Ty Lazarchik Email & Phone Number

Portland, Oregon Area

During this internship, I investigated public transit usage and Uber and Lyft usage in the Portland area. I began by collaborating with colleagues in the Urban Studies and Planning department to evaluate the differences in real-world Uber and Lyft trips and web-scraped public transit trips with the same origin and destination to find areas of the city in which public transit is competitive with Uber and Lyft. Data of actual Uber and Lyft trips in Portland were collected, cleaned, and analyzed using R and various methods of data visualization. The resulting report on this research was chosen to be presented at the Transportation and Research Board Annual Meeting in Washington, D.C. Based on the conclusions of this research, I designed and implemented an agent-based model of traffic and transportation decisions in Portland using Python. With this model, I evaluated the effects of adding a tax to Uber and Lyft trips close to Portland's city center and how varying levels of that tax may affect transit usage.

Sarasota, Florida Area

During the fall semester of 2018, I acted as a one-on-one tutor with a student in New College's Introduction to Python class. During the semester, we met approximately 2-3 times a week to review class materials and complete homework assignments. Topics covered include data types, method-based and object-oriented programming, procedural thinking, and debugging. In these tutoring sessions, I assisted the student in understanding and completing 7 overdue homework assignments. Exam scores for this student steadily increased during the course of the semester, resulting in a final exam score over 20% higher than their midterm score.

Sarasota, Florida Area

During this research internship, I worked with a professor at New College of Florida to evaluate consumer privacy and product personalization preferences and their effects on company success and consumer purchase behavior. This research began with a review of recent academic articles to formulate a research question, after which we conducted a more focused literature review to understand privacy preferences. Based on this research, I designed and implemented an agent-based simulation in Java that simulated consumers with various levels of privacy concern and their purchase decisions between companies of different privacy and product personalization levels. Simulations were then run to understand which companies thrived when the consumer base had different aggregate privacy concern levels. This research gave us insight on how consumers may interact with organizations based on privacy concerns and suggested that an equilibrium of personalization using secured data may maximize company profits and consumer happiness.