Physical Sciences and Mathematics Commons^™

Cnn-Lstm Vs Ann: Option Pricing Theory, Edward Chang

Undergraduate Student Research Internships Conference

The modern derivatives market has been steadily growing since the development of the first accurate option pricing model by Fischer Black, Robert Merton, and Myron Scholes. Since then, there have been many different approaches to more accurately price options like the binomial option pricing model and approaches using technology such as machine learning. There are many different research papers on option pricing with artificial neural networks (“ANN”) but not many with other neural network types. We contribute to the existing literature by developing a convolutional neural network – long short-term memory (“CNN-LSTM”) model to price options and compare it to …

Reporting Standards For Machine Learning Research In Type 2 Diabetes, Grace Kang

Undergraduate Student Research Internships Conference

In this project, three people scored 90 papers on machine learning predictive models for type 2 diabetes to assess their adherence to TRIPOD, MI-CLAIM, and DOME reporting guidelines.

A Qualitative Look Into Repair Practices, Jumana Labib

Undergraduate Student Research Internships Conference

This research poster is based on a working research paper which moves beyond the traditional scope of repair and examines the Right to Repair movement from a smaller, more personal lens by detailing the 6 categorical impediments as dubbed by Dr. Alissa Centivany (design, law, economic/business strategy, material asymmetry, informational asymmetry, and social impediments) have continuously inhibited repair and affected repair practices, which has consequently had larger implications (environmental, economic, social, etc.) on ourselves, our objects, and our world. The poster builds upon my research from last year (see "The Right to Repair: (Re)building a better future"), this time pulling …

Model Transformations Between Sequence Diagram And Activity Diagram With Qvto, Yutong Xia

Undergraduate Student Research Internships Conference

Complex software systems are specified by various models denoting the behavior of the system components, the exchanges of messages and data among components, the intents of the system stakeholders, the flow of system processes, and the structure of the system as a collection of modules.

When such systems are maintained and evolved (e.g. by adding new functionality, fixing bugs, or porting to a new operating environment), one or more of these models are altered. This brings the system specification to an inconsistent state since some models reflect the new behavior while other models were not appropriately evolved.

This research presents …

Damage Assessment In Aging Structures Using Augmented Reality, Omar Zuhair Awadallah, Ayan Sadhu

Undergraduate Student Research Internships Conference

Structural Health Monitoring (SHM) is the assessment of bridges and observation of data regarding these bridges over time to monitor their evolution and detect the presence of any possible damages. However, existing methods to perform structural inspections in bridges are high in cost, time-consuming and risky. Inspectors use expensive equipment to reach a certain area of the bridge to inspect it, and at different heights, this can pose a risk to the inspector’s safety. This study aims to find cheaper, faster, and safer ways to perform structural inspections using augmented reality and artificial intelligence. The developed system uses a machine …

How Can Social Networks Impact Careers In Game Development?, Tongzhang Wang

Undergraduate Student Research Internships Conference

The game development industry is one characterized by young workers and fast worker turnover. The popularity of using twitter as a professional tool within the video games industry presents a potentially insightful view port into a professional's informal network. Investigating characteristics of the social networks of newly graduated students in the game development industry may reveal what factors contribute to fast worker turnover and how certain cohorts may face additional barriers.

Data Preprocessing For Machine Learning Modules, Rawan El Moghrabi

Undergraduate Student Research Internships Conference

Data preprocessing is an essential step when building machine learning solutions. It significantly impacts the success of machine learning modules and the output of these algorithms. Typically, data preprocessing is made-up of data sanitization, feature engineering, normalization, and transformation. This paper outlines the data preprocessing methodology implemented for a data-driven predictive maintenance solution. The above-mentioned project entails acquiring historical electrical data from industrial assets and creating a health index indicating each asset's remaining useful life. This solution is built using machine learning algorithms and requires several data processing steps to increase the solution's accuracy and efficiency. In this project, the …

A Kuramoto Model Approach To Predicting Chaotic Systems With Echo State Networks, Sophie Wu, Jackson Howe

Undergraduate Student Research Internships Conference

An Echo State Network (ESN) with an activation function based on the Kuramoto model (Kuramoto ESN) is implemented, which can successfully predict the logistic map for a non-trivial number of time steps. The reservoir in the prediction stage exhibits binary dynamics when a good prediction is made, but the oscillators in the reservoir display a larger variability in states as the ESN’s prediction becomes worse. Analytical approaches to quantify how the Kuramoto ESN’s dynamics relate to its prediction are explored, as well as how the dynamics of the Kuramoto ESN relate to another widely studied physical model, the Ising model.

Simulating Salience: Developing A Model Of Choice In The Visual Coordination Game, Adib Sedig

Undergraduate Student Research Internships Conference

This project is primarily inspired by three papers: Colin Camerer and Xiaomin Li’s (2019 working paper)—Using Visual Salience in Empirical Game Theory, Ryan Oprea’s (2020)—What Makes a Rule Complex?, and Caplin et. al.’s (2011)—Search and Satisficing. Over the summer, I worked towards constructing a model of choice for the visual coordination game that can model player behavior more accurately than traditional game theoretic predictions. It attempts to do so by incorporating a degree of bias towards salience into a cellular automaton search algorithm and utilizing it alongside a sequential search mechanism of satisficing. This …