UROP Project
Deep Learning for Time-Series Forecasting
Deep Learning; Time-Series Forecasting; Artificial Intelligence; Machine Learning; Energy Systems
Research Mentor: Dr., Prof. Ravikumar Gelli, He, His, Him
Department, College, Affiliation: Florida State University/ECE Department, 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:
Department, College, Affiliation: Florida State University/ECE Department, 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, Computer Science, Data Science, Statistics, Applied Mathematics, or related quantitative disciplines. Students from other majors with an interest in artificial intelligence and data analysis are also 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:
Number of Research Assistants: 2
Relevant Majors: Electrical Engineering, Computer Engineering, Computer Science, Data Science, Statistics, Applied Mathematics, or related quantitative disciplines. Students from other majors with an interest in artificial intelligence and data analysis are also 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
Many engineering systems generate measurements that change continuously over time. Examples include electricity demand, renewable energy generation, weather conditions, voltage, frequency, equipment measurements, and other sensor data. Being able to predict how these quantities will change in the future is important for planning, operation, resource management, and decision-making.This project will explore how deep-learning methods can be used to forecast future values from historical time-series data. Undergraduate researchers will begin by learning how to visualize and analyze time-series datasets, identify trends and recurring patterns, and understand how past observations can be used to predict future behavior. Students will then develop baseline forecasting approaches and progressively explore deep-learning models for time-series prediction.
Students will work primarily with power and energy system datasets, such as electricity demand or renewable energy generation, while developing broadly applicable skills in Python, data analysis, machine learning, deep learning, and model evaluation.
Prior experience with deep learning, machine learning, forecasting, or electric power systems is not required. The project is designed to provide first-year and early undergraduate students with a structured introduction to computational research.
Research Tasks: 1) Review introductory materials on time-series data, forecasting, and applications in engineering and energy systems.
2) Learn basic Python tools for data analysis, visualization, and machine learning.
3) Explore and visualize historical time-series datasets such as electricity demand, solar generation, wind generation, weather, or other engineering measurements.
4) Identify important characteristics of time-series data, including trends, seasonality, variability, and correlations.
5) Prepare datasets for forecasting by handling missing values, selecting input features, scaling data, and creating appropriate training, validation, and testing sets.
6) Develop simple baseline forecasting models to establish reference performance.
7) Progressively develop and evaluate deep-learning models for time-series forecasting, such as neural networks, recurrent models, or other suitable architectures.
8) Compare forecasting methods using appropriate performance metrics and investigate how factors such as input history, forecast horizon, and data quality affect prediction accuracy.
9) Analyze model errors and identify conditions under which forecasting becomes more difficult.
10) 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, data analysis, or engineering
-- Willingness to learn programming and computational tools
-- Curiosity about patterns in real-world data
-- Ability to work consistently and communicate progress
-- Willingness to troubleshoot and experiment with different approaches
Recommended but not required:
-- Basic familiarity with Python or another programming language
-- Introductory mathematics or statistics
-- Basic experience working with data or spreadsheets
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 analysis, 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, experiment with different approaches, and understand that unexpected or unsuccessful results are part of the research process. 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 patterns in real-world data and learning how AI can be used to predict future behavior are encouraged to apply.Link to Publications
My research website https://gridai.fsu.edu/LINK Research Practice Partnership
professional development, partnership, collaboration, K-12 education, higher education
Research Mentor: Dr. Vanessa Dennen,
Department, College, Affiliation: EPLS, Education, Health, and Human Sciences
Contact Email: vdennen@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators: Dr. Secil Caskurlu, Dr. Megan Crombie. Ms. Hilal Ayan
Faculty Collaborators Email:
Department, College, Affiliation: EPLS, Education, Health, and Human Sciences
Contact Email: vdennen@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators: Dr. Secil Caskurlu, Dr. Megan Crombie. Ms. Hilal Ayan
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: No, the project is remote 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:
Number of Research Assistants: 2
Relevant Majors: Open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required: No, the project is remote 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: 12:00
End Time: 12:30
Zoom Link: https://fsu.zoom.us/my/vdennen
Project Description
LINK (Learning, Inquiry, and Networked Knowledge) is a research-practice partnership that brings together university researchers, graduate students, and K-12 educators to collaboratively investigate authentic problems of practice in education. Through professional learning, collaborative inquiry, and classroom-based research, participants explore how teaching, learning, and educational technologies can support meaningful educational experiences. Undergraduate researchers will contribute to the organization's ongoing work by assisting with research coordination, communication, event planning, data organization, resource development, and community-building activities. Students will gain experience working within a collaborative research team while learning about educational research, partnership development, and the processes through which research and practice inform one another.Year 1 of the partnership included teacher/doctoral student projects focused on AI and wrapped up in June 2026. Year 2 is beginning and the focus is on sensemaking and critical thinking in K-12 classrooms. The video link below provides an overview of Year 1.
Research Tasks: Tasks may include: literature review, data organization and analysis, assistance with developing and promoting projects under the larger LINK umbrella, creating graphics, assisting with data collection
Skills that research assistant(s) may need: Required: attention to detail
We will train for all necessary tasks
Mentoring Philosophy
UROP offers a great opportunity for students to have early research experiences. I apply the cognitive apprenticeship model in my research teams, where my UROP mentees are full members of the 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.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, and there are opportunities to go into a K-12 school as part of the research team, if interested.Typically we schedule project team 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://www.youtube.com/watch?v=2b-6uTP4ryQModeling 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:
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:
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
Detecting Anomalies in Grid Communications Using LLMs
Large Language Models; Anomaly Detection; Grid Communications; Cybersecurity; Artificial Intelligence
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:
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: 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:
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/Virtual Reality (VR)-Based Visualization of Graph and Time-Series Data
VR; Graph Data; Time-Series Data; Data Visualization; Real-Time Interaction
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:
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: 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:
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/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:
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:
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/10887Generative AI for Student Learning
Generative AI; Student Learning; Artificial Intelligence; Engineering Education; AI Assistants
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:
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: 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:
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/Algorithmic Imaginaries
social media, youth, algorithms
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:
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
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:
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
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
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:
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:
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
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:
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:
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!