UROP Project | Center for Undergraduate Research and Academic Engagement

meta-analysis, psychology, schizophrenia, spatial navigation, cognition

Research Mentor: Dorota Kossowska-Kuhn,
Department, College, Affiliation: Psychology, Arts and Sciences
Contact Email: kuhn@psy.fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: No
Number of Research Assistants: 2
Relevant Majors: Open to all majors.
Project Location: Zoom
Research Assistant Transportation Required: No, the project is remote
Remote or In-person: Fully Remote
Approximate Weekly Hours: 5-10,
Roundtable Times and Zoom Link: Tuesday, Sept. 3: 1-1:30, 1:30-2
Wednesday, Sept. 4: 2-2:30, 2:30-3
Thursday, Sept. 5: 3-3:30, 3:30-4
Zoom link for all meetings: https://fsu.zoom.us/j/2290878150
Meeting ID: 229 087 8150

Project Description

Meta-analysis is a quantitative research design that synthesizes results from multiple studies of a particular topic, and analyses the statistical significance of these compiled results to estimate a standardized effect size. In this study, we are examining the differences between people with schizophrenia and healthy controls in regard to spatial navigation skills.

Spatial navigation is a conglomeration of skills that are used to orient and navigate in daily life. Our previous research has shown that spatial navigation performance is an indicator of Alzheimer’s Disease and Mild Cognitive Impairment. We are investigating schizophrenia now to see if spatial navigation impairments are dementia specific.

In this project, you will learn about and become familiarized with scientific literature, data extraction, and the nature of systematic research.

Research Tasks: Tasks range from screening through relevant literature, extracting relevant data (coding), and other miscellaneous tasks such as scientific database navigation.

Skills that research assistant(s) may need: All the hardworking and motivated students are welcome.

Mentoring Philosophy

My mentoring philosophy revolves around empowering undergraduate students to excel in their project work through a combination of ownership, accountability, shared experience, and interactive learning. I believe in fostering an environment that nurtures their growth and encourages independent thinking.
I emphasize giving mentees ownership of their work by involving them in project decisions, from goal setting to execution. This not only bolsters their confidence but also instills a sense of responsibility for their outcomes. I promote accountability by setting clear expectations and milestones, enabling them to track their progress and take pride in their achievements.
Drawing from my own experience, I share stories of challenges and successes, illustrating the real-world applications of their efforts. This bridges the gap between theory and practice, enhancing their understanding and motivation. I also encourage open dialogue, where questions and ideas are welcomed, creating an interactive platform for collaborative learning.
I understand that each student is unique, with varying skills and aspirations. To accommodate this, I tailor my guidance, offering guidance that aligns with their interests and goals. I provide resources, recommend reading materials, and suggest relevant workshops, fostering holistic development.
In conclusion, my approach to mentoring undergraduates centers on nurturing their autonomy, cultivating responsibility, leveraging shared experiences, and fostering an interactive learning ecosystem. By doing so, I aim to not only support their immediate project objectives but also to equip them with lifelong skills for success.

Additional Information

Link to Publications

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transit; housing; built environment;

Research Mentor: Dr. Amber DeJohn, she/her
Department, College, Affiliation: Geography, Public Health, Social Sciences and Public Policy
Contact Email: amber.dejohn@fsu.edu
Research Assistant Supervisor (if different from mentor): Dr. Matthew Suandi he/him
Research Assistant Supervisor Email: Matthew.Suandi@FHFA.GOV
Faculty Collaborators: Dr. Matthew Palm
Faculty Collaborators Email: palmmatt@unc.edu

Looking for Research Assistants: No
Number of Research Assistants: 1
Relevant Majors: Economics, Geography, Computer Science, Statistics, Civil Engineering, Urban Planning, or related majors (required skills more important)
Project Location: N/A
Research Assistant Transportation Required: No, the project is remote
Remote or In-person: Fully Remote
Approximate Weekly Hours: 7-10,
Roundtable Times and Zoom Link: Not participating in the Roundtable

Project Description

This project studies the housing market effects of transit development; specifically, we consider four openings and expansions of light rail and rapid transit lines across the United States since 1999. Reverse causality is the fundamental problem affecting the credibility of estimates of the returns to transit spending. In determining where lines are to be drawn and stations are to be placed, urban planners incorporate political considerations that are likely to be associated with property values and expected changes in property values. Hence, our empirical approach leverages a quasi-experimental difference-in-differences analysis to document the key research question of the capitalization of fixed guideway public transit investments into real estate prices and trends.

Research Tasks: - Geocode light rail transit alternatives for all transit projects (for which we have documentation) from the last 25 years
- Assist with data pulling and management (e.g., LODES data from the BLS; road network data from OSM)
- Assist with data checks / quality assurance

Skills that research assistant(s) may need: Required:
- GIS (ArcGIS/ArcPro/ArcGIS Online - software license provided by team)
- Coding (R, STATA, Python, Shell preferred; R and/or STATA sufficient)
- Strong attention to detail and ability to resolve issues independently

Mentoring Philosophy

As a research mentor, I believe in fostering a collaborative environment that encourages curiosity, critical thinking, and the open exchange of ideas. My role is to guide and support mentees in developing their skills and confidence, empowering them to become independent researchers. I prioritize open communication, mutual respect, and a growth mindset, recognizing that mistakes are valuable learning opportunities. I am committed to providing personalized mentorship, adapting to meet each student's unique needs and goals. In doing so, I hope to encourage students to feel a sense of ownership in the research and foster a passion for geographic inquiry.

Additional Information

As a researcher on this project, you will have opportunities to work with world-class researchers in the federal government and academia. The student researcher will be working directly with Dr. DeJohn and Dr. Suandi, and occasionally with Dr. Palm at UNC-Chapel Hill. This is a time sensitive project, so adherence to deadlines is critical. Do not apply if your fall term is already busy.

Link to Publications

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Sleep, mice, RNA, molecular, neuroscience, hippocampus

Research Mentor: Dr. Lisa Lyons,
Department, College, Affiliation: Department of Biological Science, Program in Neuroscience, Arts and Sciences
Contact Email: lyons@bio.fsu.edu
Research Assistant Supervisor (if different from mentor): Natalie Storch
Research Assistant Supervisor Email: nstorch@fsu.edu
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: No
Number of Research Assistants: 2
Relevant Majors: Neuroscience, Biology, Biochemistry, BMS
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: In-person
Approximate Weekly Hours: 10,
Roundtable Times and Zoom Link: Not participating in the Roundtable

Project Description

Sleep fragmentation and chronic insomnia are significant public health concerns that affect millions of children, teenagers and adults. Sleep fragmentation impairs learning and cognitive performance, increases disease risk for many diseases, and aggravates neurological disorders. We are interested in the neural and molecular consequences of chronic sleep restriction. We seek undergraduate research assistants to help investigate the impacts of chronic sleep restriction in a mouse model.

Research Tasks: Student will be assisting with chronic sleep restriction experiments including animal monitoring and tissue collection. Student will need to complete training necessary for working with animals on an animal care protocol. Student will be responsible for checking on animals when assigned, assisting with sleep restriction and maintaining accurate records of weight and behavior. Student will also be expected to learn basic molecular skills in the laboratory in preparation for assisting with molecular experiments. Student will also help with lab maintenance, be expected to read assigned literature, and attend weekly lab meetings. This project requires all in person work.

Skills that research assistant(s) may need: Strong background in biology and neuroscience. Pipetting experience required. Recommended: experience with solution making.

Mentoring Philosophy

I strive to help students become critical thinkers who ask questions, pay attention to details, and discuss science and scientific results from multiple angles. I encourage students to show initiative in their work and in their approach to science through a lab atmosphere that emphasizes reading scientific literature and discussing scientific questions and approaches. Everyone in the lab is encouraged to think about the research objectives and how the objectives may be achieved. I work with students to plan experiments or find solutions for problems that arise, but I encourage students to work independently to perform experiments after training.

Additional Information

Link to Publications

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public schools, private schools, education, research, literature review

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Research Mentor: Dr. Wajeeha Hazoor Bajwa, she/her/hers
Department, College, Affiliation: Office of Research, Education, Health, and Human Sciences
Contact Email: wb24a@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: 3
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 to 10 hours,
Roundtable Times and Zoom Link: Not participating in the Roundtable

Project Description

Educational privatization and school choice is highly contested. Whereas proponents of educational privatization argue that market-oriented solutions redress public sector shortcomings in quality and access to education for the masses, proponents of public education argue that private education comes at the expense of equity; that is, private providers exclude poorly performing students and students with disabilities to optimize performance in high-stakes testing.

In this context, this projects seeks to conduct a keyword search on major academic databases to extract school choice literature on the impacts and implications of public and private educations on test scores and access to education for vulnerable populations. The time-period of the analysis is 2010 to 2024.

This project will inform the ongoing debate on privatization in education and school choice and whether to determine which form of education is more effective in terms of increasing access and quality of education. This is a topic that holds increasing relevance in the passing of the Abidjan Principles in 2019 and the Right to Education acts. It will result in the publication of a journal article in which the GAs will be listed as co-authors.

Research Tasks: The GAs are expected to:
1) Conduct a keyword search on key academic databases.
2) Organize relevant journal articles according to themes aligning with the research questions.
3) Analyze and summarize relevant journal articles. The GAs are expected to note effect sizes in these summaries.

Skills that research assistant(s) may need: Recommended: Database search
Recommended: Journal article analysis
Recommended: Journal article summarization
Recommended: Analytical thinking and ability to draw connections between seemingly disparate concepts

Mentoring Philosophy

My mentorship philosophy puts the mentee first and builds on a collaborative rather than a hierarchical dynamic. I take the time to understand the mentee's capacity, ambitions, and needs and fully account for them when establishing the mentoring relationship and milestones. In addition to equipping the mentee with the skills they seek, I seek to equip them with sustainable skills and knowledge that will benefit them throughout their academic career. I have strong networks in academia in the United States and overseas and am happy to leverage them to benefit the mentee's ambitions. I also have strong links in industry and can advice mentees on building upon their academic strengths to position themselves for industry.

Additional Information

Link to Publications

https://orcid.org/0009-0007-5513-8049

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PFAS, Laboratory work, Machine Learning, Deep Learning,

Research Mentor: Mr. Shahin Alam , Shaheen
Department, College, Affiliation: Civil & Environmental Engineering, FAMU-FSU College of Engineering
Contact Email: ma23ch@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: Environmental Engineering, Environmental Science, Chemistry, Chemical Engineering, Data Science, Computer Science
Project Location: FAMU-FSU College of Engineering
Research Assistant Transportation Required: University Bus
Remote or In-person: In-person
Approximate Weekly Hours: 10,
Roundtable Times and Zoom Link: Tuesday, September 3:00 to 3:30
Zoom Link:
https://fsu.zoom.us/j/92107461404
https://fsu.zoom.us/j/92107461404

Project Description

Introduction
Per- and polyfluoroalkyl substances (PFASs) represent a class of synthetic chemicals that have emerged as pervasive contaminants across various environmental media globally (Alam & Chen, 2024). Their persistence, bioaccumulative nature, and toxicity present substantial risks to ecosystems and human health (Hamid et al., 2023). The Lower Suwannee River Basin of Florida, which provides essential water resources for nearby communities, is threatened by PFAS contamination owing to intensified industrial and agricultural activities. This study aims to characterize PFAS spatial distribution in the basin and potential ecological impacts. Owing to limited available data, machine learning (ML) will be used to identify PFAS contamination patterns, and trace PFAS sources (Kibbey et al., 2020, 2021). Deep learning (DL), a subset of machine learning, which excels at modeling complex relationships in datasets, will be used to predict future PFAS contamination scenarios, enabling proactive management and mitigation strategies. This goal of this proposed research outlines a comprehensive study to investigate PFAS contamination in the Lower Suwannee River Basin by integrating field sampling and laboratory analysis with advanced ML and DL techniques to identify PFAS sources, track their distribution, and predict future contamination patterns on a watershed scale. It is hypothesized that PFAS contamination in the Lower Suwannee River Basin has identifiable source footprints and exhibit spatial distribution patterns corresponding to related industrial and agricultural activities, which can be predicted and modeled using ML and DL techniques.
Objective
The research questions to be answered in this proposed research include: 1) What are the concentration levels and distribution patterns of PFAS in the surface water and sediments of the Lower Suwannee River Basin? 2) How can machine learning models be utilized to identify and trace the sources of PFAS contamination using non-targeted PFAS data? And 3) Can deep learning models accurately predict PFAS contamination scenarios based on current and historical data? Corresponding to the research questions, the Objectives of this proposal research are to: 1) quantify PFAS concentrations and distribution in the surface water and sediments of the Lower Suwannee River Basin by assessing PFASs in surface water and sediments through field sampling and laboratory analysis, 2) apply machine learning to track PFAS sources by using non-targeted PFAS data and developing machine learning models to identify contamination footprints, and 3) develop deep learning models to predict future PFAS contamination scenarios, enabling proactive management and mitigation strategies.
Methodology
Water and sediment samples will be collected from locations along Suwannee River in the Lower Suwannee River Basin, covering upstream, midstream, and downstream sites. PFASs in the surface water and sediments will be identified and quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) following EPA methods 1633. Statistical analyses will be performed to determine PFAS concentration levels, distribution patterns, and potential sources. Non-targeted PFAS data from various sources, including environmental samples, industrial discharge records, and historical monitoring data, will be compiled from US Environmental Protection Agency (EPA), Florida Department of Environmental Protection (FDEP), and published literatures, and integrated to provide a comprehensive overview of PFAS contamination in the region. ML algorithms will be used to extract meaningful features from the non-targeted PFAS data by focusing on identifying unique fingerprints that can indicate specific contamination sources, enhancing the accuracy of source tracking. Supervised and unsupervised ML models, such as random forests and clustering algorithms, will be applied to identify and classify PFAS sources. These models will help distinguish between different contamination sources and provide insights into their contributions to the overall PFAS contamination levels. The ML models will be validated using potential known PFAS sources (e.g., airport location, military bases, wastewater treatment plant, biosolids application location) and cross-referenced with historical data to ensure the model reliability and accuracy. A comprehensive dataset will then be established by incorporating PFAS concentration levels, environmental parameters (e.g., temperature, pH, flow rate), rainfall, soil, and land use data, which will serve as the foundation for developing predictive models. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), will be modified and trained to predict future PFAS contamination levels. These models will leverage the comprehensive dataset to learn complex relationships and patterns and be trained to simulate various contamination scenarios under different environmental and anthropogenic conditions. The predictions of the deep learning models will be validated using a subset of the dataset, and model parameters will be optimized to improve accuracy and reliability, ensuring robust predictions. The results of the proposed research will provide valuable insights into potential future contaminations and inform proactive management strategies.
References
Alam, M. S., & Chen, G. (2024). PFAS Regulatory Frameworks, Sources, Occurrence, Fate, and Exposure: Trend, Concern, and Gaps. Manuscript submitted for publication.
Hamid, N., Junaid, M., Manzoor, R., Sultan, M., Chuan, O. M., & Wang, J. (2023, Dec 20). An integrated assessment of ecological and human health risks of per- and polyfluoroalkyl substances through toxicity prediction approaches. Science of the Total Environment, 905.
Kibbey, T. C. G., Jabrzemski, R., & O'Carroll, D. M. (2020, Aug). Supervised machine learning for source allocation of per- and polyfluoroalkyl substances (PFAS) in environmental samples. Chemosphere, 252.
Kibbey, T. C. G., Jabrzemski, R., & O'Carroll, D. M. (2021, Jul). Source allocation of per- and polyfluoroalkyl substances (PFAS) with supervised machine learning: Classification performance and the role of feature selection in an expanded dataset. Chemosphere, 275.

Research Tasks: Water and Sediment sampling; Laboratory analysis; Literature Review; Computational Modeling

Skills that research assistant(s) may need: Recommended

Mentoring Philosophy

My mentoring philosophy is built on the belief that everyone has the potential to excel when given the proper support and opportunities. My primary goal is to create an environment where individuals can grow, thrive, and reach their full potential. In my view, effective mentoring is based on trust, open communication, and mutual respect. I recognize that each person has unique strengths, perspectives, and goals, and I see it as my responsibility to help them discover and develop these qualities.
I approach mentoring with empathy and patience, understanding that each person’s path is unique. By getting to know their individual goals, challenges, and interests, I tailor my guidance to meet their specific needs. I aim to foster a collaborative and encouraging atmosphere that builds confidence, nurtures critical thinking, and empowers individuals to take ownership of their development. A strong commitment to diversity, equity, and inclusion is central to my mentoring approach. I am dedicated to supporting individuals from all backgrounds, particularly those who are underrepresented. A diverse and inclusive environment enriches everyone, leading to innovative ideas and solutions. Therefore, I actively seek to mentor people from diverse backgrounds, encouraging them to pursue their passions and contribute meaningfully to their fields. Beyond academic and professional guidance, I view mentoring as an opportunity to help individuals navigate broader life challenges. I emphasize the importance of lifelong learning, curiosity, and open dialogue about essential life skills as foundations for personal and professional growth.

Additional Information

Link to Publications

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social entrepreneurship, entrepreneurial growth, African descent, societal impact

Research Mentor: Dr. Eric Liguori,
Department, College, Affiliation: Entrepreneurship, Jim Moran College of Entrepreneurship
Contact Email: eliguori@fsu.edu
Research Assistant Supervisor (if different from mentor): Michael Cedeno
Research Assistant Supervisor Email: mac19f@fsu.edu
Faculty Collaborators: Dr. Susana Santos
Faculty Collaborators Email: ssantos@jmc.fsu.edu

Looking for Research Assistants: No
Number of Research Assistants: 2
Relevant Majors: Open to all majors, but students studying (or interested in) Business, Entrepreneurship, Psychology, Sociology, Social Work, International Relations, Public Administration, and/or Urban and Regional Transformation may find a particularly strong alignment.
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 8,
Roundtable Times and Zoom Link: Friday, September 6, 12:30-1:00pm
Zoom: https://fsu.zoom.us/j/8257454875
Meeting ID: 825 745 4875

Project Description

This exciting research project emerged from a U.S. Department of State grant that sought to identify, understand, and help eliminate the barriers faced by social entrepreneurs of African Descent from across the globe. The data on which this project is based is highly novel and completely proprietary; drawn from a network of 55 Founders, Co-Founders, Presidents, or Executives of high-impact organizations working to address social issues across the globe. These founders span a diverse array of countries including Ghana, Liberia, Tanzania, Uganda, Zimbabwe, Australia, Belgium, Germany, Portugal, Spain, Netherlands, United Kingdom, Algeria, Morocco, Barbados, Brazil, Canada, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Peru. All of the founders were of African descent, enabling us to better identify and explore opportunities and barriers faced by this unique population of change makers. Preliminary data analysis has yielded some very promising early results and informed the development of a working model we now seek to further refine and validate with your help. Given the current state of this project, we feel confident it is well-positioned to result in both a successful conference presentation and a peer-reviewed journal publication. We also expect our findings to be of interest to policy makers at a variety of levels and believe there is potential for our results to garner media attention.

Research Tasks: The most immediate next step is the completion of a literature review to best frame out the work. After that is completed, it is likely some additional interviews will be needed and/or a survey developed and disseminated. Then we will analyze the new data, frame out the remainder of the manuscript, and more forward towards presentation and publication.

Skills that research assistant(s) may need: Required Skills: strong written and verbal communication skills; high attention to detail
Recommended Skills: critical thinking; collecting and analyzing data; conducting literature reviews; digital information literacy

Mentoring Philosophy

This project is part of the LEAD Lab (https://jimmorancollege.fsu.edu/research/labs/lead-lab) in the Jim Moran College of Entrepreneurship. The LEAD Lab's mentoring philosophy is rooted in empowering undergraduate researchers to excel in both academic inquiry and practical application. We prioritize collaboration, encouraging students to engage with diverse perspectives and interdisciplinary approaches that reflect the complexities of entrepreneurship. Mentorship at the LEAD Lab focuses on fostering continuous learning, ethical research practices, and entrepreneurial action. By creating an inclusive environment where every student is valued, we aim to inspire the next generation of scholars to contribute meaningfully to both the academic community and society at large. Through this mentorship, students are equipped to transform research into impactful, real-world outcomes.

Additional Information

UROP students engaging in this project will become affiliates of the LEAD Lab and eligible to engage in the lab's myriad of other projects, invited to lab events, and benefit from access to the lab's expansive network of global scholars. The value of this as a networking opportunity for students seeking to pursue research careers in academia and the social sciences is profound. Students interested in working in the public sector, especially on impact projects, will also find great benefits to participating.

Link to Publications

https://eliguori.com

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entrepreneurship, economic development, public policy, Ukraine

Research Mentor: Dr. Eric Liguori,
Department, College, Affiliation: Entrepreneurship, Jim Moran College of Entrepreneurship
Contact Email: eliguori@fsu.edu
Research Assistant Supervisor (if different from mentor): Michael Cedeno
Research Assistant Supervisor Email: mac19f@fsu.edu
Faculty Collaborators: Dr. Vilma Fuentes, Program Director, FSU Ukraine Task Force; Susana Santos, Jim Moran Professor, Jim Moran College of Entrepreneurship
Faculty Collaborators Email: ssantos@jmc.fsu.edu

Looking for Research Assistants: No
Number of Research Assistants: 6
Relevant Majors: Open to all majors, but students studying (or interested in) Business, Entrepreneurship, Economics, International Relations, Public Administration, and/or Political Science may find a particularly strong alignment.
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 8,
Roundtable Times and Zoom Link: Friday, September 6, 1:30-2:00pm
Zoom: https://fsu.zoom.us/j/8257454875
Meeting ID: 825 745 4875

Project Description

This forward-looking research project is a joint effort between the LEAD Lab in the Jim Moran College of Entrepreneurship (https://jimmorancollege.fsu.edu/research/labs/lead-lab) and FSU’s Ukraine Task Force (https://lsi.fsu.edu/fsu-ukraine-task-force). The project’s goal is to begin exploring strategies for post-conflict recovery in Ukraine, and in particular, strategies to rebuild and strengthen their entrepreneurial ecosystem. More specifically, we aim to identify, understand, and address the challenges Ukrainian entrepreneurs can expect to face as they navigate the complex process of rebuilding their businesses and fostering economic growth in a post-conflict environment.

The research will be conducted through a series of in-depth interviews with Ukrainian entrepreneurs and policymakers, as well as through the study of lessons learned from other economic recovery situations (e.g., BP Oil Spill; COVID-19 pandemic; hurricanes). UROP students will get a ground-level perspective on the obstacles and opportunities that arise during the recovery phase. These interviews will provide unique and valuable insights, as they capture the real-time experiences of those who are directly involved in the economic revival of the nation.

We anticipate that the findings will contribute to the development of a robust framework for fostering entrepreneurial success in Ukraine, with potential applications in other regions facing similar challenges. Given the innovative nature of this research, we are confident that it will be well-positioned for presentation at leading conferences and publication in peer-reviewed journals. Additionally, we expect our findings to be of significant interest to policymakers at various levels, as well as to the broader media, given the global relevance of Ukraine's recovery efforts.

Research Tasks: Our work will proceed forward in phases, beginning with a comprehensive literature review of entrepreneurial recovery situations. Once completed, we will shift to exploring the current state of entrepreneurship in Ukraine, including looking at what the status quo was pre-conflict. Then we will move into an active data collection phase, talking to entrepreneurs, policy makers, and other experts, with the goal being to develop an actionable framework to aid recovery efforts. The ultimate deliverable is a conference presentation, a journal publication, and taking steps to deliberately position our findings to have real-world impact.

Skills that research assistant(s) may need: Required Skills: strong written and verbal communication skills; high attention to detail
Recommended Skills: understanding of global politics and / or economies; critical thinking; collecting and analyzing data; conducting literature reviews

Mentoring Philosophy

This project is part of the LEAD Lab in the Jim Moran College of Entrepreneurship. The LEAD Lab's mentoring philosophy is rooted in empowering undergraduate researchers to excel in both academic inquiry and practical application. We prioritize collaboration, encouraging students to engage with diverse perspectives and interdisciplinary approaches that reflect the complexities of entrepreneurship. Mentorship at the LEAD Lab focuses on fostering continuous learning, ethical research practices, and entrepreneurial action. By creating an inclusive environment where every student is valued, we aim to inspire the next generation of scholars to contribute meaningfully to both the academic community and society at large. Through this mentorship, students are equipped to transform research into impactful, real-world outcomes.

Additional Information

Link to Publications

www.eliguori.com

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Large Language Models, Efficiency

Research Mentor: Dr. Shangqian Gao,
Department, College, Affiliation: Computer Science, Arts and Sciences
Contact Email: sg24bi@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, Electrical Engineering
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10,
Roundtable Times and Zoom Link: Not participating in the Roundtable

Project Description

Large Language Models (LLMs) have gained significant popularity recently. However, their model size is often too large to be deployed on commercial-grade hardware. The objective of this research project is to explore cutting-edge techniques for reducing the size of LLMs, such as weight pruning, structural pruning, and other similar methods. The project begins with the implementation of existing techniques on various LLMs, including OPT, Phi, LLama, and others. With a thorough understanding of the limitations of current methods, novel approaches can be proposed to address these limitations.

Research Tasks: Research Tasks:
a. Literature Review on Large Language Models and Model Compression
b. Implement previous model compression methods for Large Language Models
c. Improve the previous model compression algorithms based on the understanding of the Implementation.

Skills that research assistant(s) may need: Programming skills in Python are required.
Knowledge of Linear Algebra and Probability are required.
Experience with Pytorch is highly recommended.

Mentoring Philosophy

My mentoring philosophy is built on collaboration, growth, and mutual respect. My role is to guide students in discovering their strengths, overcoming challenges, and achieving their goals. I aim to equip students with foundational knowledge in machine learning, coding, and paper reading relevant to my research. Recognizing that research is a challenging journey, I encourage students to view obstacles as opportunities for learning and growth. By sharing my experiences and offering constructive feedback, I strive to deepen their understanding of key topics. Given the inherent uncertainties in exploration, I am committed to providing students with hands-on experience in implementation, learning, and the development of new ideas, helping them navigate their research with confidence.

Additional Information

Link to Publications

https://gaosh.github.io/publications/

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Alzheimer's Disease, statistics, dementia, neurodegeneration, memory

Looking for Research Assistants: No
Number of Research Assistants: 2
Relevant Majors: Open to all majors.
Project Location: remote
Research Assistant Transportation Required: No, the project is remote
Remote or In-person: Fully Remote
Approximate Weekly Hours: 7-10,
Roundtable Times and Zoom Link: Tuesday, Sept. 3,: 3-3:30, 3:30-4
Wednesday, Sept. 4: 1-1:30, 1:30-2
Thursday, Sept. 5: 1-1:30, 1:30-2
Zoom link for all meetings: https://fsu.zoom.us/j/2290878150
Meeting ID: 229 087 8150

Project Description

The proportion of individuals aged 65 and over is expected to nearly double by 2050, reaching 1.6 billion people. Consequently, the prevalence of dementia, primarily driven by aging, is anticipated to rise sharply. Dementia, currently the 7th leading cause of death globally, significantly contributes to disability and dependency among older adults. Alzheimer's disease (AD) is the most common form, comprising 60-80% of dementia cases. Early diagnosis allows patients and their families to understand the challenges ahead and develop strategies for managing current and future issues. With emerging medical treatments, early and accurate diagnosis is increasingly critical.
Spatial navigation tests have shown promise in diagnosing Mild Cognitive Impairment (MCI), a condition that often precedes AD, predicting the risk of conversion from MCI to dementia, and monitoring AD progression. Our meta-analysis on spatial navigation skills in individuals with MCI revealed significant differences between this group and cognitively healthy older adults at the level of standardized mean difference Hedges g = 0.88 (Kossowska-Kuhn et al., in prep). We plan to take the next step towards conducting a model-based meta-analysis that will allow us to compare the effectiveness of various potential predictors of Alzheimer's disease in predicting its presence, as well as identify which of these predictors make independent contributions to the prediction.

Research Tasks: - data collection
- data analysis
- literature review

Skills that research assistant(s) may need: All motivated and hardworking students are welcome.

Mentoring Philosophy

Additional Information

Link to Publications

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Robotics, STEM Education

Research Mentor: Dr. or Prof. Taylor Higgins, She/her
Department, College, Affiliation: Florida State University, FAMU-FSU College of Engineering
Contact Email: th22u@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: No
Number of Research Assistants: 1
Relevant Majors: Open to all ENGINEERING/Computer Science majors
Project Location: AME Building (2003 Levy Ave. Tallahasse, FL)
Research Assistant Transportation Required: Yes
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5-10 (negotiable),
Roundtable Times and Zoom Link: Not participating in the Roundtable

Project Description

As a female researcher in STEM, I am passionate about reaching out to the local community to increase interest and diversity of participation in STEM. Specifically, I want to develop a workshop or series of workshop activities that I can use to help teach K-12 students in the community about robotics, technology, and engineering. I have a robotics learning platform called the Edison (https://meetedison.com/), but I need someone to 1) explore how to use it and what lesson-plan resources are currently available and 2) develop a set of workshops for various ages of students to engage with these robots to learn about STEM. This will involve following online instructions to set up my Edison robot, finding and following instructions online for pre-planned lessons that are already available (just so we know what's resources we have at our disposal), and then possibly creating your own learning module with the robots to teach students about basics in human/robot interaction (the focus of the RTHM lab here at FAMU-FSU).

Research Tasks: Very very basic programming. Following online resources to learn very basic robotics principles and then creating lesson plans to teach these principles to a K-12 audience.

Skills that research assistant(s) may need: Required: Introductory experience with any programming language. Clear communication skills in both writing and speaking. An interest in education. I need someone who WANTS to engage with the community and make robotics more accessible.

Mentoring Philosophy

As a mentor, I work best with students who are inquisitive, pro-active, and problem solvers. I do not like to micro-manage. I see my job as being a resource to my research students, helping to solve problems when stuck, providing overall vision, and occasionally giving nudges in the right direction. I see my students as the expert in their given project, and I expect students to go learn skills, find possible solutions, try many things that may or may not work, and ultimately to come to research meetings ready to teach me all the cool things they’ve learned, tried, and developed. I aim to creating a safe environment in which mentees feel that is acceptable to fail and learn from their mistakes. I do expect mentees to take ownership of their work and have accountability for their effort in the project. Overall, I want to do fun engineering work that makes a difference in human lives, and I want my students to feel empowered to do difficult things and solve challenging problems.

Additional Information

Link to Publications

https://rthmlab.wixsite.com/taylorgambon

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