizza will be provided.

Roger Nelsen
November 10, 2022

Roger Nelsen Talk: Democracy Inaction: Why our elections are unfair

American presidential primaries are examples of
multicandidate elections in which plurality usually
determines the winner. Is this the “best” way to decide who
wins? While plurality is a common procedure, it has serious
flaws. Are there alternative procedures that are in some
sense more “fair”? How do we determine the “fairness” of
an election procedure? With no more mathematics than
arithmetic (to count votes), we will examine some alternate
procedures and fairness criteria.
Rogers
July 19, 2022

Tuesday Talks: Rogers Science Research

Student research presentations
Rogers
July 5, 2022

Tuesday Talks: Rogers Science Research

Student research presentations
Data
February 15, 2022

Research Talk

Tuesday, February 15, 2022
10:30-11:30
 
Title: Application of Machine Learning and Deep Learning Neural Networks in Health Informatics Abstract: With the rapid development in technology, more and more data are available for data analysis tasks. To glean useful insights from diverse data sets and develop predictive models, machine learning (ML), including deep learning (DL) methods, become the most promising solutions. Health Informatics have been vital research areas where ML and DL hold great promise in improving healthcare. In the research presentation, I include multiple studies that introduce ML and DL applications in healthcare
 
Computer
February 3, 2022

Research Talk: Visualization in the Age of Data: Human-looped Science & Design

Abstract: Data is everywhere, but without a means to understand it, data is fundamentally useless. One avenue to make data meaningful is the use of visualization—interactive computer graphics for visually analyzing information. But while visualization has attended to data sets of ever increasing size, we cannot lose track of the very human nature of visualization: It solves problems humans have, so we must use the science of visualization to help those users. In this talk, I discuss a few examples of how we have used visualization to address important problems while keeping the user in the loop. These designs incorporate expert analysis, user studies, and more. I conclude with some thoughts on future directions of visualization research.
Zoom Link: https://zoom.us/j/9016798003?pwd=Kys3OS9FT3ZudkZWYjlXejlzNjVwdz09
Computer Science
February 1, 2022

Research Talk: Low Rank and Sparse Methods for Data Science

Abstract:
In order to better understand large data-sets, it is helpful to understand the underlying patterns. Even when the underlying pattern is nonlinear, the data matrix can be approximated as being low rank, a property that allows for techniques to analyze the data in terms of a low dimensional latent space using Principal Component Analysis (PCA) or non-negative matrix factorization (NMF), identifying outliers through Robust PCA, and accurately inferring missing entries from very few observations of a matrix through matrix completion. 
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