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Student Presentations

PhD Thesis Defenses

Spring 2016

Thursday, March 17, 2016
1:30 p.m. in Yost 306
Title: Sequential Monte Carlo Estimation for Dynamic Brain Imaging In Magnetoencephalography
Student: Lijun Yu
Advisors: Daniela Calvetti and Erkki Somersalo

Friday, March 18, 2016
2:00 p.m. in Yost 306
Title: Bayesian Parameter Estimation and Inference Across Scales
Student: Margaret Callahan
Advisors: Daniela Calvetti and Erkki Somersalo

Thursday, March 24, 2016
10:00 a.m. in Yost 306
Title: Rademacher Sums, Hecke Operators, and Moonshine
Student: Paul Bruno
Advisor: John Duncan

Friday, March 25, 2016
4:00 p.m. in Yost 306
Title: The Surface Area Deviation of the Euclidean Ball and a Polytope
Student: Steven Hoehner
Advisor: Elisabeth Werner

Friday, March 25, 2016
Dissertation Defense Reception
5:30 p.m. in the Faculty/Graduate Student Lounge on the second floor of Yost Hall

The MAMS Department will host a reception to celebrate our PhD students’ successful dissertation defenses, with cake and beverages provided.  All department faculty, students, staff, and visitors are welcome.

Thursday, April 28, 2016
12:00 p.m. in Yost 306
Title: Stratified Worm Burden Approach to Modeling Schistosomiasis Transmission and Control
Student: Nara Yoon
Advisor: David Gurarie

MS Thesis Defenses

Spring 2016

Senior Capstone Presentations

Spring 2016

Tuesday, March 29, 2016
4:15 p.m. in Yost Hall, Room 343
Student: Yu Peng
Advisor: Erkki Somersalo

Title: Independent Component Analysis

Abstract: Independent Component Analysis (ICA) is a powerful computational tool for separating independently generated signals from each other based on a multi-channel registration of the mixed signal. The classic example is the cocktail party problem, in which the goal is to separate several simultaneous speakers from each other. ICA is widely used, e.g., in medical applications such as electroencephalography (EEG) to discern the different brain signals from the noisy registration. The talk reviews the basic idea behind the ICA.

Monday, April 25, 2016
1:00 p.m. in Yost Hall, Room 306
Student: Margo J. Suryanaga
Advisor: Wanda Strychalski

Title: A Comparison of Public Market Equivalent Calculations

Abstract: Generally, the Private Equity industry calculates Internal Rates of Return (IRR) and Market Multiples to weigh the performance of an investment or fund. However, while these calculations can be used to compare between private investments, it cannot be used to compare private investments with other asset classes, requiring the calculation of a public market equivalent. A public market equivalent (PME) acts as a measure of the performance of a private equity fund by comparing it against a benchmark in the public market (in our case the S&P500). While several methods have been created to calculate PMEs have been found, we will take a closer look at three methods of calculating PME: Long-Nickels method, Kaplan-Schoar method, and Direct Alpha method. We will look at how each equation and/or algorithm to calculate PME affects its ability to accurately depict the performance of an investment. We will also look at using these methods to then determine the best one among the three to be a basis for a predictive model.

 

MATH 352 Senior Capstone Presentation Session
Wednesday, April 27, 2016
9:00 a.m.-3:15 p.m. in Yost Hall, Room 306
Organizer: Joel Langer
See here for the itinerary.

 

Thursday, April 28, 2016
3:00 p.m. in Yost Hall, Room 306
Student: James Matthiesen
Advisor: Danhong Song

Title: Finding Bestsellers? An Application of Data Analysis with Open Source Data

Abstract: This project analyzes a historical data set containing the daily top 100 bestselling shoe products on amazon.com over a 2-and-a-half-year period. The project goal is to identify characteristics which may be indicative of successful products, success being defined as the amount of days a product is listed in the top 100 bestsellers, and to attempt to predict the amount of days a product will appear on the best sellers list. This project will showcase the practical application of data analysis techniques such as linear regression, principal components analysis, Poisson regression, time series analysis, and exploratory data analysis among other techniques.

For a list of past student presentations, please click here.