UROP Project

Art Therapy and injury rehabilitation: a systematic review and meta-analysis

rehabilitation; art therapy; injury; systematic review
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Research Mentor: Dr. Malhotra Bani Malhotra, she/her
Department, College, Affiliation: Department of Art Education, Fine Arts
Contact Email: bmalhotra@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Art therapy, Psychology, Medicine, Neurology, Social Sciences, Health Sciences, Nursing, Rehabilitation
Project Location: Can be done remotely
Research Assistant Transportation Required: No, the project is remote
Remote or In-person: Fully Remote
Approximate Weekly Hours: 7-10 hours, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
  • Day: Monday, August 31
    Start Time: 12:00
    End Time: 12:30
    Zoom Link: https://fsu.zoom.us/j/94074789630
  • Day: Wednesday, September 2
    Start Time: 4:30
    End Time: 5:00
    Zoom Link: https://fsu.zoom.us/j/99231705220
  • Day: Thursday, September 3
    Start Time: 4:00
    End Time: 4:30
    Zoom Link: https://fsu.zoom.us/j/93079542589

Project Description

Physical injuries affect individuals across the lifespan, with long-term medical, social, and psychosocial repercussions. Injuries such as brain injury, spinal cord injury, burns, and physical injuries are a leading public health concern and a cause for significant disability needing multidisciplinary rehabilitation. Art therapy as a non-pharmacological intervention has been used to facilitate rehabilitation goals within integrative medical care setting that addresses psychosocial needs for military members with TBI, burn patients, and other trauma and rehabilitation units in both adult and pediatric settings. The objective of this review is to identify art therapy studies addressing injury rehabilitation outcomes for individuals with injuries, identifying characteristics, and the outcomes that are examined to guide future research in art therapy, and identify gaps in current knowledge using systematic review and meta-analaysis.

Research Tasks: - Literature review
- Data screening
- Data extraction using training on data evidence synthesis software (Covidence)
-Rating study quality
-Preparing data set for metanalysis and narrative synthesis
- Data analysis (for qualitative and quantitative synthesis of secondary sources) and data visualizations
-Manuscript writing
-Reference citation and management

Skills that research assistant(s) may need: Required:
-Openness to learning
-Attention to detail: Essential for screening and data extraction accuracy
-Ability to follow protocols: Adhering closely to predefined review procedures
-Time management: Meeting deadlines for screening or extraction tasks
-Responsiveness to feedback- Willingness to revise decisions based on calibration exercises
-Clear communication: Reporting uncertainties or discrepancies to supervisors
-Basic academic reading comprehension for research articles: Ability to identify research purpose, sample, methods, and outcomes
-Data organization: Use of Excel/Google Sheets for tracking articles and data extraction
-Applying structured criteria: Ability to follow inclusion/exclusion guidelines consistently

Recommended:
-Understanding of research methods: Basic knowledge of study designs (RCTs, qualitative, observational)
-Introductory statistics knowledge: Familiarity with means, standard deviations, etc
-Data extraction experience: Prior exposure to structured coding or dataset creation
-Reliability in team-based work: Consistency when working alongside other coders
- Intellectual curiosity: Interest in engaging deeply with research questions, especially interdisciplinary work

Mentoring Philosophy

My mentoring philosophy is grounded in growth-oriented, collaborative, and reflective framework. I view mentoring as a relational process that values curiosity, and critical thinking, and development of professional identity. I strive to create a supportive space where mentees feel empowered to ask questions, engage in dialogue, and take initiatives.
I prioritize providing timely, constructive, and strength-based feedback while also encouraging mentees to engage in self-reflection and deepening skills with practice. Drawing from my experiences as a clinician-researcher, I support mentees in navigating tasks and learning through interdisciplinary contexts with humility, rigor, and self-awareness. While curiosity and inquiry as central to scientific growth, I recognize that each mentee’s engagement is shaped by their lived experiences grounded in their intersecting identities. I believe these perspectives meaningfully inform the questions mentees ask (and don’t ask), the ideas they engage with, and the ways they approach research work. For instance, a decade ago, I would not have imagined that I could write scholarly articles that would contribute meaningfully to the scientific community. Over time, however, I learned that writing is simply not an innate skill, but a practice that develops through persistence, mentorship, feedback, and continued engagement with ideas. This experience shapes how I mentor students and trainees: I aim to demystify research tasks and scholarly writing.
As a mentor, I aim to honor varied experiences and ultimately, my goal is to instill confidence, foster openness to learning, while working collaboratively with mentees in advancing their growth.

Additional Information


Link to Publications

https://arted.fsu.edu/people/bani-malhotra/ ; https://scholar.google.com/citations?user=Lwd4R8wAAAAJ&hl=en&oi=ao; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261445670
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Integrate-and-Fire Sampling Meets Optimal Transport

Integration, Sampling, Optimal Transport, Signal Encoding and Reconstruction
20240610_Mathematics_Rocio-Díaz-Martín_Headshot-1-3X4.jpg
Research Mentor: Rocio Diaz Martin,
Department, College, Affiliation: Department of Mathematics, Arts and Sciences
Contact Email: rdiazmartin@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 1
Relevant Majors: mathematics, computer science, physics, engineering
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

This project studies the relationship between the classical integrate-and-fire sampling scheme and one-dimensional optimal transport.

The integrate-and-fire sampling scheme is a method for recording a signal by tracking when its accumulated value reaches a fixed threshold. Instead of measuring the values of the signal at fixed times, this sampler starts a counter at zero and continuously integrates, or accumulates, the signal until the total mass reaches a prescribed threshold. It then records that time stamp as a spike and resets the counter to zero. This process is repeated to obtain subsequent time stamps.

At its core, this scheme is closely related to optimal transport, since both ideas involve moving from one representation of a signal to another in terms of accumulated mass. The integrate-and-fire sampler records the times at which equal amounts of the signal have been accumulated. In one dimension, optimal transport similarly describes how to move uniformly distributed mass into the shape of a given signal. Therefore, the firing times can be understood as samples of the map that transports uniform mass to the signal.

Although this connection is hinted in the literature, it has not been explicitly stated. The first goal of this project is to make this connection precise. We will then investigate and implement the reconstruction of signals from spike times by interpolating the transport map, and compare density-based error with Wasserstein reconstruction error, that is, the error measured using a distance from optimal transport. The goal is to determine whether optimal transport geometry provides a natural and stable framework for analyzing integrate-and-fire sampling.


Research Tasks: - Literature review.
- Developing and implementing algorithms through programming.
- Formulate and rigorously prove mathematically grounded results.

Skills that research assistant(s) may need: Foundation in Calculus (required) and linear algebra (recommended).
Basic programming skills, preferably in Python (recommended).
Some knowledge of probability theory is preferred (recommended).

Mentoring Philosophy

I consider students as junior colleagues, empowering them to grow as collaborators rather than passive learners. I begin by sharing the theoretical foundations (especially intuitive and proof-based thinking) while suggesting programming as a tool for exploration.

As a young researcher, I recognize that mentoring presents a challenge for me, but it is one that excites me deeply. I view this process as a two-way learning experience: while students grow as mathematicians and programmers, I grow as a mentor and teacher. Coming from Argentina, I also bring a perspective shaped by my own educational journey, which helps me relate to students navigating diverse paths and backgrounds.

I set clear, mutual goals and maintain open, respectful communication. By inviting students to co-create the learning path, align expectations and build trust. My mentorship is inclusive: I’m attentive to different learning styles and backgrounds, ensuring all students feel valued and encouraged to share their ideas. Being approachable is essential, and I actively cultivate this quality in myself to create a welcoming and supportive environment for everybody.

I guide them to develop mathematical intuition by asking guiding questions and encouraging reflection, helping them formulate logical results and rigorous proofs in their own words. We periodically assess progress, celebrate small victories, and iterate our process to strengthen understanding and confidence.

Ultimately, I aspire for students to become confident, independent thinkers: capable programmers, insightful mathematicians, and critical collaborators who continue learning beyond our time together.

Additional Information

The goal of this project is to connect the nonlinear sampling scheme of integrate-and-fire with tools and concepts from Optimal Transport theory. Optimal Transport is a central area of my current research, and as a new Assistant Professor in the Department of Mathematics, I am excited to continue studying it, connect it with different areas of mathematics, and share my background with undergraduate students. We will begin by exploring both the fundamental theory and applications of Optimal Transport. For example, the Wasserstein metric, also known as the Earth Mover's Distance, is a key object in Optimal Transport and is widely used in Machine Learning, inspiring developments such as Wasserstein Generative Adversarial Networks, introduced by M. Arjovsky and collaborators in 2017. This theoretical study will also provide an opportunity to explore other important concepts in applied mathematics.

We will then focus on making a clear connection with the integrate-and-fire technique. Classical integrate-and-fire sampling is a nonlinear sampling mechanism in which a nonnegative signal is integrated until the accumulated mass reaches a prescribed threshold. At that moment, a spike is recorded and the integrator is reset. We will review the literature on this procedure, including the recent work “Model Agnostic Signal Encoding by Leaky Integrate-and-Fire: Performance and Uncertainty” by Diana Carbajal and José Luis Romero.

The guiding questions for the project are: Can the integrate-and-fire scheme be characterized exactly as sampling a specific optimal transport map? Can one reconstruct a signal, or at least its induced probability measure, from spike times? Can the stability of spike times be described using Wasserstein distances? Can optimal transport geometry provide improved reconstruction guarantees from spike data?

The resulting algorithms will preferably be implemented in Python.

We will work in-person during the Fall semester 2026 and remotely during the Spring semester 2027.

Link to Publications

https://rociodm.github.io/
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Learning the Rules, Not Just the Data: Property-Preserving Neural Networks for Learning Mathematical Functions and Operators

Neural Networks, operator learning, function approximation
20240610_Mathematics_Rocio-Díaz-Martín_Headshot-1-3X4.jpg
Research Mentor: Rocio Diaz Martin,
Department, College, Affiliation: Department of Mathematics, Arts and Sciences
Contact Email: rdiazmartin@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 1
Relevant Majors: mathematics, computer science, physics, engineering
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

Many mathematical functions and operators are characterized not only by their input-output behavior, but also by structural properties such as linearity, multiplicativity, symmetry, equivariance, differential identities, or commutation relations. As a simple example, the exponential function satisfies exp(x+y)=exp(x)exp(y). Moreover, this property, together with the condition exp(0)=1, essentially characterizes the exponential function. Similarly, sine and cosine satisfy trigonometric identities, while operators between function spaces, such as the Fourier transform, are linear and interact in useful ways with translations and convolutions.

Standard neural networks are universal approximators and can often learn accurate pointwise approximations from data. However, when trained only through pointwise error, they may fail to preserve the mathematical identities that characterize the object being learned, especially outside the training range.

This project is motivated by the following broad question: Can neural networks be trained or designed to behave like known mathematical functions and operators by enforcing their structural properties?

The project will investigate neural networks that approximate known mathematical functions, viewed as maps between points, and operators, viewed as maps whose inputs and outputs are functions, while also respecting their defining structure. We will compare standard neural networks trained only to minimize approximation error with structure-preserving models in which a specific identity or property is enforced either through the loss function or through the architecture.

The exponential function will serve as a first model problem. We will compare a standard neural network approximation of exp(x) with models that enforce its homomorphism property, namely that addition in the input corresponds to multiplication in the output. The project may then extend to other mathematical functions and simple operators, where one can enforce properties such as linearity, equivariance, or commutation relations.

The main goal is to understand whether incorporating mathematical structure improves generalization, extrapolation, and interpretability. The project combines numerical experiments, neural network implementation, and mathematical analysis, and is suitable for a student interested in applied mathematics, machine learning, harmonic analysis, or scientific computing.


Research Tasks: - Literature review on physics-informed neural networks, structure-preserving learning, and operator learning.
- Mathematical background review to identify the structural identities and properties that characterize functions and operators.
- Dataset generation: creation of synthetic training and testing datasets for simple functions and operators, including interpolation and extrapolation regimes.
- Development and implementation of algorithms through programming, including exploratory AI-assisted coding.
- Visualization and interpretation of results, including plots of learned functions, error curves, training behavior, and failure cases.
- Documentation and presentation of the methodology, experiments, and conclusions

Skills that research assistant(s) may need: Foundation in Calculus (required) and linear algebra (recommended).
Basic programming skills, preferably in Python (recommended).

Mentoring Philosophy

I consider students as junior colleagues, empowering them to grow as collaborators rather than passive learners. I begin by sharing the theoretical foundations (especially intuitive and proof-based thinking) while using programming as a tool for exploration.

As a young researcher, I recognize that mentoring presents a challenge for me, but it is one that excites me deeply. I view this process as a two-way learning experience: while students grow as mathematicians and programmers, I grow as a mentor and teacher. Coming from Argentina, I also bring a perspective shaped by my own educational journey, which helps me relate to students navigating diverse paths and backgrounds.

I set clear, mutual goals and maintain open, respectful communication. By inviting students to co-create the learning path, align expectations and build trust. My mentorship is inclusive: I’m attentive to different learning styles and backgrounds, ensuring all students feel valued and encouraged to share their ideas. Being approachable is essential, and I actively cultivate this quality in myself to create a welcoming and supportive environment for everybody.

I guide them to develop mathematical intuition by asking guiding questions and encouraging reflection, helping them formulate logical results and rigorous proofs in their own words. We periodically assess progress, celebrate small victories, and iterate our process to strengthen understanding and confidence.

Ultimately, I aspire for students to become confident, independent thinkers: capable programmers, insightful mathematicians, and critical collaborators who continue learning beyond our time together.

Additional Information


Link to Publications

https://rociodm.github.io/
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Measuring Intellectual Humility in Written Responses

Social Psychology, Perception, Humility, Leadership, Text Analysis
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Research Mentor: Dr. Irmak Olcaysoy Okten, She/her
Department, College, Affiliation: Florida State University, Arts and Sciences
Contact Email: okten@psy.fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Psychology, pre-med, sociology, anthropology. education, communication preferred. All social science majors will be considered.
Project Location: On FSU Main Campus
Research Assistant Transportation Required: Yes
Remote or In-person: Partially Remote
Approximate Weekly Hours: 8-10 hours, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
  • Day: Monday, August 31
    Start Time: 12:00
    End Time: 12:30
    Zoom Link: Meeting completed. See below for the link to the recording.

Project Description

Successful leadership in today’s professional world increasingly depends on intellectual humility (IH). Individuals high in IH recognize the limits of their knowledge, avoid both overconfidence and underconfidence, and remain open to learning from others (Leary et al., 2017). Despite its many documented benefits, however, IH may be undervalued in leadership contexts. Important questions, therefore, remain about why intellectually humble leaders are not always viewed positively and how IH can be fostered among current and emerging leaders.

The goal of this project is to examine how people perceive intellectual humility in leadership and to identify factors that shape these perceptions. As part of a larger longitudinal research program, we will investigate how individuals with current or prior leadership experience express intellectual humility, particularly when responding to social conflict and disagreement. Research assistants recruited through the UROP program will contribute to the development of a coding system for assessing intellectual humility in written responses and will gain experience in content analysis, psychological research methods, and data management.

Research Tasks: This project will involve conducting a literature review of prior research on intellectual humility and contributing to the development of a coding scheme for the thematic analysis of intellectual humility expressed in written responses. Students will have the opportunity to review and analyze existing research data, participate in the coding process, and gain experience with content analysis.

Skills that research assistant(s) may need: Research assistants will work as a team, so strong collaboration skills are required for this position. Previous experience with scientific literature review and APA- style writing is recommended.

Mentoring Philosophy

In the Motivated Social Cognition Lab within the Department of Psychology, students work collaboratively as part of a research team alongside fellow students and me. My goal is to foster students’ enthusiasm for scientific research through a mentoring approach that is both structured and flexible. I strive to create a supportive, respectful, and productive environment where students feel comfortable asking questions, sharing ideas, learning from mistakes, and developing their skills. Through regular lab meetings and hands-on involvement in research projects, students receive training in core research skills, including study design, data collection, data analysis, and scientific communication. As students build their methodological and theoretical knowledge, I encourage increasing independence, with the ultimate goal of helping them become confident and capable researchers.

Additional Information

Link to the UROP Roundtable meeting recording is here: https://fsu.zoom.us/rec/play/_Usj5CurmutG0U8SqPSvMWT6PN57mNNNjy8ZVssEbTgKSxBH5nD46JdjEbIyJdAuCfPWxF3aXsBPIVFk.eoUFNgLO4ZfugELJ?accessLevel=meeting&canPlayFromShare=true&from=share_recording_detail&continueMode=true&oldStyle=true&componentName=rec-play&ori

Link to Publications

https://www.motivatedsocialcognition.com/publications
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Dioxane Contamination and Transport

Environment, Permeation, 1,4-Dioxane, Measurement, Pollution
Research Mentor: Dr. Youneng Tang, He/him
Department, College, Affiliation: Civil and Environmental Engineering, FAMU-FSU College of Engineering
Contact Email: ytang@eng.famu.fsu.edu
Research Assistant Supervisor (if different from mentor): Mr. Dennis Ssekimpi He/him
Research Assistant Supervisor Email: dssekimpi@fsu.edu
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 1
Relevant Majors: Civil Engineering, Environmental Engineering
Project Location: FAMU-FSU College of Engineering
Research Assistant Transportation Required: Yes
Remote or In-person: In-person
Approximate Weekly Hours: 6, During business hours
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

1,4-Dioxane is a likely carcinogen and common groundwater contaminant due to its wide presence in industrial and home products. Many products are disposed of in landfills. To prevent solid waste in landfills contaminating groundwater, landfills are usually contained by bottom liners. The objective of this project is to determine the 1,4-dioxane transport characteristics through the bottom liners such as the breakthrough time. We welcome one UROP scholar to participate in this research project. Professor Tang and his graduate student will supervise the UROP scholar. The graduate student will provide most of the direct supervision.

Research Tasks: The UROP scholar is expected to:
1) Complete a few lab safety training sessions.
2) Shadow the graduate student.
3) Review the literature to understand 1,4-dioxane and their contamination.
4) Learn the method based on gas chromatography-mass spectrometry for measuring 1,4-dioxane.
5) Use the measurement method to determine the 1,4-dioxane transport characteristics in diffusion cells.
6) Summarize and discuss the research results in a poster and/or a white paper.

Skills that research assistant(s) may need: Required: MS WORD

Mentoring Philosophy

I believe that that quality of research products is more important than the quantity of research products. I also believe that interest in research is the primary motivation.

Additional Information


Link to Publications

https://eng.famu.fsu.edu/cee/people/tang
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Exploring Women Characters in Bollywood Films

Film Studies, Bollywood, Media Studies, Gender Studies, South Asia
Research Mentor: Dr. Dr. Rebecca Peters, she/her
Department, College, Affiliation: Religion, Arts and Sciences
Contact Email: rlp08c@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 6
Relevant Majors: Open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5-10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
  • Day: Tuesday, September 1
    Start Time: 4:30
    End Time: 5:00
    Zoom Link: https://fsu.zoom.us/j/91524726261
  • Day: Wednesday, September 2
    Start Time: 4:30
    End Time: 5:00
    Zoom Link: https://fsu.zoom.us/j/91575128544
  • Day: Friday, September 4
    Start Time: 12:00
    End Time: 12:30
    Zoom Link: https://fsu.zoom.us/j/98061402050

Project Description

This project looks to quantify whether women directors impact the representation of women characters in their films. We are doing so by analyzing Bollywood films directed by women. To determine the number and quality of female characters, we will utilize what is called "film coding." "Film coding" is done through through close watching and marking every time we see certain things. Basically, we're looking to see what real impact having a woman in the role of director has on a film.

Research Tasks: The student research assistant will watch subtitled Bollywood films (no language proficiency or other experience in or knowledge of India is necessary), and complete surveys on their computers about each woman character onscreen. The student will receive training and will have the support of the mentor for any questions or points of clarification. The process allows for a flexibility of when it can be completed within the week, for students who have more unusual schedules. NO prior film knowledge and no coding knowledge is necessary.

Skills that research assistant(s) may need: Required: None
Recommended: Access to Netflix and Prime; An understanding of Excel; Understanding of Google drive folders and files organization

Mentoring Philosophy

As a mentor, I view myself as a facilitator, an individual trained in specific fields of study that enables me to create the necessary environment and conditions where mentees can learn and grow. I view the mentee as someone whose aim is to increase their understanding of the world and to prioritize learning over finishing. Further, I accept that experience is one of the best and most lasting conduits for learning. In the mentor/mentee exchange, I commit to providing clear and straightforward expectations, to being available for any and all questions or concerns, and to creating work and environment productive to learning. I expect mentees to approach projects with open minds and inquisitive natures; it is never inappropriate to ask for clarification or repetition of expectations.

Additional Information

Please watch the zoom recording below to learn more about the project.
https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Ffsu.zoom.us%2Frec%2Fshare%2FIv-vhVzYViEUbPPPFmrt8Vd3moYGZmyS4p_P4dtpCHw5iRLRiQCw-Gs9ECvPvSiL.BlqN_KnLjfHZoriK&data=05%7C02%7Crlp08c%40fsu.edu%7Cc5e262e660d345cd988108df086e44ed%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639238942056692323%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=jAx4D1mXpiVzMFLOOCZ6ghbXxfzKifv8JsdIzNvepo4%3D&reserved=0

Link to Publications


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Developing AI Literacy Through Prompt Engineering in High School Computer Science Education

Prompt Engineering, Digital Competencies, K-12 Education, Secondary School Computer Science, Advanced Coding Class, High School Computing, Laboratory School Context, Generative AI (GenAI), Custom AI Agents, Gemini Gems, AI Coding Assistant
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Research Mentor: Damilare Ajayi, he/him/his
Department, College, Affiliation: Educational Psychology & Learning Systems, Education, Health, and Human Sciences
Contact Email: dfa24@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Artificial Intelligence
Education
Computer Science

Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Fully Remote
Approximate Weekly Hours: 3-5 hours, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
  • Day: Thursday, September 3
    Start Time: 1:00
    End Time: 1:30
    Zoom Link: https://fsu.zoom.us/my/damilare?omn=93417216980

Project Description

This qualitative case study, situated within a Research-Practice Partnership (RPP) at Florida State University Schools (FSUS), investigates the development of AI literacy and prompt engineering skills among high school Computer Science students (specifically in advanced Level 4 Game & Sim classes). As students engage in project-based learning to build games using C# and the Unity engine, they collaborate with Generative AI (GenAI) technologies, utilizing general AI coding assistants (such as ChatGPT and Gemini) as well as customized AI Agents/Gems. Grounded in the AI literacy framework (Long & Magerko, 2020), this research explores how structured, teacher-led prompt engineering instruction, iterative student-AI communication, and systematic prompt logging influence students' computational thinking, critical code evaluation strategies, and overall development of AI literacy.

Research Tasks: Literature review
Data analysis

Skills that research assistant(s) may need: Curiosity
Data Cleaning
Basic Ai experience
reliability and professionalism

Mentoring Philosophy

My approach to mentoring is grounded in one simple belief: people grow best when they feel seen, trusted, and challenged in equal measure. I lead by doing. When I work with mentees, I am not standing at a distance giving instructions, I work alongside with them, modeling the curiosity, rigor, and adaptability I hope they develop.
I start by getting to know each mentee as a person. Their goals, their strengths, what excites them, what holds them back. From there, I try to build an environment where they feel safe to take risks, ask questions, and yes, make mistakes, because mistakes are where the real learning happens. I do not micromanage. I set clear expectations, then step back and let mentees own their work. Ownership is how confidence gets built.
Kindness is not separate from professionalism in my mentoring relationships — it is the foundation of them. I believe mentees do their best thinking when they feel respected, not evaluated. So I hold high standards while also being flexible enough to meet people where they are. Every mentee comes with a different set of experiences, and my job is to recognize what makes each person uniquely capable and help them build on it.
Ultimately, I want mentees to leave our work together not just with stronger research skills, but with a clearer sense of who they are as thinkers and contributors.

Additional Information


Link to Publications

https://dfajayi.com/research
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Explainable Artificial Intelligence for Traffic Volume Prediction

Artificial Intelligence, Transportation, Machine Learning, Traffic Modelling
IMG_2798 (1).png
Research Mentor: George Amu,
Department, College, Affiliation: Industrial and Manufacturing Engineering, FAMU-FSU College of Engineering
Contact Email: gka24a@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators: Arda Vanli Dr.
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 1
Relevant Majors: Open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: In-person
Approximate Weekly Hours: 10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
  • Day: Tuesday, September 1
    Start Time: 4:30
    End Time: 5:00
    Zoom Link: https://drive.google.com/file/d/1T-feehnehr-3FOY5bEJ_yrMtqseVlxY1/view?usp=drive_link
  • Day: Wednesday, September 2
    Start Time: 4:30
    End Time: 5:00
    Zoom Link: https://drive.google.com/file/d/1T-feehnehr-3FOY5bEJ_yrMtqseVlxY1/view?usp=drive_link
  • Day: Thursday, September 3
    Start Time: 4:30
    End Time: 5:00
    Zoom Link: https://drive.google.com/file/d/1T-feehnehr-3FOY5bEJ_yrMtqseVlxY1/view?usp=drive_link

Project Description

Background
State departments and agencies monitor traffic volumes to manage and maintain efficient highway systems. Traffic data collected are used for various applications including risk assessments to identify high crash locations as well as resource allocation, maintenance planning, and policy formulation.
Research Problem
Traffic data collection requires significant time and effort. Current manual methods used for data collection are insufficient and do not cover all road segments. Currently, in Florida, the department of transport uses manual ground-based methods to count traffic on some major roadways. There is still a substantial gap in data collection in terms of coverage. Artificial intelligence (AI) and statistical computing offer significant potential in providing robust and cost-effective traffic volume data for these unmonitored road segments.
Research Objectives

This research will focus on:
1. Reviewing existing literature and identifying best practices for traffic volume prediction.
2. Building predictive artificial intelligence models for traffic volume prediction on unmonitored roadways in Florida.

Methodology
The project will focus on traffic volume prediction for selected districts in Florida. The proposed methodology will include traffic demand modelling and machine learning. The designed model will be evaluated based on defined metrices to ascertain its robustness.
Expected Outcomes
This research is expected to result in defined tested strategies and recommendations for predicting traffic volumes using artificial intelligence.


Research Tasks: 1. Reviewing existing literature and identifying best practices for traffic volume prediction.
2. Building predictive artificial intelligence models for traffic volume prediction on unmonitored roadways in Florida.
Methodology

Skills that research assistant(s) may need: Data analytics - recommended

Mentoring Philosophy

Developing a relationship founded on mutual respect
Giving mentees’ ownership of their work and promoting accountability
Sharing your own experience.
Creating a safe environment in which mentees feel that is acceptable to fail and learn from their mistakes

Additional Information


Link to Publications

Project Overview Video : https://drive.google.com/file/d/1T-feehnehr-3FOY5bEJ_yrMtqseVlxY1/view?usp=sharing
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Comparing Life Satisfaction of Second-Generation Mexican Young Adults in University Versus the Workforce

Life Satisfaction; Mexican; Young Adults
Research Mentor: Mikayla Heath,
Department, College, Affiliation: Human Development and Family Sciences, Education, Health, and Human Sciences
Contact Email: mah19b@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

I will build on my previous research that explored how Mexican immigrant families navigate emotional, cultural, and educational dynamics related to intergenerational expectations and identity. Using dyadic interviews, the study offered a relational analysis of how themes such as cultural identity, emotional interdependence, parenting practices, and career decision-making are experienced and interpreted across generations. One of the central aims of this study was to better understand the pressures and motivations experienced by Latino emerging adults as they make decisions about their future. Participants frequently described navigating implicit parental expectations, particularly around education and career success, while also expressing deep emotional loyalty and gratitude. In several cases, children referenced the sacrifices their parents made to immigrate, framing their own success to honor those efforts. Parents, meanwhile, expressed hopes for their children’s happiness and success, often using themselves as moral or motivational examples. However, a disconnect sometimes emerged between the messages children heard and the intentions parents expressed, reflecting broader themes of ambivalence within immigrant parent-child relationships
Given these limitations, there is a clear need for studies that directly compare life satisfaction across different post–high school pathways among second-generation Mexican adults. Understanding these differences can help clarify whether university environments or early workforce entry provide advantages or challenges for this population’s well-being. The primary objective of this researh h is to understand and clarify whether university environments or early workforce entry provide advantages or challenges for the well-being of second-generation Mexican adults. The secondary objective is to understand the differences in career and personal motivations that led to the two-different life paths identified above for this population.

Research Tasks: -Literature review
-Participant Recruitment
-Data Analysis


Skills that research assistant(s) may need: Required:
-Pays attention to detail
-Willing to learn
-Dedicated to improving their skills
-Strong writing skills
-Ability to manage time effectively

Mentoring Philosophy

I have been very fortunate to work with amazing mentors during my career. Without their support and willingness to teach me, I would not be where I am today in my academic and professional career. I believe that every student, regardless of how much experience they start with, deserves an opportunity to be taught. I work with my students in collaboration, recognizing that my teaching is an investment in the broader research space. The relationships I aim to foster are professional, motivating, and encourage growth.

Additional Information


Link to Publications


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Media Analysis of Twitter’s 2017 Hateful Conduct Policy

Twitter, cyberhate, qualitative research, content analysis
Akuo.jpg
Research Mentor: Mr. Andrew Kuo, He/him
Department, College, Affiliation: Criminology and Criminal Justice, Criminology and Criminal Justice
Contact Email: ak25b@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Criminal justice, sociology, psychology, media and communication, open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: In-person
Approximate Weekly Hours: 10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
  • Day: Monday, August 31
    Start Time: 3:00
    End Time: 3:30
    Zoom Link: https://fsu.zoom.us/j/9690061830
  • Day: Tuesday, September 1
    Start Time: 3:00
    End Time: 3:30
    Zoom Link: https://fsu.zoom.us/j/9690061830

Project Description

Early Twitter policies did not specifically ban harassment or hate speech, but instead focused on user spam and abuse. This omission stood until 2017, when Twitter added an explicit policy on hateful conduct. The current project seeks to understand the Twitter policy change of 2017 through media analysis, examining media coverage of hateful conduct on Twitter before and after the 2017 policy change. This research aims to answer (1) which major events related to hateful conduct on Twitter may have resulted in the 2017 policy change? And (2) did the 2017 policy change have an impact on hateful conduct on Twitter?

Research Tasks: Literature search, literature review, data collection, news article search, content analysis

Skills that research assistant(s) may need: Attention to detail, communication skills, follow instructions, time management, familiarity with Microsoft Office or Google products

Mentoring Philosophy

I think the core of a good mentor-mentee relationship is established through mutual understanding, so I will do my best to explain my expectations and goals for the project, assign clear and precise tasks, and set realistic timelines for deadlines. Moreover, I will establish a healthy and open channel of communication, where students feel comfortable discussing issues and questions so we can address them swiftly. When unexpected things happen and tasks cannot be completed in time, we will discuss in a timely manner and adjust accordingly. Even if this project does not align exactly with your interests, through this work you will gain valuable research skills and experience. For the eventual poster presentation, students will be in the driver’s seat, but I will provide ample support, recommendations, and suggestions for preparing and presenting the poster.

Additional Information


Link to Publications


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