PhD Thesis DefensesSpring 2016 Thursday, March 17, 2016 Friday, March 18, 2016 Thursday, March 24, 2016 Friday, March 25, 2016 Friday, March 25, 2016 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 |
MS Thesis DefensesSpring 2016 |
Senior Capstone PresentationsSpring 2016 Tuesday, March 29, 2016 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 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
Thursday, April 28, 2016 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.