UROP Project | Center for Undergraduate Research and Academic Engagement

Modeling and Simulation of Electric Shipboard Power Systems

Shipboard Power Systems; Modeling and Simulation; Power Electronics; Electric Power Systems; Energy Systems

Research Mentor: Dr., Prof. Ravikumar Gelli, He, His, Him
Department, College, Affiliation: Florida State University, FAMU-FSU College of Engineering
Contact Email: rgelli@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: Electrical Engineering, Computer Engineering. Students interested in electric power, simulation, energy systems, or computational modeling are encouraged to apply.
Project Location: 2000 Levy Ave, Tallahassee, FL 32310. Center for Advanced Power Systems (CAPS)
Research Assistant Transportation Required: FSU Seminole Express provides service between the FSU main campus and the FAMU-FSU College of Engineering.
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5 to 8 hours per week, 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: 12:00
End Time: 1:00
Zoom Link: https://fsu.zoom.us/j/6540925978

Project Description

Modern electric ships depend on integrated electrical power systems to support propulsion, navigation, communication, sensing, computing, and other onboard loads. Unlike conventional terrestrial power systems, shipboard power systems must operate within limited space and generation capacity while responding rapidly to changing electrical demands and operating conditions.

This project will introduce undergraduate researchers to the modeling and simulation of electric shipboard power systems. Students will begin by learning the basic structure and components of shipboard electrical systems, including generators, loads, energy storage, power electronic converters, and distribution networks. They will then develop and study simulation models representing selected portions of an electric shipboard power system.

Students will use simulation tools to investigate how the system responds to changing loads, disturbances, component configurations, and operating conditions. As the project progresses, students may explore topics such as power flow, dynamic response, energy management, power quality, system resilience, or integration of emerging high-power electrical loads.

Prior experience with shipboard power systems or advanced power-system modeling is not required. The project is designed to provide first-year and early undergraduate students with a structured introduction to electric power systems, engineering modeling, and simulation-based research.

Research Tasks: 1) Review introductory materials on electric power systems and shipboard electrical architectures.
2) Identify major shipboard power-system components, including generators, distribution networks, loads, energy storage systems, and power electronic converters.
3) Learn the basic simulation environment and modeling tools used for the project.
4) Develop simplified models of selected shipboard power-system components and subsystems.
5) Simulate different operating conditions, such as changes in electrical load, generation, or system configuration.
6) Analyze voltage, current, power, frequency, and other relevant system variables during different operating scenarios.
7) Document the models, simulation results, and conclusions and prepare a research poster for the Undergraduate Research Symposium.

Skills that research assistant(s) may need: Required:
-- Interest in electrical systems, energy, modeling, or engineering
-- Willingness to learn new simulation and computational tools
-- Basic problem-solving skills
-- Ability to work consistently and communicate progress
-- Curiosity about how complex engineering systems operate

Recommended but not required:
-- Introductory familiarity with circuits, physics, or electrical engineering
-- Basic MATLAB, Python, Simulink, or other programming/simulation experience
-- Basic understanding of voltage, current, and electrical power

Mentoring Philosophy

My mentoring approach emphasizes learning through progressively structured research experiences. Students will initially receive clear guidance, background resources, and manageable research tasks to help them develop the necessary technical foundations. As their skills and confidence grow, they will be encouraged to take increasing ownership of their models, simulations, analysis, and research questions. Regular meetings will be used to discuss progress, troubleshoot challenges, interpret results, and identify next steps. Students will be encouraged to ask questions, experiment with different approaches, and understand that unexpected results are an important part of engineering research. The goal is to help students develop technical skills, critical thinking, research communication, independence, and confidence in conducting research.

Additional Information

Students who are interested in understanding how complex electrical systems operate and learning through simulation and hands-on engineering research are encouraged to apply.

Link to Publications

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Detecting Anomalies in Grid Communications Using LLMs

Large Language Models; Anomaly Detection; Grid Communications; Cybersecurity; Artificial Intelligence

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Computer Science, Computer Engineering, Electrical Engineering, Cybersecurity, Data Science, Information Technology, or related disciplines. Students with an interest in AI, communication systems, cybersecurity, or data analysis are encouraged to apply.
Project Location: 2000 Levy Ave, Tallahassee, FL 32310. Center for Advanced Power Systems (CAPS)
Research Assistant Transportation Required: FSU Seminole Express provides service between the FSU main campus and the FAMU-FSU College of Engineering.
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5 to 8 hours per week, 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: 1:00
End Time: 2:00
Zoom Link: https://fsu.zoom.us/j/6540925978

Project Description

Modern electric power systems depend on communication networks to exchange measurements, control commands, equipment status, and other operational information. Detecting unusual communication patterns is important for identifying system problems, communication failures, misconfigurations, and potentially malicious activity.

This project will explore the use of Large Language Models (LLMs) and related artificial intelligence methods for detecting anomalies in electric-grid communication data. Undergraduate researchers will begin by learning how communication logs, messages, and event sequences are structured and how normal system behavior can be represented and analyzed.

Students will then investigate how communication data can be converted into representations suitable for AI analysis. They will explore whether LLMs can identify unusual message sequences, unexpected events, or deviations from normal communication behavior. The project may also compare LLM-based approaches with simpler anomaly-detection methods to understand their strengths and limitations.

As the project progresses, students may investigate real-time or streaming analysis, model accuracy, false alarms, computational requirements, and the reliability of LLM-based anomaly detection.

Prior experience with Large Language Models, cybersecurity, communication networks, or electric power systems is not required. Students will be introduced progressively to the necessary concepts and tools.

Research Tasks: 1) Review introductory materials on electric-grid communications, anomaly detection, and Large Language Models.
2) Learn basic Python tools for processing, analyzing, and visualizing communication or event data.
3) Explore sample communication logs, message sequences, or simulated grid-communication datasets.
4) Implement simple baseline techniques for detecting unusual communication patterns.
5) Explore the use of LLMs or language-model-based representations for identifying anomalous messages or sequences.
6) Build an LLM-based prototype and investigate false positives, missed anomalies, and the types of communication events that are difficult to identify.
7) Document the methodology and results and prepare a research poster for the Undergraduate Research Symposium.

Skills that research assistant(s) may need: Required:
-- Interest in artificial intelligence, cybersecurity, communications, or engineering
-- Willingness to learn programming and data-analysis tools
-- Curiosity about how communication systems operate
-- Ability to work consistently and communicate progress
-- Willingness to troubleshoot and experiment with unfamiliar technologies

Recommended but not required:
-- Basic familiarity with Python or another programming language
-- Introductory programming experience
-- Basic understanding of computer networks or data structures
-- Familiarity with AI or machine learning

Mentoring Philosophy

My mentoring approach emphasizes learning through progressively structured research experiences. Students will initially receive clear guidance, background resources, example datasets, and manageable computational tasks to help them develop the necessary technical foundations. As their skills and confidence grow, they will be encouraged to take increasing ownership of data analysis, model development, experiments, and research questions. Regular meetings will be used to discuss progress, troubleshoot challenges, interpret results, and identify next steps. Students will be encouraged to ask questions, compare alternative methods, and understand that unexpected or unsuccessful results are an important part of research. The goal is to help students develop technical skills, critical thinking, research communication, independence, and confidence in conducting interdisciplinary research.

Additional Information

Students who are curious about Large Language Models, communication data, cybersecurity, or real-world AI applications are encouraged to apply.

Link to Publications

My research website https://gridai.fsu.edu/

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Virtual Reality (VR)-Based Visualization of Graph and Time-Series Data

VR; Graph Data; Time-Series Data; Data Visualization; Real-Time Interaction

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Computer Science, Computer Engineering, Electrical Engineering, Data Science, Software Engineering, Information Technology, or related disciplines. Students interested in visualization, VR, interactive systems, or data are encouraged to apply.
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5 to 8 hours per week, 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: 1:00
End Time: 2:00
Zoom Link: https://fsu.zoom.us/j/6540925978

Project Description

Many engineering systems can be represented as networks or graphs in which individual nodes are connected through physical, communication, or logical relationships. These systems also generate time-series measurements associated with individual nodes and connections. Understanding both the network structure and the changing data can be difficult using conventional two-dimensional plots.

This project will explore the use of Virtual Reality (VR) to create interactive visualizations of graph-based systems and associated time-series data. Undergraduate researchers will learn how to represent network data in a three-dimensional interactive environment and connect individual graph nodes to measurements that change over time.

Students may develop a VR environment in which a user can navigate a network, select individual nodes, inspect associated measurements, visualize changes over time, and interact with the data in real time. Power and energy systems may be used as a representative application, where network nodes can correspond to buses, devices, sensors, or other system components.

The project is designed as an interdisciplinary introduction to research in data visualization, VR, graph modeling, and interactive engineering systems. Prior experience with VR, graph theory, or power systems is not required.

Research Tasks: 1) Review introductory concepts in graph data, network visualization, time-series data, and virtual reality.
2) Learn the basic software tools and development environment used for the VR application.
3) Create and visualize simple graph structures with nodes and connections.
4) Associate time-series measurements with individual graph nodes or edges.
5) Develop interactive functions that allow users to select, inspect, and navigate graph elements.
6) Display associated time-series data when a user interacts with a selected node or system component.
7) Integrate and test real-time or simulated data streams into the visualization environment where feasible.
8) Document the developed system and prepare a research demonstration or poster for the Undergraduate Research Symposium.

Skills that research assistant(s) may need: Required:
-- Interest in virtual reality, interactive systems, data visualization, or engineering
-- Willingness to learn new programming and visualization tools
-- Curiosity and creativity in designing interactive systems
-- Ability to work consistently and communicate progress
-- Willingness to troubleshoot software and interface problems

Recommended but not required:
-- Basic programming experience in Python
-- Familiarity with Unity, Unreal Engine, or another graphics environment
-- Introductory knowledge of graphs, networks, or data structures
-- Basic experience with data visualization

Mentoring Philosophy

My mentoring approach emphasizes learning through progressively structured research experiences. Students will initially receive clear guidance, example datasets, introductory resources, and manageable development tasks to help them build the necessary technical foundations. As their skills and confidence grow, they will be encouraged to take increasing ownership of the visualization design, implementation, experimentation, and research questions. Regular meetings will be used to review progress, troubleshoot technical challenges, evaluate interface choices, and identify next steps. Students will be encouraged to experiment with different visualization and interaction approaches and to learn from both successful and unsuccessful implementations. The goal is to help students develop technical skills, creativity, critical thinking, research communication, independence, and confidence in interdisciplinary research.

Additional Information

Students who are interested in building interactive tools and exploring new ways to understand complex data are encouraged to apply.

Link to Publications

My research website https://gridai.fsu.edu/

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Personal and Professional Learning Network Development

social media, learning networks, online community, learning and culture, professional development, higher education

Research Mentor: Dr. Vanessa Dennen,
Department, College, Affiliation: Educational Psychology & Learning Systems, Education, Health, and Human Sciences
Contact Email: vdennen@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. May be of strongest interest to students in social sciences, communication, and education.
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Fully 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: Monday, August 31
Start Time: 12:30
End Time: 1:00
Zoom Link: https://fsu.zoom.us/my/vdennen

Project Description

Learning increasingly occurs across networks of people, resources, technologies, and communities rather than within a single classroom or institution. This project investigates how learners navigate these networks to seek information, share knowledge, build relationships, create new understanding, and support professional growth. Drawing on research in networked learning and personal learning networks, the project examines how individuals engage in knowledge activities across digital and face-to-face spaces and what these experiences reveal about learning in a connected world.

Research Tasks: Data organization and processing. Data coding and analysis (qualitative data). Figure and table development. Literature review.

Skills that research assistant(s) may need: Strong organizational skills and attention to detail are needed.
We will train you on all research-specific skills.
Students need to complete CITI training (“FSU Faculty, Staff, and Students” Social/Behavioral) before beginning any work.

Mentoring Philosophy

UROP offers a great opportunity for students to have early research experiences. I apply the cognitive apprenticeship model in my research teams, in which my UROP mentees are full members of the research team. As cognitive apprentices, you get to see all parts of the research process, contribute your own insights along the way, and gradually gain responsibilities as you learn how to perform different research tasks. I have high expectations for our research outcomes, and will train and support you to meet them. I will also seek ways to help you work toward your long-term educational and professional goals.
Above all else, I believe in kindness, respect, and lifelong learning, and aim to foster a research environment that supports these ideals.
I have worked with many UROP students over the years. Several have become collaborators on presentations at national conferences and some have published with me. The opportunity is there for you if you seek it. :)

Additional Information

Work on this project is, by default, remote, but there is the opportunity to have in-person meetings for people who prefer that type of interaction.
Typically we schedule meetings every 1-2 weeks, and between meetings everyone has tasks that they work on. During meetings we share the outcomes of our prior tasks, learn new skills, and determine next tasks. Between meetings we typically communicate via email, GroupMe, and MS Teams.

Link to Publications

https://journals.aau.dk/index.php/nlc/article/view/10887

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Generative AI for Student Learning

Generative AI; Student Learning; Artificial Intelligence; Engineering Education; AI Assistants

Looking for Research Assistants: Yes
Number of Research Assistants: 5
Relevant Majors: Electrical Engineering, Computer Engineering, Computer Science, Engineering, Education, Data Science, Information Technology, or related disciplines. Students from other majors interested in AI and student learning are encouraged to apply.
Project Location: 2000 Levy Ave, Tallahassee, FL 32310. Center for Advanced Power Systems (CAPS)
Research Assistant Transportation Required: FSU Seminole Express provides service between the FSU main campus and the FAMU-FSU College of Engineering.
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5 to 8 hours per week, 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: 1:00
End Time: 2:00
Zoom Link: https://fsu.zoom.us/j/6540925978

Project Description

Generative AI tools are increasingly being used by students to explain concepts, answer questions, generate examples, prepare for exams, and support independent learning. However, simply providing students with access to an AI chatbot does not necessarily result in effective learning. AI-generated responses may be inaccurate, provide answers without supporting understanding, or fail to align with the material and learning objectives of a specific course.

This project will explore how Generative AI can be designed and used to better support student learning. Undergraduate researchers will develop and evaluate AI-based learning assistants using tools such as Gemini, Gemini Gems, Gemini Notebook, and other available Generative AI platforms. These learning assistants may be grounded in instructor-provided engineering course materials and designed to provide explanations, guided hints, practice questions, feedback, and other forms of learning support.

Students will investigate how different instructions, prompts, course materials, and AI configurations affect the quality, accuracy, and educational usefulness of generated responses. The project will emphasize systematic evaluation of AI-generated learning support rather than simply using AI tools.

The project provides an accessible introduction to research at the intersection of Generative AI and education. Prior experience with Generative AI, engineering education research, or advanced programming is not required.

Research Tasks: 1) Review introductory literature on Generative AI and its applications in student learning and engineering education.
2) Explore current Generative AI learning tools such as Gemini, Gemini Gems, and Gemini Notebook.
3) Select representative engineering course topics and organize instructor-provided learning materials for use with AI systems.
4) Develop AI learning assistants for selected engineering topics using customized instructions, prompts, and course materials.
5) Design representative learning tasks such as concept questions, guided problem solving, explanations, practice questions, and feedback activities.
6) Develop evaluation criteria for correctness, relevance, clarity, grounding in course materials, educational usefulness, and hallucination.
7) Systematically test AI-generated responses across different learning tasks and configurations.
8) Investigate common AI failure modes, including incorrect explanations, unsupported answers, excessive answer generation, and responses that may not promote student reasoning.
9) Develop recommendations for designing Generative AI tools that support rather than replace active student learning.
10) Document results and prepare a research poster for the Undergraduate Research Symposium.

Skills that research assistant(s) may need: Required:
-- Interest in Generative AI, education, or engineering
-- Curiosity about how students learn
-- Willingness to explore and systematically evaluate AI tools
-- Ability to communicate observations and research progress
-- Willingness to learn new computational tools

Recommended but not required:
-- Familiarity with Generative AI tools such as Gemini or ChatGPT
-- Basic programming experience

Mentoring Philosophy

My mentoring approach emphasizes learning through progressively structured research experiences. Students will initially receive clear guidance, background resources, example learning materials, and manageable research tasks to develop the necessary foundations. As their skills and confidence grow, they will be encouraged to take increasing ownership of the design, evaluation, and improvement of Generative AI learning approaches. Regular meetings will be used to discuss progress, troubleshoot challenges, interpret results, and identify next steps. Students will be encouraged to question AI-generated results, experiment with alternative approaches, and understand that unexpected results are an important part of research. The goal is to help students develop technical skills, critical thinking, research communication, independence, and confidence in conducting interdisciplinary research.

Additional Information

Students who are curious about Generative AI, engineering, education, or how technology can improve learning are encouraged to apply.

Link to Publications

My research website https://gridai.fsu.edu/

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Algorithmic Imaginaries

social media, youth, algorithms

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: Fully 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: Monday, August 31
Start Time: 12:30
End Time: 1:00
Zoom Link: https://fsu.zoom.us/my/vdennen

Project Description

What does your social media algorithm look like? And what do you do to develop and maintain it? In this project, we examine how people envision the algorithms that influence their social media use along with the effects of these algorithms on their behaviors and opportunities. Our team will be primarily focused on qualitative data analysis this year.

Research Tasks: Literature review, qualitative data analysis, and developing figures.

Skills that research assistant(s) may need: Strong organizational skills and attention to detail are needed.
We will train you on all research-specific skills.
Students need to complete CITI training (“FSU Faculty, Staff, and Students” Social/Behavioral) before beginning any work.

Mentoring Philosophy

UROP offers a great opportunity for students to have early research experiences. I apply the cognitive apprenticeship model in my research teams, in which my UROP mentees are full members of the research team. As cognitive apprentices, you get to see all parts of the research process, contribute your own insights along the way, and gradually gain responsibilities as you learn how to perform different research tasks. I have high expectations for our research outcomes, and will train and support you to meet them. I will also seek ways to help you work toward your long-term educational and professional goals.

Above all else, I believe in kindness, respect, and lifelong learning, and aim to foster a research environment that supports these ideals.

Additional Information

Link to Publications

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Social Network Analysis of Learning Cohorts

social networks, higher education, network analysis

Research Mentor: Vanessa Dennen,
Department, College, Affiliation: Educational Psychology & Learning Systems, Education, Health, and Human Sciences
Contact Email: vdennen@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: Open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Fully 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: Monday, August 31
Start Time: 12:30
End Time: 1:00
Zoom Link: https://fsu.zoom.us/my/vdennen

Project Description

In this project we will be working from an existing data set showing relationships among different people in cohorts. We will be using social network analysis to depict and better understand these relationships among individuals as well as between individuals and shared ideas or interests.

Research Tasks: We will be engaged in literature review, data transformation and analysis using social network analysis (SNA), and data visualization. SNA is a useful approach for researchers in the social sciences and professional fields who are interested in how people and ideas are connected.
There may be an opportunity for engaging in a new data collection effort in the spring semester.

All necessary skills and software will be taught.

Skills that research assistant(s) may need: Attention to detail is important.

All necessary skills and software will be taught.

Mentoring Philosophy

UROP offers a great opportunity for students to have early research experiences. I apply the cognitive apprenticeship model in my research teams, in which my UROP mentees are full members of the research team. As cognitive apprentices, you get to see all parts of the research process, contribute your own insights along the way, and gradually gain responsibilities as you learn how to perform different research tasks. I have high expectations for our research outcomes, and will train and support you to meet them. I will also seek ways to help you work toward your long-term educational and professional goals.

Above all else, I believe in kindness, respect, and lifelong learning, and aim to foster a research environment that supports these ideals.

I have worked with many UROP students over the years. Several have become collaborators on presentations at national conferences and some have published with me. The opportunity is there for you if you seek it. :)

Additional Information

Link to Publications

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Characterizing the Stabilization of Cardiac Troponin I N-terminal Extension (NTE) on Cardiac Troponin C, and Novel Relaxation-Inducing Small Molecule Discovery

Molecular biology, cell biology, cardiomyocytes, heart muscle, computational modeling, sarcomere, troponin, molecular dynamics, AlphaFold, fluorescence microscopy

Research Mentor: Dr. Christopher Solis, he/him
Department, College, Affiliation: Health, Nutrition & Food Sciences, Education, Health, and Human Sciences
Contact Email: csolis@fsu.edu
Research Assistant Supervisor (if different from mentor): Priyanka Perumalraja she/her
Research Assistant Supervisor Email: pp22z@fsu.edu
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 1
Relevant Majors: Open to all majors, but preferably Biology, Biochemistry, Computational Biology, Computer Science, and related majors.
Project Location: On FSU Main Campus
Research Assistant Transportation Required: Yes
Remote or In-person: In-person
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: Monday, August 31
Start Time: 4:00
End Time: 6:00
Zoom Link: https://fsu.zoom.us/j/8808046008
Day: Tuesday, September 1
Start Time: 2:00
End Time: 4:00
Zoom Link: https://fsu.zoom.us/j/8808046008
Day: Wednesday, September 2
Start Time: 4:00
End Time: 6:00
Zoom Link: https://fsu.zoom.us/j/8808046008
Day: Friday, September 4
Start Time: 12:00
End Time: 3:00
Zoom Link: https://fsu.zoom.us/j/8808046008

Project Description

The human cardiac troponin complex (cTn) is a heterotrimer located on the thin filaments of sarcomeres which is essential for regulating cardiomyocyte contraction and relaxation. Phosphorylation of Ser23/24 on troponin I (cTnI) causes Ca2+ desensitization in the site II motif of troponin C (cTnC) and thus the dissociation of the stabilizing cTnI N-terminal extension (NTE) from the N-terminus of cTnC. This project aims to characterize the stabilization effect of the NTE on cTnC during contraction and relaxation by performing a combination of in-silico and in-vitro experiments. Additional studies will focus on small molecule interactions that can potentially induce similar relaxation effects to Ser23/24 phosphorylation. Students will gain hands-on experience in mammalian cell culture, in-vitro motility assays, and microscopy. Understanding specific functions of how the NTE stabilizes the cTnC N-terminus may provide insight into potential points of action regarding cardiomyocyte health and disease.

Research Tasks: UROP students will be able to conduct research tasks according to their ability and interest, including in-silico (i.e., computational) modeling and molecular dynamics. Wet lab work includes assisting with in-vitro motility assays, cell culture, and microscopy. All work will require data analysis. Students will also conduct a literature review on NTE stabilization of cTnC, EF-hand/site II motif of cTnC, and troponin complex structure and function.

Skills that research assistant(s) may need: An interest in cell and molecular biology, cardiovascular biology, protein biochemistry, and/or computational modeling are highly recommended. Strong time management, communication, teamwork, and independent-work skills are highly valued. For students interested in computational work, a moderate background in at least one programming language, preferably python, and file management is recommended. Experience with laboratory techniques is useful but not required. Training will be given during the research assistantship. Students are required to be curious and excited to learn! 

Mentoring Philosophy

My goal as a mentor is to help students identify their passions in science/research and grow as a researcher, student, professional, and individual. Research is a field driven by passion and curiosity, so I hope to help my students hone-in on these qualities to determine which topics mean the most to them. This will be done through consistent check-in meetings, mentorship for research presentations, and guidance on external research opportunities, including conferences. I also hope to support my students on their academic and professional journeys via general professional and academic development, as well as support through future endeavors. 

Additional Information

No more emails being accepted at this time. The Research Assistant Supervisor, Priyanka Perumalraja, will be the primary lab member attending the roundtables.

Link to Publications

https://solislab.create.fsu.edu/

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***Communication and Marketing Evaluation for Higher Education Opportunities

communication, marketing, social media

Research Mentor: David Montez, He/Him
Department, College, Affiliation: Center for Undergraduate Research and Academic Engagement, Undergraduate Studies
Contact Email: dmontez@fsu.edu
Research Assistant Supervisor (if different from mentor): Juniper Curtis She/Her
Research Assistant Supervisor Email: jsc21g@fsu.edu
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Media Communication Studies, Advertising, Public Relations, Marketing, English, Statistics, Professional Sales, still open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 7, During business hours
Roundtable Times and Zoom Link:

Day: Wednesday, September 2
Start Time: 12:00
End Time: 1:00
Zoom Link: https://fsu.zoom.us/j/99434762956
Day: Thursday, September 3
Start Time: 4:00
End Time: 5:00
Zoom Link: https://fsu.zoom.us/j/92478878403

Project Description

This project will examine how social media can most effectively promote undergraduate research opportunities and events at Florida State University. The student researcher will conduct a structured literature review of academic and industry sources on social media promotion best practices, covering topics such as posting frequency, content format, audience targeting, and platform-specific engagement strategies. In parallel, the researcher will collect and statistically analyze two years of historical social media engagement data (e.g., likes, shares, comments, click-throughs, follower growth) from the program's accounts to identify patterns, high-performing content types, and opportunities for improvement. The project will conclude with a set of evidence-based recommendations that synthesize findings from the literature with the program's own engagement data, informing future social media strategy for undergraduate research marketing efforts.

Research Tasks: Conduct literature search — Search academic databases (e.g., Google Scholar, Communication & Mass Media Complete) and industry sources (e.g., Hootsuite, Sprout Social, HubSpot reports) for best-practice literature.
Synthesize literature review — Organize findings into themes (e.g., content type, timing, audience engagement tactics, platform algorithms) and write a summary report.
Collect engagement data — Pull two years of historical post-level data from relevant social platforms (via native analytics tools or export).
Clean and organize data — Structure the dataset for analysis (e.g., by platform, post type, date, engagement metric).
Conduct statistical analysis — Apply descriptive and/or inferential statistics to identify trends, correlations, and top-performing content characteristics.
Compare findings to literature — Assess how well the program's actual engagement patterns align with (or diverge from) best practices identified in the review.
Develop recommendations — Draft actionable, evidence-based recommendations for improving future social media strategy.
Prepare final deliverables — Write up the literature review, data analysis, and recommendations; prepare a poster presentation summarizing key findings.

Skills that research assistant(s) may need: Recommended: Microsoft software suite including Excel

Mentoring Philosophy

My mentoring philosophy is grounded in the belief that undergraduate research should serve the student's growth first, and the project's output second. I see my role less as a supervisor directing a set of tasks and more as a guide helping students build confidence, curiosity, and ownership over their own intellectual work. Research skills matter, but so does learning to ask good questions, and tolerate ambiguity, — skills that outlast any single project.

In practice, this means I involve students early in shaping the direction of their work rather than simply assigning tasks. I encourage them to voice ideas, challenge assumptions, and make real decisions about their project, even when that means a slower or less polished result than if I directed every step myself. I also prioritize open communication. I check in regularly, not just on progress, but on how a student is experiencing the work — what's engaging them, what's frustrating them, and what they hope to get out of the experience. I try to meet students where they are, adjusting my level of guidance based on their confidence and experience rather than applying a one-size-fits-all approach. Ultimately, I measure success not by what a project produces, but by whether a student leaves the experience more curious, more capable, and more confident in their own ability to think and work independently.

Additional Information

Link to Publications

https://www.linkedin.com/in/dmontez/

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A Narrow American Exemplar: Status Threat and the Pressure to Conform to Norms

Conformity, Stereotypes, Americanness

Research Mentor: Dr. David March,
Department, College, Affiliation: Psychology, Arts and Sciences
Contact Email: march@psy.fsu.edu
Research Assistant Supervisor (if different from mentor): Ms. Maria Gullesserian
Research Assistant Supervisor Email: gullesseria@psy.fsu.edu
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 1
Relevant Majors: Psychology
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
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

When people are presented with statistics indicating that one’s racial group is declining in relative demographic size, it triggers status threat among majority groups (e.g., White Americans in the US). For example, previous work has shown that, among White Americans, information about demographic changes shifts the prototype of a “typical American” toward Whiteness and other characteristics associated with the majority. This outcome might serve as a means to implicitly reaffirm the majority’s group status. But demographic change does not occur in a vacuum. As the demographic dominance of one group diminishes, the relative size and strength other groups necessarily increases. In other words, as the majority loses demographic power, other groups gain it. These rising minority groups are often the target of backlash from the diminishing majority, including increased anti-minority bias and in-group favoritism—a response we refer to as a backlash threat. Yet little work has examined the consequences of these demographic and power shifts from the perspective of minority groups whose relative standing is increasing.

From the minority perspective, perceiving backlash threat can produce two broad responses that function as mediators to minimize its harmful consequences: (1) assimilation or (2) norm rejection. Assimilatory responses involve adopting behaviors stereotypically associated with the majority group, thereby reducing backlash threat by minimizing perceived differences between groups. Norm rejection, in contrast, occurs when minority group members resist behaviors specifically because they are prescribed by majority group norms. This response may reduce the effects of backlash threat by affirming that membership in a shared culture does not require behavioral conformity and by emphasizing the value of minority-group culture within a multiethnic society. The emerging response may depend in part on the extent to which individuals identify with the broader culture of which they are a part (e.g., how American they feel relative to minority identification). In other words, this national identification of feeling American could help indicate the reaction that follows a backlash threat and therefore would be included as a measure in our work (e.g., via a manipulation check).

Our project thus examines how demographic status threat shapes White and Hispanic Americans’ perception of the characteristics of a “Typical American.” Specifically, we ask whether members of the majority group (White Americans) and a growing minority group (Hispanic Americans) respond differently demographic status threat. We are primarily interested in how such threat shapes the implicit mental prototype of Americanness—for example, the extent to which “American” becomes associated with characteristics such as Whiteness, trustworthiness, friendliness, and other characteristics— and how these associations shift differently across White and Hispanic participants. Secondarily, we will examine whether a narrowed or broad perception of Americanness differently manifests as assimilatory or norm-rejecting behaviors, and whether these responses depend on national identification (i.e., how much one identifies with being an American) and ethnic group membership (i.e., White or Hispanic American). This could lead to high American identification Hispanic-Americans rejecting a narrow definition of Americanness due to this characteristic holding an important role in their mental perception of themselves. On the flip side, low American identification could lead to Hispanic-Americans accepting a narrower definition if individuals feel as though they do not form part of the majority in-group and are not seeking ways to fit into the category.

Research Tasks: Stimuli generation, data collection, data cleaning, literature review, and study piloting.

Skills that research assistant(s) may need: Knowledge of basic image editing such as cropping and exporting (required), knowledge on use of Qualtrics (recommended; can be taught), basic statistical knowledge (recommended).

Mentoring Philosophy

In the March Lab we encourage hands on learning to provide experiences furthering students' understanding of what goes into the research process. For example, research assistants (RAs) have opportunities to learn about the process of idea generation, study design, and background information for the work done in the lab. We provide them with relevant articles that we use as the foundation for our work. They are also assigned the task to collect data which gives them the opportunity to interact with the participants that complete our studies. RAs work closely with the lab’s graduate students and are encouraged to ask questions to further their understanding of their roles in the lab as well as the information and experience that they are able to gain in the lab. Prioritizing communication among the members of the lab is one of our main goals in order to foster a collaborative environment. We also encourage students to share their future plans in order to assign them tasks that are relevant to their goals.

Additional Information

Link to Publications

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