UROP Project

HOT-KG: Harmonic Optimal Transport for Knowledge Graph Completion

machine learning; artificial intelligence; knowledge graphs; mathematics; link prediction

Research Mentor: Astrit Tola, Dr.
Department, College, Affiliation: Mathematics, Arts and Sciences
Contact Email: atola@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 students of all majors.
Students from the following programs may find the material connects especially well to their coursework: Computer Science; Mathematics; Statistics; Data Science; Scientific Computing; Computational Science; Physics; Electrical and Computer Engineering; Information Technology.
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

Almost every AI system you use every day depends on a knowledge graph, a huge collection of facts written as simple statements: Tallahassee - is the capital of - Florida. When you ask a voice assistant a question, when a search engine shows you a box of facts about a person, when a streaming service explains why it recommended something, a knowledge graph is doing the work behind the scenes. Hospitals and pharmaceutical researchers use them to connect drugs, genes, and diseases; banks use them to trace patterns of fraud.

The problem is that these graphs are always incomplete. Even the largest ones are missing an enormous number of true facts, simply because nobody ever wrote them down. So a central question in machine learning is: given the facts we already have, can a computer figure out which facts are missing? The goal of this project is to design and test a new approach to knowledge-graph link prediction. We will investigate whether the proposed model can identify missing facts more accurately than current methods, particularly for relations with multiple possible answers.



Research Tasks: The student will review recent knowledge-graph completion research from major AI conferences, maintain a shared reference library, and prepare short, plain-language summaries for discussion and use in the paper’s background section. They will also locate and reproduce public implementations of several established methods, troubleshoot the code as needed, and verify reported results. With my guidance and a provided mathematical specification, the student will implement and test the project’s core scoring function in Python using PyTorch and an optimal-transport library.

Skills that research assistant(s) may need: Recommended - Basic programming in Python

PRISM: Pseudo-Riemannian Ising Signed Model

machine learning; artificial intelligence; signed networks; mathematics; link prediction

Research Mentor: Astrit Tola , Dr.
Department, College, Affiliation: Mathematics, Arts and Sciences
Contact Email: atola@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 students of all majors.
Students from the following programs may find the material connects especially well to their coursework: Computer Science; Mathematics; Statistics; Data Science; Scientific Computing; Computational Science; Physics; Electrical and Computer Engineering; Information Technology.
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

Many real-world networks contain both positive and negative relationships. For example, users on an online platform may trust or distrust one another, countries may form alliances or rivalries, and genes may activate or suppress other genes. These are known as signed networks. When some relationship labels are missing, a machine-learning model can be used to predict whether those relationships are likely to be positive or negative.

Many existing methods rely on simple assumptions about how relationships behave, such as the idea that “the enemy of my enemy is my friend.” However, real networks do not always follow these patterns. People and organizations often have complicated or inconsistent relationships, and current models may perform poorly in precisely these difficult cases.

The goal of this project is to develop a new mathematical and machine-learning approach that represents positive and negative relationships directly within the geometry of the model. We will test whether this representation improves the prediction of missing relationship signs, especially when traditional assumptions fail. We will also investigate whether the model can provide a meaningful numerical measure of conflict within a network. The student will help implement the model, evaluate it on established signed-network datasets, compare it with leading existing methods, and analyze when and why it succeeds or fails.

Research Tasks: The student will review recent signed networks completion research from major AI conferences, maintain a shared reference library, and prepare short, plain-language summaries for discussion and use in the paper’s background section. They will also locate and reproduce public implementations of several established methods, troubleshoot the code as needed, and verify reported results. With my guidance and a provided mathematical specification, the student will implement and test the project’s core scoring function in Python using PyTorch.

Skills that research assistant(s) may need: Recommended - Basic programming in Python

How school Principals makes sense of School-community paetnership.

Qualitative research, School Leadership, Nepal

Research Mentor: Bhushan Dahal, He/him
Department, College, Affiliation: ELPS, Education, Health, and Human Sciences
Contact Email: bd22@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: Education.
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Fully Remote
Approximate Weekly Hours: 5 hours, 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: 2:00
  Zoom Link: https://fsu.zoom.us/j/92289504023
• Day: Thursday, September 3
  Start Time: 12:00
  End Time: 1:00
  Zoom Link: https://fsu.zoom.us/j/92289504023

Project Description

This research project examines how school principals in Nepal make sense of school–community partnerships. In Nepal’s public-school system, principals carry multiple leadership and administrative responsibilities. One of their most significant roles is serving as a bridge between the school and its community. Principals who serve as the secretary of the School Management Committee (SMC), a formal governance body mandated by national policy for all public schools. In this capacity, principals play a central role in facilitating communication, collaboration, decision-making, and relationships between schools and community members.

Prior research has explored school–community partnerships and various dimensions of principal leadership, limited attention has been given to how principals themselves interpret, understand, and enact their roles in these partnerships. This study tries to address that gap by examining principals’ sensemaking processes related to school–community partnerships in Nepal. The study will draw on Institutional Review Board (IRB)-approved data.

Research Tasks: We will do a literature review and data analysis.

Skills that research assistant(s) may need: Having some knowledge of research would be very helpful. Someone from Nepal would be ideal, if not someone from a South Asian country, who would have some familiarity with the context, as my research is in Nepal's setting.

Mapping the Local Food System: An Ethnographic Approach to Food and Community in Tallahassee

Food systems, Tallahassee, ethnography, sustainability, community research

Research Mentor: Dr. Burge Abiral, She/her/hers
Department, College, Affiliation: Anthropology, Arts and Sciences
Contact Email: ba25h@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:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 8, 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: 2:00
  Zoom Link: https://fsu.zoom.us/j/93974159075
• Day: Friday, September 4
  Start Time: 2:00
  End Time: 4:00
  Zoom Link: https://fsu.zoom.us/j/93974159075

Project Description

This project investigates how food is produced, distributed, and made meaningful in Tallahassee and the surrounding North Florida region. By focusing on the local food system, this research asks how farmers, nonprofits, policy organizations, food banks, and community gardens understand food-related problems differently, and what values and assumptions underlie those differences. Why does one organization talk about “food insecurity” while another talks about “food sovereignty”? What does “local” or “sustainable” mean to a small farmer versus a food policy council? These differences reflect different theories of what the problem is, who is responsible, and what a solution would look like.

This project builds the empirical and analytical foundation for a future undergraduate course on food systems. Research assistants will learn to map and describe the food system, and to read it as a site where social, economic, and political questions play out in everyday life. In doing so, they will gain hands-on experience in core skills of anthropological and qualitative social science research: close reading, interpretive analysis, organizational research, and, for interested students, introductory interviewing.

No prior research experience is required. Curiosity, attention to detail, and a willingness to look carefully at things that might initially seem straightforward matter most.


Research Tasks: Students will work on four interconnected tasks, moving from foundational mapping to interpretive analysis:
1. Food system mapping
Students will identify and catalogue key actors across the Tallahassee food system, including producers, distributors, emergency food providers, community organizations, and policy bodies. This produces a working database that the whole team contributes to and shares, which is a map of who is doing what, for whom, and with what stated goals.
2. Close reading of organizational materials
Students will analyze the public-facing materials of a selection of organizations, including websites, social media, annual reports, and mission statements, paying close attention to language and framing. How does each organization define the food problem? Who do they identify as responsible? Who do they speak for, and who remains invisible in their account? Students will produce short interpretive memos guided by structured prompts provided by the faculty mentor.
3. Comparative analysis
Students will bring their individual close readings into conversation with each other, identifying patterns, tensions, and silences across the food system. Where do different actors share assumptions? Where do their framings conflict? This stage emphasizes collaborative thinking and is structured around regular group meetings with the faculty mentor.
4. Introductory interviews (for interested students)
Students who are interested may conduct short, focused interviews with representatives from one or two organizations, asking how they define key problems and who they see as allies or obstacles in the food system. This component is optional and will be pursued with appropriate preparation and Human Subjects Research training.


Skills that research assistant(s) may need: Strong reading and writing skills: required
Curiosity and comfort with interpretive, open-ended questions: required
Familiarity with qualitative or social science research: helpful
Basic comfort with online research and document analysis: helpful

***Beyond the chronological age: The impact of subjective age on L2 motivation and working memory among L2 language learners aged over 60 years old

subjective age, L2 motivation, working memory,

Research Mentor: Dr Wenxiao Li ,
Department, College, Affiliation: Educational Psychology, Education, Health, and Human Sciences
Contact Email: wl20bb@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5, 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: 12:30
  Zoom Link: https://fsu.zoom.us/j/2958140910

Project Description

With a mixed-method design, this study therefore investigated how age identity influenced elderly’s L2 motivation (i.e, L2 self, ought-to L2 self, and anti-ought-to self) and (perceived) working memory. A questionnaire, adapted from established frameworks (Dörnyei, 2009; Thompson, 2021; Vallat-Azouvi et al, 2012), was distributed among 120 elderly L2 learner from China and the US respectively. Later, complementary qualitative data were obtained through semi-structured interviews. Noticeably, due to the different perspective of aging in various cultures, this study also involves a culture perspective, which was emphasized in the later semi-structured interview as well.
The regression analysis from the preliminary study showed that age identity outperformed chronological age in predicting both their L2 motivation and perceived working memory. The thematic analysis further indicated the crucial role of cultural factors on their motivation and approaches to manage learning-related challenges.

Research Tasks: data analysis

Skills that research assistant(s) may need: SPSS, basic statistic knowledge preferably in relation to regression and SEM

***Re-performing the Masquerade: Cultural Translation in a TikTok Comment Section

Yoruba masquerade; digital spectatorship; audience reception; cultural translation

Research Mentor: Lucy Nyambeki Oruta, She,Her,Hers
Department, College, Affiliation: Theatre, Fine Arts
Contact Email: orutalucy@gmail.com
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: Remote
Research Assistant Transportation Required: No, the project is remote
Remote or In-person: Fully Remote
Approximate Weekly Hours: 5, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

Re-performing the Masquerade: Cultural Translation in a TikTok Comment Section

Yoruba masquerade traditions such as Egúngún are not reducible to aesthetic display alone; they activate relationships among living communities, ancestral presence, memory, movement, rhythm, and sacred space. Their meanings emerge from a larger performance environment that includes performers, witnesses, spatial arrangements, cultural knowledge, and shared cosmological frameworks. When a recording of such a performance enters a social media platform—in this study, TikTok—it is separated from much of that environment and re-encountered through the platform’s compressed visual frame, soundtrack, caption, and public comment section.

The TikTok video anchoring this study offers one example of this displacement. Commenters attempt to make the masquerade intelligible by naming it as “spirit,” “demon,” “costume,” “culture,” or “entertainment,” while others respond with explanations, corrections, ridicule, or competing claims to cultural knowledge. These exchanges matter because they do not simply document audience reactions; they participate in producing the performance’s digital meaning and afterlife. This project therefore asks: How do TikTok commenters re-perform a Yoruba masquerade by naming, fearing, explaining, correcting, and culturally relocating what they see?

The project employs an interdisciplinary digital performance historiography that combines performance-centered audience reception with corpus-assisted text analysis. Drawing from Susan Bennett’s work on theatre audiences and Caroline Heim’s concept of the audience as performer, it approaches commenting spectators as active producers of meaning. The study does not claim to represent TikTok’s entire audience; it examines the visible, self-selecting performances of spectatorship produced by users who respond publicly to the video and to one another.

The available comments associated with the video will first be organized as a small digital corpus. Where appropriate, natural language processing and computational linguistic methods will be used to locate recurring terms, collocations, naming practices, and patterns of response. These tools will support the discovery and organization of patterns rather than determine their cultural meaning or reduce the comments to automated sentiment categories.

The computational mapping will guide the selection of approximately 10–15 substantive comment threads for close performance-reception analysis. The project’s central unit of interpretation will not be the isolated comment but the spectatorial exchange: an initial interpretation followed by replies that may correct, affirm, resist, mock, or redirect it. The original video will also be examined through its movement, costume, rhythm, sound, camera framing, duration, caption, and hashtags. Within the selected threads, the research team will attend to acts of naming, expressions of fear or fascination, claims to cultural authority, corrective explanations, and the acceptance or refusal of cultural knowledge. This movement between corpus-level patterns and close reading will allow the project to remain attentive both to recurring language and to the cultural, relational, and performative contexts in which that language appears.

Diana Taylor’s archive–repertoire framework will help the research team consider what happens when an embodied practice grounded in communal and ancestral transmission becomes a digital trace surrounded by new textual performances. The video preserves a trace of the masquerade, but the comment section also rescripts its meaning for spectators removed from its original cultural and performance environment.

Ultimately, this project investigates the digital afterlife of one performance: who acquires the authority to name it, how cultural knowledge is asserted or refused, and how spectators collectively remake what they have seen.

Research Tasks: Locate and summarize 10 key sources on Yoruba masquerade, audience reception, digital spectatorship, computational linguistics, and digital historiography using a structured format: claim, method, connection, question, and quotation.
Create a timestamped performance score of the TikTok video, documenting movement, costume, rhythm, sound, camera framing, captioning, hashtags, and significant visual transitions.
Collect, anonymize, and organize the available public comments and video metadata into a small, searchable digital corpus following appropriate research-ethics practices.
Use qualitative coding and, where appropriate, natural language processing to map recurring terms, collocations, naming practices, and response patterns; use these findings to select 10–15 spectatorial exchanges for close analysis.
Prepare two or three analytical memos and create visual representations of the findings for the collaborative FSU Undergraduate Research Symposium poster.

Skills that research assistant(s) may need: Required:
Interest in performance, cultural interpretation, digital media, or language.
Careful reading, clear writing, organization, and attention to detail.
Cultural humility and respectful engagement with African performance and spiritual traditions.
Willingness to work collaboratively across disciplinary approaches.

Recommended but not required:

Coursework or experience in natural language processing, computational linguistics, corpus linguistics, computer science, data science, or digital humanities.
Familiarity with Python, R, text-processing tools, spreadsheets, or data visualization.
Interest in applying computational methods to questions of performance, cultural memory, audience reception, and digital historiography.
Coursework in Theatre and Performance Studies, African or Africana Studies, Anthropology, Religion, Media Studies, or Linguistics.

Students are not expected to possess all these skills. The project is particularly suited to students interested in bringing computational and humanistic methods into conversation.

Development of High Magnetic Field Compatible Ultra-low Temperature Thermometers

Low Temperature, Physics, Cryogenics, Programming

Research Mentor: Andrew Woods,
Department, College, Affiliation: Maglab, N/A
Contact Email: awoods@magnet.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: Physics, Engineering, Computer Science
Project Location: Maglab, 1800 E Paul Dirac Dr, Tallahassee
Research Assistant Transportation Required: Tallahassee city buses service the lab.
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

With the increased focus on quantum computing, which often needs a combination of low temperature and high magnetic fields, there is increased need for robust thermometry in this challenging regime. I aim to develop fast thermometers based on quartz tuning forks that are compatible with temperatures down to 10 mK and fields in excess of 40 T. The technique has been validated experimentally, and therefore this project would focus on improving implementation, including designing and prototyping new enclosures and electrical circuits, designing and verifying measurement software, and working with partners in industry and academia to deploy prototypes and publish initial results.

Research Tasks: Data Collection, Data Analysis, Hands on construction, CAD design and 3D printing, Software Development and testing, Scientific Writing, Collaboration with Industry.

Skills that research assistant(s) may need: Python Programming Experience (Recommended)
AI Prompt Development (Recommended)
Hobby electronics experience (soldering etc.) (Recommended)
Hobby 3D Design and Printing (Recommended)
Not all are required by a single candidate.

Generative AI-Powered Virtual Reality Museum for Interactive STEM Learning

Generative AI, Virtual Reality, Artificial Intelligence, 3D Modeling, Human-Computer Interaction, Unity, Unreal Engine, Extended Reality (XR)

Research Mentor: Dr. Migara Dr. Migara Amarasinghe,
Department, College, Affiliation: Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering
Contact Email: migaraa@eng.famu.fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators: Dr. Bernadin Dr. Shonda Bernadin
Faculty Collaborators Email: bernadin@eng.famu.fsu.edu

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Computer Engineering, Electrical Engineering, Computer Science, Information Technology, or related STEM disciplines. Students from other majors with an interest in VR, AI, 3D modeling, Graphic Design or immersive learning are also encouraged to apply.
Project Location: FAMU-FSU College of Engineering
Research Assistant Transportation Required: Yes
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10 hours, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

This project investigates the use of generative artificial intelligence (GenAI) and virtual reality (VR) to create interactive and immersive environments for STEM education. Building upon an existing VR AI Museum developed using Unity/Unreal Engine and Meta Quest, the research will explore new approaches for presenting AI concepts through interactive virtual exhibits, AI-generated content, and intelligent virtual experiences.
Undergraduate researchers will contribute to the design, implementation, and evaluation of new museum experiences. Potential research directions include integrating generative AI into virtual exhibits, developing interactive AI-guided experiences, designing educational VR activities, and evaluating how users interact with and learn from AI-enhanced immersive environments.
The project will provide students with hands-on experience at the intersection of artificial intelligence, virtual reality, human-computer interaction, and STEM education. Research outcomes may contribute to demonstrations, undergraduate research presentations, and future scholarly publications.

Research Tasks: Conduct literature reviews on generative AI, virtual reality, human-computer interaction, and immersive STEM education.
Develop and modify interactive VR environments and exhibits using Unity/Unreal Engine.
Explore methods for integrating generative AI and intelligent virtual agents into the VR museum.
Design interactive educational activities and AI-focused museum experiences.
Develop or integrate 3D assets, user interfaces, and VR interactions.
Conduct experiments and usability studies to evaluate user interaction, engagement, and learning.
Collect, organize, and analyze experimental data.
Document research findings and contribute to research posters, demonstrations, technical reports, and potential publications.

Skills that research assistant(s) may need: Required:
Interest in artificial intelligence, virtual reality, or immersive technologies.
Willingness to learn new software and research methods.
Ability to work independently and collaboratively.

Recommended but not required:
Programming experience in C#, C++, Python, or a similar language.
Experience with Unity or another game engine.
Familiarity with generative AI or machine learning.
Experience with Blender, 3D modeling, or digital content creation.
Experience with Meta Quest or other VR/XR platforms.

Prior VR or AI research experience is not required. Students who are motivated to learn these technologies are encouraged to apply.

MFA Thesis: Memory, Material, and the Changing Body

Ceramics, Sculpture, Memory, Disability, Materiality, Feminism

Research Mentor: lc25h@fsu.edu Libby Couch, she/her
Department, College, Affiliation: Fine Art, Fine Arts
Contact Email: lc25h@fsu.edu
Research Assistant Supervisor (if different from mentor): she/her
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Fine Arts preferred, but open to all majors. Ceramic experience preferred
Project Location: On FSU Main Campus
Research Assistant Transportation Required: There are buses from the main campus to the Carnaghi Art Building
Remote or In-person: In-person
Approximate Weekly Hours: 5, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:

• Day: Friday, September 4
  Start Time: 7:00
  End Time: 8:00
  Zoom Link: Reschedule
• Day: Wednesday, September 2
  Start Time: 7:00
  End Time: 7:30
  Zoom Link: https://us06web.zoom.us/j/85417426635?pwd=E7WIIzmvwaooBxVRUwvdNga7UyHDYb.1
• Day: Wednesday, September 2
  Start Time: 7:30
  End Time: 8:00
  Zoom Link: https://us06web.zoom.us/j/85417426635?pwd=E7WIIzmvwaooBxVRUwvdNga7UyHDYb.1
• Day: Wednesday, September 2
  Start Time: 5:30
  End Time: 6:00
  Zoom Link: https://us06web.zoom.us/j/85417426635?pwd=E7WIIzmvwaooBxVRUwvdNga7UyHDYb.1

Project Description

This project is part of my ongoing MFA thesis research in Studio Art at Florida State University. The project investigates the relationship between embodied experience, memory, and the ways bodies are understood, perceived, and remembered. Drawing from research on mood-congruent memory and feminist approaches to embodiment, I will explore how the conditions of the present body influence what can be remembered and how bodily experiences are shaped by cultural expectations and perceptions of the body. Through ceramic reflecting pools, cast forms, glaze experimentation, printmaking, and decals, I investigate how material processes can evoke the instability, distortion, and incomplete accessibility of memory. I am also using 3D scanning to capture faces and ecological formations and translating these scans into clay forms, exploring how digital representations of bodies and landscapes can become physical objects. An undergraduate research assistant will contribute to scholarly and artist research, systematic material testing, documentation, and the development of an organized research archive.

Research Tasks: Test ceramic materials and glazes (This involves learning to apply glaze to ceramic pieces and firing them in a kiln.)
Assist with art studio fabrication, installation, and documentation
Research medical journals and scholarly publications pertaining to thesis research


Skills that research assistant(s) may need: Required:
Strong attention to detail and organization
Willingness to conduct research and read academic or artist-written sources
Ability to document and organize research findings

Recommended:
Interest or experience in studio art, ceramics, sculpture, photography, art history, psychology, disability studies, or related fields
Chemistry or geography background may help with glaze testing, but is not required.
Experience with academic research and summarizing sources

Science Prediction Market

experiment, hypothesis, biology, chemistry, economics

Research Mentor: Steven Lenhert,
Department, College, Affiliation: Biological Science, Arts and Sciences
Contact Email: lenhert@bio.fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 8, 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: 2:00
  End Time: 2:30
  Zoom Link: https://fsu.zoom.us/j/92271342903

Project Description

What is the difference between a good and bad science experiment? This is a question that was thoroughly answered by Karl Popper in the early 20th century. He came to the now widely accepted conclusion that good science involves falsifiable hypotheses, while pseudoscience is unfalsifiable. Popper explained the reason for this rule nicely in an article entitled, "Science as Falsification”.1 Briefly, the idea is that science should answer questions to which we do not already know the answer. Theories and hypothesis that have a chance to be proven wrong through experimental observation are therefore valuable contributions to our advancement of knowledge, while theories and hypotheses that have no risk of being wrong do not increase knowledge. This “demarcation line” so nicely distinguishes science from pseudoscience, that I propose to take it one step further – can we quantify the amount of knowledge gained by an experiment by making predictions and measuring how much of a chance it is perceived to have of falsifying a hypothesis? Furthermore, the predictive value of completed science is what makes technology possible.
This project will set up a new kind of scientific evaluation system.2 A scientist can propose a scientific question, hypothesis, and experiment to test the hypothesis. That scientist can provide possible outcomes of the experiment, along with proposed probabilities for each outcome. Reviewers can then predict the outcomes and wager points to indicate their level of confidence. The value of the knowledge to be gained by the experiment can then be gauged by how much wagering takes place, as well as what the odds are for different outcomes. Presentation of published experiments and results in this format can allow students to experience the scientific process of discovery and develop their scientific prediction skills.
References:
1 Science as Falsification, by Karl Popper
https://staff.washington.edu/lynnhank/Popper-1.pdf
2 Science Prediction Market
https://www.bio.fsu.edu/lenhertgroup/prediction_market.php


Research Tasks: The students will set up a prediction market using points within the Lenhert group and other interested students and researchers. Experiments from the scientific literature that are perceived to be relevant to current projects in the group will be identified. The experiments will be presented to the group without providing the results. Other group members will predict the outcome of the experiments and express their confidence in the outcomes using points. The market will be extended to real experiments in the Lenhert lab as well as other labs.

Skills that research assistant(s) may need: Required: Critical thinking; communication; basic understanding of science
Recommended: STEM, physics, chemistry, biology, finance, economics, business