UROP Project

Privatization in Education: A Comparative Analysis of Equity in Public and Private Schools in the United States and the Global South

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

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, 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

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

PFAS Contamination in the Lower Suwannee River Basin

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, 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

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

The Growth Paradox: Barriers to Scaling for Social Entrepreneurs of African Descent

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, 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 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

Entrepreneurship recovery after crisis: Setting Ukraine up for post-conflict success

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, 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 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

Improve Efficiency for Large Lanuge Models

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, 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

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.

Cognitive Skills Model for Predicting Alzheimer's Disease

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

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: remote
Research Assistant Transportation Required: No, the project is remote
Remote or In-person: Fully Remote
Approximate Weekly Hours: 7-10, 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

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.

Outreach Robots

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), 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

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.

The Role of Information Communication Technologies in Mitigating Age-Related Cognitive Declines

aging, technology, meta-analysis, computers, cognitive psychology

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: remotely
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:
Not participating in the roundtable

Project Description

Population aging and age-related cognitive declines present unprecedented challenges for the United States and the world. Social, cognitive, and activity engagement has the potential to protect against cognitive declines in middle-aged and older adults. Information communication technologies (ICT) can provide opportunities for all those engagements. Numerous cross-sectional and longitudinal studies suggest that ICT use in late adulthood is associated with cognitive benefits, while experimental studies providing ICTs and ICT trainings to older non-users showed mixed results. The current study aims to: (1) quantify the associations between ICT use and baseline cognition in cross-sectional and longitudinal studies, (2) quantify the protective effects of ICT use on cognitive changes in longitudinal studies, and (3) examine whether introducing ICTs to older non-users has cognitive benefits.

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

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

Parental Leave Policies in STEM

Parental Leave, STEM, Policies, Diversity, Engineering, Women in STEM, Robotics

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: 2
Relevant Majors: Open to all majors (slight preference for those in STEM fields, but really it doesn't matter to me!)
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), 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

As a female in academia who recently had a baby, I am curious about what current maternity leave policies are for universities around the United States and understanding how the FAMU-FSU policy compares. Eventually I would like to make strategic efforts to ensure that FAMU-FSU is a welcoming place for young female scientists to work. To prepare for this effort, I need to know what our policies are, what other university policies are, how our current policies are received by young female academics, and how our current policies could affect our ability to recruit and retain strong female faculty candidates in STEM. As a researcher on this project, you will help me to collect data along these lines, create/distribute surveys for women in STEM to voice their opinions, analyze results, and write a report and/or presentation for us to share our findings with members of the community/administration/etc. This is a project that I am personally very passionate about and that I hope will be a very rewarding project with tangible benefits for women and families in academia.

Research Tasks: Data collection via online research and surveys, data analysis, writing report on our results

Skills that research assistant(s) may need: Good communication skills - I need someone who is particularly good at sharing our findings with the larger community in order to make a difference in future policy decisions.
Good sleuthing skills - I need someone who is good at finding information online (figuring out the current parental leave policies at other universities), and who can find literature showing the effects of various leave policies on faculty recruitment and diversity.

Intent inference for lower-limb assistive robotics

Robotics, prosthetics, exoskeletons, human robot interaction

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: 2
Relevant Majors: Mechanical/Electrical/Biomedical Engineering, Computer Science
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), 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

Robotic lower-limb prostheses need to be able to determine what action the amputee is trying to accomplish in order to help them to achieve that goal. For instance, if the user is trying to sit down, the robotic knee joint must bend at just the right time to allow the sit action. This problem is called ‘intent recognition’. This project aims to leverage computer vision and human motion capture to improve intent recognition for these types of robots. In this case, we are collecting data as individuals go through normal activities of daily living so that we can then develop algorithms to recognize objects in the environment that the user is likely to interact with, and then reason about what these objects and their proximity tell us about the user’s intended actions.

Research Tasks: Data analysis, data collection, human subject research, programming

Skills that research assistant(s) may need: Matlab programming experience. Other languages of programming, such as Python and/or C++ are welcome, but not necessary. Human subject research.