Project Title: VA Clinic Process Improvement
Department: Industrial and Systems Engineering
Faculty Mentor: Xiang Zhong, xiang.zhong@ise.ufl.edu
Ph.D. Student Mentor(s): N/A
Terms Available: Fall, Spring
Student Level: Junior, Senior, 1 student per semester.
Prerequisites: COP 2271 Computer Programming For Engineers, STA 4322 Introduction to Statistics Theory
Credit: 0-3 credits via EGN 4912
Stipend: none unless selected for University Scholars
Application Requirements: Basic online application, resume, UF unofficial transcripts, email one pdf file of requirements to xiang.zhong@ise.ufl.edu to request an interview
Application Deadline: N/A
Website: N/A
Project Description: The objectives of this project were to model patient flow in Gainesville VA Medical Clinics, evaluate resource utilization rate and waiting times, identify major areas of inefficiency within the VA clinic system, and improve patient flow and the scheduling of resources. A simulation model will be developed, which is capable of transforming inputs into objective data-driven outputs. These objective data-driven outputs can include queue length, clinic utilization rate, and provider utilization rate. Policy makers could use the data to recognize areas of inefficiency within the clinic and exploit those areas with new policies.
Project Title: Nonprofit Decision Analytics
Department: Industrial and Systems Engineering
Faculty Mentor: Aleksandr Kazachkov, akazachkov@ufl.edu
Ph.D. Student Mentor(s):
Terms Available: Fall, Spring, Summer
Student Level: Freshman, Sophomore, Junior, or Senior; 2 Students per Term
Prerequisites: Understanding of analysis of algorithms, experience with linear or integer optimization preferred but not necessary. Python or Julia programming experience helpful.
Credit: 0-3 credits via EGN 4912
Stipend: none unless selected for University Scholars
Application Requirements: Resume, UF Unofficial Transcripts, and Faculty Interview; To request an interview, email one pdf file with all application requirements to akazachkov@ufl.edu.
Application Deadline: March 1 for the summer term, July 15 for fall term, and November 1 for spring term
Website: akazachk.github.io
Project Description: There is an opportunity to partner with a local Gainesville nonprofit to explore improvements to their operational efficiency and/or analyze the fairness of their current allocation or logistics strategies. This would begin with a data-gathering phase and a cost-benefit of analysis of technological interventions compared to the organization’s existing approach. A key focus of this work is to investigate if there exist better policies to improve allocations over time. Another avenue is exploring the nonprofit’s responses when facing disaster scenarios.
Project Title: Inclusive Design of Automated Vehicles for Individuals with Mild Cognitive Impairments
Department: Industrial and Systems Engineering
Faculty Mentor: Wayne Giang, wayne.giang@ise.ufl.edu
Ph.D. Student Mentor(s): Mahtab Eskandar, m.eskandar@ufl.edu
Terms Available: Fall, Spring, Summer
Student Level: Freshman, Sophomore, Junior, or Senior; 2 Students per Term
Prerequisites: none, tasks may differ based on previous experience and courses taken
Credit: 0-3 credits via EGN 4912
Stipend: none unless selected for University Scholars
Application Requirements: Basic online application, resume, UF Unofficial Transcripts, and Faculty Interview; To request an interview, email one pdf file with all application to Dr. Wayne Giang (wayne.giang@ise.ufl.edu) with an email title “Inclusive AV Design Undergraduate Research Application”
Application Deadline: Rolling
Website: N/A
Project Description: Automated vehicles have the potential to be a great resource to improve independence and quality of life for individuals with mild cognitive impairment (MCI) who may no longer be able to drive themselves anymore or feel unsafe doing so. However, current automated vehicles may be especially difficult for those with MCI to use due to how they change the driving task and the complexities of the systems. In this project, you will be assisting with the design and evaluation of an inclusive automated vehicle of the future focused on supporting those with MCI. In this project you will learn skills about human factors engineering, cognitive science, human-computer interaction, and user experience design.
Project Title: Human factors data analysis of Advanced Driver Assistance System usage by individuals with Parkinson’s Disease
Department: Industrial and Systems Engineering
Faculty Mentor: Wayne Giang, wayne.giang@ise.ufl.edu
Ph.D. Student Mentor(s): N/A
Terms Available: Fall, Spring, Summer
Student Level: Sophomore, Junior, or Senior; 2 Students per Term
Prerequisites: programming (any), statistics (good to have)
Credit: 0-3 credits via EGN 4912
Stipend: none unless selected for University Scholars
Application Requirements: Basic online application, resume, UF Unofficial Transcripts, and Faculty Interview; To request an interview, email one pdf file with all application to Dr. Wayne Giang (wayne.giang@ise.ufl.edu) with the title “PD AV Undergrad Student Application”
Application Deadline: Rolling
Website: N/A
Project Description: Advanced Driver Assistance Systems (ADAS), such as adaptive cruise control and lane keeping assist, and In-Vehicle information Systems (IVIS), such as blind spot monitors, are new safety features that has potential benefits for driver safety, particularly for older adults or individuals with medical conditions such as Parkinson’s Disease (PD). In this project, we are evaluating whether individuals with PD have improved driving performance when using ADAS and IVIS technologies in an on-road study. A variety of data (video, vehicle telemetry, experimenter notes) are collected as part of this study. We are looking for students who are interested in helping with the organization, data cleaning, and data analysis of this data to answer human factors questions about ADAS and IVIS usage (i.e., how do individuals with PD benefit from these technologies, how well do individuals with PD understand these systems, and what can we change about the human-machine interface to improve the usability of these systems?). You will learn skills in human factors engineering, statistics, R, Python, computer vision and machine learning as part of this project.