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The Major Qualifying Project (MQP) is a professional-level design or research experience completed by every WPI undergraduate. An integral element of WPI's project-enriched education, the immersive capstone project enables students to synthesize their learning by tackling and solving real-world problems in their fields of study.

MQPs completed by Actuarial Mathematics (MAC) and Mathematical Sciences (MA) students:

Actuarial Mathematics

Title: Explorations of Sequence Risk
Students: Jonathan Andrew Furey Cohen (MAC), Jeremy John (MAC), Jacob B Steinberg, (MAC)
Advisors: Jon Abraham, Barry Posterro
Academic Year: 2018-2019

The accumulation phase of retirement planning is when an individual accrues assets that would fund their retirement. The decumulation phase begins when they retire. Our work is based on parts of Clare, Seaton, Smith, and Thomas’s paper on sequence risk. Sequence risk is the uncertainty created by the order of a specific set of returns. We simulated portfolios’ accumulation and decumulation phases to illustrate how different sequences of the same set of returns result in different portfolio values. By creating 100,000 permutations for each phase, we analyzed each scenario’s ending values for accumulation and annual withdrawal rates for decumulation.

Title: Projecting The Ultimate Loss of Catastrophic Events
Students: Elizabeth Hansen (MAC), Amber Munderville (MAC), Mike Sullivan (MAC), Tyler Turchiarelli (MAC)
Advisors: Jon  Abraham, Barry Posterro.
Sponsor: The Hanover Insurance Group
Academic Year: 2016-2017

In collaboration with the Hanover Insurance Group, we worked to develop a predictive model that projects the ultimate losses associated with catastrophes. The model we created is interactive and user-friendly. Users are able to select various characteristics that define the parameters for the model. The printable summary report presents graphical views of the expected development patterns over time and additional information regarding the projections. This model can be updated on a daily basis to ensure accuracy and will ultimately aid the catastrophe reserving process at Hanover.

Analysis

Title: Stability Properties of a Crack Inverse Problem in Half Space
Student: Andrew Murdza (MA)
Advisor: Darko Volkov
Academic Year: 2019-2020

This project focuses on the analysis of a partial differential equation model relevant in the field of geophysics where sensors can capture seismic and displacement data. The question studied involves whether the geometry of faults, total slip between plates, and accumulated mechanical stress can be determined given data that results in an overdetermined system.

Computational Biomechanics

Title: A Marker-Point Model for Simulation of Elastic Surface Deformation
Student: Kamryn P. Spinelli (MA)
Advisor: Min Wu
Academic Year: 2020-2021

This project developed a higher-accuracy method to simulate the deformation of rotationally-symmetric surfaces such as plant cells. Such a model increases computational efficiency and could be used to better understand how plant cells grow or how deformation can encourage diffusion on the cellular or nuclear level. The project incorporates elements of numerical analysis and differential geometry.

Discrete Mathematics

Title: Regulatory Network Models in Biology
Student: Manasi Pradeep Vartak (MA)
Advisor: Brigitte Servatius
Co-Advisor: Marian Walhout (UMass Medical School)
Academic Year: 2009-2010

In this project, we studied transcriptional regulation in C. elegans through a network approach. We used techniques analyzing degree distribution, motifs, gene regulation subgraphs etc. to investigate various properties of the network. Our motif analysis discovered previously unknown motifs that are likely to have biological significance. We introduced a new technique for quantifying amount of gene regulation and formulated a new hypothesis to predict autoregulation. Our results will serve as a basis for future biological experiments.

Financial Mathematics

Title: Computations in Option Pricing Engines
Students: Vital Mendonca Filho (MA), Pavee Phongsopa (MA), Nicholas Wotton (MA)
Advisors: Yanhua Li (CS), Qingshuo Song (MA), Gu Wang (MA),
Academic Year: 2019-2020

This project explores how machine learning techniques can be utilized in financial models such as option pricing methods.

Financial Statistics

Title: Analyzing the Dynamic Relationship Between Intraday Trading Activity and Volatility Using High-Frequency Data
Students: Emily Baker (MA), Ryan Candy (MA), Isadora Coughlin (MA)
Advisors: Fangfang Wang (MA), Jian Zou (MA)
Academic Year: 2020-2021

Measuring equity volatility is an important metric and understanding, describing, and predicting volatility using trading activity provides insight into navigating the stock market. Taking inspiration from existing research analyzing volatility in the stock market, we explore the dynamic relationship between trading volume, trading frequency, and volatility on an intraday basis across ten stocks in the consumer discretionary sector of the S&P 100 for the fourth quarter of 2013. Using three different volatility measures we implement variations of the heterogeneous autoregressive model and vector autoregressive model to investigate the lead-and-lag relationship between volatility and trading activity. Our quantitative analysis provides strong empirical evidence that current trading frequency and trading volume can be used to predict 30-minute measures of volatility and that the prior day rolling average and lagged trading measures are useful predictors in modeling the volatility measures.

Machine Learning

Title: Player Performance Prediction Automation for Draftkings
Students: Kayleigh Cambell (MA), Diego Gonzalez Villalobos (ECE), Benjamin Huang (ECE), Gabriel Katz (MA), Skylar O'Connell (CS)
Advisors: Donald Brown (ECE), Randy Paffenroth (MA)
Sponsor: Draftkings
Academic Year: 2020-2021

In this Major Qualifying Project, we worked with DraftKings, an online daily fantasy sports company, to build a model that would predict the number of points, rebounds, and assists a NBA basketball player would score in a game, as well as the closing lines for NBA games. Our data sources were numerical and categorical data from DraftKings as well as data gathered from third-party sources. Then, we utilized algorithms such as linear regression, Lasso regression, random forest, and neural networks to predict the number of points, rebounds, and assists a NBA basketball player would score in a game, as well as the game lines for NBA games. We were able to reduce the standard deviation of our prediction error substantially for both game lines and for the number of points, rebounds, and assists a NBA basketball player would score in a game.

Mathematical Biology

Title: Parameter Estimation of Cancer Cell Dynamics
Student: Lynne Moore (MA)
Advisors: Andrea Arnold and Sarah Olson
Co-Advisor: Mike Lee (UMass Medical School)
Academic Year: 2019-2020

To screen different drugs as potential cancer therapies, high throughput experiments have been designed to study the dynamics of cell growth and death. Due to experimental costs and limitations, the cell data can only be recorded at a minimal number of time points. In this project, through parameter estimation techniques, we studied the optimal timing of data in order understand time points most relevant to capture cell dynamics.

Probability and Statistics

Title: Incorporation of PPI in LRT
Student: Dongni Zhang (MA)
Advisor: Zheyang Wu
Academic Year: 2014-2015

Statistical association studies have contributed significantly in the detection of novel genetic factors associated with complex diseases. Incorporation of biological information that reflects the complex mechanism of disease development is likely to increase the power of association tests. In this study, we develop a statistical framework for association studies that integrates the information of the functional effect of SNPs to the disease related protein-protein interactions. Based on both real and simulated phenotypes of hypertension, the method is compared with multiple well-known association tests for sequencing data.

View additional MQP’s in our project database.