Barrett Small Network Hero

Data Analytics and Mathematics

Explore the various projects below categorized under the general theme of Data Analytics and Mathematics. Be sure to return to the Barrett College Fellows Program main page and explore projects under the other 11 themes as well. You might be surprised at what you find and maybe you will discover the perfect research project for what you hope to study!

Please do not contact the research centers or faculty listed below directly (a formal application process is a required step to joining these research opportunities).

For questions about the Barrett College Fellows Program or specific research projects, please contact Dr. Sarah Graff at: BarrettCollegeFellows@exchange.asu.edu.

Projects with an asterisk (*) indicate projects still taking students. If you would like to apply for any of these projects, please contact barrettcollegefellows@asu.edu and fill out the student application.

Back to Barrett College Fellows main page

Research projects

Mass Spectrometry-based Metabolomics

Project # 8

Center name: The Gu Research Group
Campus/Location: Downtown
Faculty lead: Haiwei Gu

Project description

The Gu Research Group focuses on biomarker discovery and metabolic mechanism studies using mass spectrometry-based metabolomics.

Special skills needed

Mass spectrometry, bacterial culture, metabolite extraction, and genearl data analysis.

Majors

Biology, Analytical Chemistry, Statistics

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics

A Monte Carlo Comparison of the Efficacy of Mplus, flexMIRT, PROC IRT, ltm and mirt for Maximum Likelihood Estimation of Item Response Theory Models *

Project # 13

Center name: Dr. Yi Zheng's Research Team
Campus/Location: Tempe
Faculty lead: Yi Zheng

Project description

Dr. Yi Zheng and Dr. Mark Reiser from the School of Mathematical and Statistical Sciences are looking for a student to join our study entitled "A Monte Carlo Comparison of the Efficacy of Mplus, flexMIRT, PROC IRT, ltm and mirt for Maximum Likelihood Estimation of Item Response Theory Models." Item Response Theory (IRT) is a statistical framework used to analyze test or survey data. It provides accurate interval-level measures of each individual's latent trait level, enables rich analysis of individual items of the instrument, and enables the creation of adaptive tests ensuring comparability across different test versions. Several software packages have been created to estimate IRT models based on observed response data. Given the same data, though, different packages offer different results. This simulation study aims to test and analyze the performance of the various packages in a comprehensive array of realistic scenarios and provides guidelines to practitioners (for example, psychometricians, social science researchers) in terms of which program to use to analyze their data. We are looking for someone who is strong or a quick learner of statistical computing and programming.

Special skills needed

Strong in statistical computing and programming

Majors

Statistics, Mathematics, Data Science, Computer Science

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics

Exploring the Efficacy of Large Language Models (LLM's) for Conducting Large-scale Literature Review

Project # 28

Center name: Dr. Yi Zheng's Research Team
Campus/Location: Tempe
Faculty lead: Yi Zheng

Project description

This is an exploratory project is directed to Barrett Fellows proficient with computer programming and are interested in Large Language Models (LLM's). Using a sample of a couple of hundred research papers that have already been manually reviewed and labeled, we explore whether LLM's can effectively produce similar or even superior summaries of the papers. The ideal product of the project is a framework/process for effectively soliciting high-quality literature review summaries from LLM's. ASU research computing offers free access to the LLM machines on their supercomputers and the Barrett Fellows participating in this project will gain experience in using supercomputers and interacting with LLM's.

Special skills needed

Proficient with computer programming.

Majors

Computer Science

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics

Artificial Intelligence and Deep Learning for Medical Imaging

Project # 29

Center name: Imaging Informatics Research (Liang Lab)
Campus/Location: Fully remote
Faculty lead: Jianming Liang

Project description

This is a set of projects aimed at developing novel methods and systems for computer-aided diagnosis (CAD) empowered by artificial intelligence and deep learning (AI/DL) to support clinical decision-making and facilitate precision medicine via imaging. Our lab works on images of the brain, heart, lung, skin, eye, and abdomen diseases across modalities (X-rays, CT, MRI, PET, etc.), and Barrett Fellows may choose to focus on one particular condition at one specific modality based on interests. Our lab needs Barrett Fellows with strong programming skills in Python; good understanding of medical imaging, artificial intelligence, and deep learning; demonstrated commitment to conducting rigorous experiments to establish the SoTA baselines; and successful passing of a mini-course designed for beginners in deep learning and computer-aided diagnosis. Over the semester, Barrett Fellows will acquired a deep understanding of scientific research, paper writing, and publishing along with advanced technical skills in medical image analysis, artificial intelligence, deep learning, etc.

Special skills needed

Strong programming skills and a general understanding of machine learning.

Majors

Computer Science, Computer Engineering, Mathematics, Statistics, Biomedical Informatics, Software Engineering, Biomedical Engineering

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Engineering

Multimodal Medical Foundation Models (Artificial General Intelligence) for Medicine and Imaging *

Project # 30

Center name: Imaging Informatics Research (Liang Lab)
Campus/Location: Fully remote, ASU-Mayo Health Futures Center
Faculty lead: Jianming Liang

Project description

This project aims to develop novel methods in artificial intelligence (AI), artificial general intelligence (AGI), and deep learning (DL) towards multimodal medical foundation models (FM) (based on ChatGPT/GPT-4, Gemini/Bard, and LLaMA) to support clinical decision-making and facilitate precision medicine. Our lab works on the brain, heart, lung, skin, eye, and abdomen diseases across modalities (images, reports, videos, and audio), and Barrett Fellows may choose to focus on one particular condition at one specific modality based on your interest. Our lab needs Barrett Fellows with strong programming skills in Python; good understanding of medical imaging, artificial intelligence, and deep learning; demonstrated commitment to conducting rigorous experiments to establish the SoTA baselines; and successful passing of a mini-course designed for beginners in artificial general intelligence. Over the semester, Barrett Fellows will develop an understanding of scientific research, paper writing, and publishing and advanced technical skills in medical image analysis, artificial general intelligence, deep learning, etc.

Special skills needed

Strong programming skills and a general understanding of machine learning.

Majors

Computer Science, Computer Engineering, Mathematics, Statistics, Biomedical Informatics, Software Engineering, Biomedical Engineering

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Engineering

Modeling the Mechanics of 2D Materials

Project # 33

Center name: Dr. Maria Espanol's Research Team
Campus/Location: Tempe
Faculty lead: Malena Espanol

Project description

A graphene sheet is a single-atom thick macromolecule of carbon atoms arranged in a honeycomb hexagonal lattice. When observing a graphene sheet suspended over a substrate, moiré patterns appear driven by lattice and orientation mismatches. In this project, Barrett Fellows will perform molecular dynamic simulations using LAMMPS to replicate these patterns observed in real experiments.

Special skills needed

Programming skills, mathematical background, and some physics background is welcome (but not required).

Majors

Computational Mathematics, Physics, Computer Science, Engineering

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Engineering

AI for Medical Imaging and Diagnostics

Project # 34

Center name: Dr. Maria Espanol's Research Team
Campus/Location: Tempe
Faculty lead: Malena Espanol

Project description

In this project, we explore the use of machine learning algorithms to reconstruct images of the body for diagnostic purposes.

Special skills needed

Programming skills and a mathematical background.

Majors

Computational Mathematics, Mathematics, Data Science, Computer Science, Engineering

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Engineering, Health and Wellness

Testing Machine Learning Optimization Algorithms

Project # 42

Center name: Goldwater Center for Science and Engineering
Campus/Location: Tempe
Faculty lead: Johannes Brust

Project description

Under the supervision of Dr. Johannes Brust, Barrett Fellows will test and compare a set of unconstrained optimization algorithms that are widely used in machine learning (ML). What is different in this investigation is that the test problems are drawn from a standard optimization problem library. In such a setting typically classical algorithms perform well, and it the goal of this study to uncover the behavior of ML methods in this environment.

Special skills needed

Programming and Mathematics that includes linear algebra and basic multivariate calculus.

Majors

Applied Mathematics, Mathematics, Statistics, Data Science, Computer Science, Electrical Engineering

Years

2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics

Maricopa County Teacher Persistence Data *

Project # 48

Center name: Dr. Terri Kurz's Research Team
Campus/Location: Fully remote
Faculty lead: Terri Kurz

Project description

Teacher retention is a problem on a national scale. Teachers enter the field but do not persist, eventually leaving the profession. Teacher turnover is a problem in many communities throughout the nation but is particularly widespread in Arizona. Arizona teachers are more likely to say they will leave the profession, and data show they do, in fact, leave at one of the highest rates in the nation. Per year, approximately one out of every four Arizona teachers (24%) quit the profession or switch districts compared to a national rate of 8% (Sutcher et al., 2019). Using 10 years of data (2011-2021), Barrett Fellows will support the organization of data. The focus will be on tracking Maricopa County teachers’ persistence in the profession, specifically examining whether or not preparation plays a role in their persistence. All data are anonymous and provided by the Arizona Department of Education. The data are disorganized, messy and spread across multiple Excel documents. Barrett Fellows participation in this project will focus specifically on the organization of data of all teachers who entered the profession across that 10-year period.

Special skills needed

Any major that focuses on data is welcomed (a major in education is not required); specific skills needed include: 1) data analysis - the ability to analyze the data to understand its structure, patterns, and anomalies is crucial (this includes identifying missing data, outliers, duplicates, and inconsistencies); 2) problem-solving - a problem-solving mindset is essential to devise appropriate strategies to clean and organize the data (this involves breaking down complex data sets into manageable parts and developing logical solutions); 3) attention to detail - being meticulous in spotting errors, discrepancies, and patterns is crucial to effectively organize the data (small details can significantly impact the accuracy and reliability of the final results); 4) familiarity with Excel - proficiency in using data spreadsheets is necessary (these tools enable data sorting, filtering, and transformation, making it easier to organize and clean the data); and 5) finding enjoyment in working independently with data.

Majors

Any undergraduate major that focuses on data collection or analysis would best fit this project. Appropriate majors might include: Statistics, Data Science, Mathematics, Computer Science or Education (with a strong background in mathematics or STEM). Anyone with a strong STEM background who enjoys data might be a good fit for this project.

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Education

Spatial Mapping of Vegetation for Amazon Forest Management *

Project # 55

Center name: The Cadillo Lab
Campus/Location: Tempe
Faculty lead: Hinsby Cadillo-Quiroz

Project description

This project grounded in ecological theories is taking place in thirteen local elementary schools. Tutors are providing onsite tutoring in ELA and Mathematics. Half of the tutors will also be providing physical activity breaks during the tutoring sessions in order to determine if our hypothesis is supported that tutoring plus physical activity leads to significantly greater academic achievement than tutoring only. This large scale project can offer Barrett Fellows many different opportunities which could include, data entry and management, data analyses, and writing/grant writing with the research team.

Special skills needed

Have taken courses related to remote sensing, GIS, and knowing how projections work; knowledge of GPS principles; knowledge of a GIS environment (ArcGIS Pro, ArcMap, QGIS); have taken courses in statistics and data analysis involving classification methods; know the principles of how to train and validate a model; be proficient in Python or R, or some programming language with which they can generate replicable classification models.

Majors

Computer Science, Spatial Sciences, Geology, Biology, Conservation Majors

Years

2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Sustainability

The Science and Policy of Certifying Carbon Removal

Project # 62

Center name: Center for Negative Carbon Emissions
Campus/Location: Tempe, West, Fully remote
Faculty lead: Stephanie Arcusa

Project description

Prof. Arcusa is seeking Barrett Fellows interested in joining the fight to stop global warming. Carbon removal is a set of technologies that capture carbon dioxide from the air and stores it in rocks, oceans, vegetation, and soils. We cannot keep the Paris Agreement to stay below 1.5 or 2 degree C alive without carbon removal. This means a global carbon removal industry will need to be developed and two of the pressing questions are: how will we certify that carbon removal is taking place as claimed (that is, carbon accounting) and what policies will we need to put in place to sustain such an industry. This project has various opportunities depending on the interest of the Barrett Fellow. Topical questions that need answering span law, justice, business, communication, engineering, public policy, international trade, diplomacy, economics, sustainability, and earth science. Answers will have real world applications for a growing carbon removal industry.

Special skills needed

Curiosity.

Majors

Engineering, Business, Public Policy, Global Management, Economics, Law, Humanities

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Business and Entrepreneurship, Data Analytics and Mathematics, Engineering, Humanities, Journalism, Communication, and Mass Media, Law, Justice, and Public Service, Sustainability

Instrument Development for Nanopore Sensing *

Project # 65

Center name: Dr. Quan Qing's Research Team
Campus/Location: Tempe
Faculty lead: Quan Qing

Project description

A team of Barrett Fellows would work with the mentor to design an microfluidic/electronic system for delivering liquid sample to a microchip and record electronic signals from a molecular bridge within a nanopore sensor. Over the course of the semester, Barrett Fellows will learn 3D modeling and 3D printing, PCB board design and operational amplifier circuits. The project also involves Python programming for data acquisition and analysis.

Special skills needed

Motivation to learn new skills, experience in electronics, 3D modeling, and Python programming.

Majors

Physics, Electrical Engineering

Years

4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics

Collective Behavior in Honey Bees

Project # 72

Center name: Collective Logic Lab
Campus/Location: Tempe
Faculty lead: Bryan Daniels

Project description

Honey bees solve collective challenges on a daily basis. The aggregate work output of a colony is resilient and adaptive to environmental changes. Information exchange between bees is crucial to these collective outcomes. In this project, using existing honey bee tracking data, we will construct a classifier that predicts bee behavior based on where it is in the hive and which other bees it interacts with. This will eventually be developed into a computational model of bee decision-making. This project will involve data science techniques and coding in Python.

Special skills needed

Some degree of familiarity or desire to learn programming and data analysis in Python.

Majors

Open to all majors though students in Applied Math, Computer Engineering, Data Analytics, Computer Science, Physics, Biology may find this project particularly relevant

Years

2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics

Discovering Control Genes from RNA Expression Data

Project # 74

Center name: Collective Logic Lab
Campus/Location: Tempe
Faculty lead: Bryan Daniels

Project description

Inside each of your cells, a complicated choreography determines which proteins will be produced and how the cell will behave. This can be understood using Boolean networks: for instance, if gene X and gene Y are expressed, then this will cause gene Z to be expressed. Large networks of these interactions, essentially Boolean logic gates, define how cells respond to their environment or change into different cell types. Experimental data on the expression of genes in single cells is giving us greater insight into the logic of these networks. The goal of this project is to develop an algorithm to convert gene expression data to a prediction of which genes can most easily control a cell's fate. This project will involve data analysis and writing code in Python.

Special skills needed

Some degree of familiarity or desire to learn programming and data analysis in Python.

Majors

Open to all majors though students in Applied Mathematics, Computer Engineering, Data Analytics, Computer Science, Physics, Biology may find this project to be particularly relevant

Years

2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics

24h Behaviors Laboratory

Project # 80

Center name: 24h Behaviors Laboratory
Campus/Location: Downtown, Fully remote
Faculty lead: Matthew Buman

Project description

The 24h Behaviors Laboratory utilizes emerging technologies (including wearables, smartphone applications, and other novel devices) and health behavior change interventions to understand the dynamic interplay of sleep, sedentary, and more active behaviors, and how collectively these behaviors may be harnessed for health promotion and disease prevention.

Special skills needed

Attention to detail, excellent organizational and communication skills, a self-started, and a genuine interest in clinical research.

Majors

Population, Public Health, and Health Care Policy; Neuroscience; Movement Science; Medical Studies and Health Sciences; Nutrition; Healthy Lifestyles and Health Education; Neuroscience; Nutrition; Biomedical Informatics; Data Analytics

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Art, Architecture, and Design, Biological, Chemical, and Physical Sciences, Business and Entrepreneurship, Data Analytics and Mathematics, Engineering, Health and Wellness, Social and Behavioral Sciences

Zoonosis - Project 1

Project # 86

Center name: Dr. My V. T. Phan's Research Team
Campus/Location: Downtown
Faculty lead: My V.T. Phan

Project description

A many emerging infectious diseases are of zoonotic origin - that is, pathogen movement between host species. With climate change, we hypothesize that some infectious diseases, such as airborne and vector-borne diseases, may become more frequent or severe. Despite improvements in sequencing technologies and computational analyses, practical challenges remain for studies tracking virus transmissions at the one-health interface (for example, air sampling and dust studies and too little is known about zoonoses and mechanisms that control virus host switching). In Project 1, Barrett Fellows will learn and use computational methods to systematically catalogue and visualize what is known about virus transmission through dust/air particles (PM 2.5) and what practical challenges are presented for one-health genomic studies. Based on this survey, we will develop practical solutions for studies exploring virus transmission patterns.

Special skills needed

Bioinformatics, literature review, genomics, genetics, and virology.

Majors

Biology, Virology, Bioinformatics

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Health and Wellness

Zoonosis - Project 2

Project # 87

Center name: Dr. My V. T. Phan's Research Team
Campus/Location: Downtown
Faculty lead: My V.T. Phan

Project description

A many emerging infectious diseases are of zoonotic origin - that is, pathogen movement between host species. With climate change, we hypothesize that some infectious diseases, such as airborne and vector-borne diseases, may become more frequent or severe. Despite improvements in sequencing technologies and computational analyses, practical challenges remain for studies tracking virus transmissions at the one-health interface (for example, air sampling and dust studies and too little is known about zoonoses and mechanisms that control virus host switching). In Project 2, we will test the hypothesis that climate change will exacerbate virus emergence and transmission. In particular, Barrett Fellows will explore existing data of how weather has changed during recent years, will document population growth and urbanization and explore possible links of climate change with the (re)emergence of infectious diseases.

Special skills needed

Bioinformatics, literature review, genomics, genetics, and virology.

Majors

Biology, Virology, Bioinformatics

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Health and Wellness

Ecology and Evolution of Host-microbial Interactions in Wild Rodents across Madrean Sky Islands

Project # 95

Center name: Biodesign Center for Health Through Microbiomes
Campus/Location: Tempe
Faculty lead: Taichi Suzuki

Project description

Understanding the origins and transmission patterns of complex mammalian microbiomes is a fundamental question in microbial ecology, evolution, and human health. It has been reported that certain gut microbial species are inherited from parents to offspring across multiple generations, a process known as host-microbial codiversification. However, beyond primates, the exploration of mammal-microbial codiversification and the mechanisms sustaining such evolutionarily stable associations are still largely unknown. This project's aim is to investigate host-microbial codiversification in wild rodents across the Madrean Sky Islands, which span desert to forest environments. We are seeking Barrett Fellows interested in analyzing microbiome data from ecological and evolutionary biology perspectives. Students with prior experience in bioinformatics and phylogenetics are preferred, but those without such experience who exhibit independence, creativity, and a passion for ecology and evolutionary biology are encouraged to apply.

Special skills needed

Bioinformatics (R, Excel, Command Line) and a background in ecology, evolutionary biology, genomics, or microbiology.

Majors

Computer Science, Microbiology, Life Sciences

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics

Space and Economic Development: The Case of Arizona *

Project # 98

Center name: Space Governance Lab
Campus/Location: Tempe, Downtown, Polytechnic, West, Fully remote
Faculty lead: Timiebi Aganaba

Project description

While Arizona’s Aerospace and Defense Sector has a rich and prosperous history, it has operated in isolation from the state’s much larger and more impactful “5 Cs” economy. Iit is also isolated from the economic drivers of the state (real estate, mfg, health, retail). Space within Arizona is ill defined, a portfolio for commercializing space has to be clearly defined to apply resources (capital, workforce and focus) by key stakeholders. The state has an extensive and comprehensive structure to develop STEM-focused talent and there is potential for additional expansion and growth (JTED, Charter schools, 2-year institutions, etc.), but the aerospace and defense industries have a relatively small footprint in the state (Tucson and Phoenix areas) and as a result the messaging, decision-making, and process to integrate workforce to help grow this industry (including space) is limited. There are commercial opportunities within the government subcontracting sector, marketplace, and supply chain along with possibilities for investment that provide returns both in public and private markets. An Investment and Finance approach to economically developing space is recommended to enable economic growth utilizing space as a focus without compromising the other economic drivers in the state and our research (and the work of our Barrett Fellows) will help explore this hypothesis.

Special skills needed

Research, curiosity, attention to detail, and interviewing skills.

Majors

Economics, Data Analytics, Public Policy

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Law, Justice, and Public Service, Social and Behavioral Sciences

Researching Efficacy of Plastic Pollution Interventions

Project # 99

Center name: Conservation Innovation Lab
Campus/Location: Tempe, Fully remote
Faculty lead: Leah Gerber

Project description

The aim of this project is to predict the effectiveness of Hawai'i's interventions for reducing marine plastic pollution.

Special skills needed

Efficient research, model development, and data analytic skills.

Majors

Sustainability, Biological Sciences, Conservation, Mathematics

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Sustainability

Endangered Species Act: Species Status Assessments

Project # 100

Center name: Conservation Innovation Lab
Campus/Location: Tempe, Fully remote
Faculty lead: Leah Gerber

Project description

In this project, we are are investigating the type of scientific information in Species Status Assessments (that is, species ecology, life history, current conditions, future conditions) and which of these pieces of information is used in policy. Our goal is to figure out the knowledge gaps in applied conservation science, and what information policy makers need when designing laws, protections, and conservation actions for endangered and threatened species.

Special skills needed

Read "Species Status Assessment" to gather certain information via a Google Form, data analysis, and manuscript preparation.

Majors

Sustainability, Conservation, Mathematics, Biological Sciences

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Sustainability

Business and Biodiversity

Project # 101

Center name: Conservation Innovation Lab
Campus/Location: Tempe, Fully remote
Faculty lead: Leah Gerber

Project description

Our project involves the analysis of corporate sustainability responsibility (CSR) and other company reports, identifying the sustainability strategies adopted as it relates to biodiversity conservation.

Special skills needed

The position requires skills in data collection, organization, and analysis. Barrett Fellows will assist in the collection of company reports over a period of time, using global databases. These reports will be content analyzed, using a computerized text analysis program and perform statistical analysis including clustering and other theme-development methods. The position requires an independent worker with strong problem solving skills, a flexible approach to working and an acute attention to detail and depending on interest and ability, the position may entail some writing as well.

Majors

Sustainability, Conservation, Mathematics, Biological Sciences, Business Sustainability

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Business and Entrepreneurship, Data Analytics and Mathematics, Sustainability

Conservation Education

Project # 102

Center name: Conservation Innovation Lab
Campus/Location: Tempe, Fully remote
Faculty lead: Leah Gerber

Project description

Our Conservation Education Immersive Program works with and studies 6th through 12th grade students - researching their responses to a number of ecological and conservation affinity measures and analyzing how our program influences those attitudes and behaviors.

Special skills needed

Skills required include transferring paper surveys into a digital format (through Google Sheets). As part of this project, Barrett Fellow will gain exposure to quantitative measures in social science, experience with a conservation education research project, mentoring, participation in team meetings, and a letter of recommendation (with satifactory job performance).

Majors

Sustainability, Conservation, Education, Biological Sciences, Behavioral Sciences

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Education, Social and Behavioral Sciences, Sustainability

Actionable Science and Conservation *

Project # 103

Center name: Conservation Innovation Lab
Campus/Location: Tempe, Fully remote
Faculty lead: Leah Gerber

Project description

Our work attempts to understand the impacts of conservation interventions. Barrett Fellows will help our lab analyze survey data fom surveys collected during the summer 2022 measuring the impact of Species Distribution Models or SDM's as part of an overall conservation management plan. Barrett Fellows may also focus their work on a case study as how Conservation International uses science to inform their conservation decisions.

Special skills needed

Coding and analyzing social data, data collection, and project management.

Majors

Sustainability, Conservation, Mathematics, Biological Sciences

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Sustainability

Endangered Species Act: Recovery Planning Interviews

Project # 104

Center name: Conservation Innovation Lab
Campus/Location: Tempe, Fully remote
Faculty lead: Leah Gerber

Project description

The goal of this project is to illuminate similarities and differences in recovery goals for species listed under the Endangered Species Act and offer recommendations for a path forward in recovery planning collaborations. Our work involves qualitative research methods, including grounded theory and developing codebooks for themes - our hope is that this knowledge will help improve future government/NGO partnerships.

Special skills needed

Coding interviews using grounded theory, qualitative analysis, assisting with writing the manuscript, and potentially designing your own project using the data collected.

Majors

Sustainability, Conservation, Biological Sciences, Mathematics

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Sustainability

Targeting Agroforestry Expansion in the Peruvian Amazon *

Project # 105

Center name: Conservation Innovations Lab
Campus/Location: Tempe, Fully remote
Faculty lead: Leah Gerber

Project description

The aim of the project is to identify areas in the Peruvian Amazon where switching from regular agricultural practices to agroforestry schemes brings the highest conservation benefits while accounting for investment costs and protecting of the benefits of agriculture to local farmers. We are developing an optimization approach to find such areas with one of the final products will be a user interface so the potential investors of the agroforestry schemes can explore the results of our analyses.

Special skills needed

This project involves the development of a user interface (probably using R Shiny); skills needed include research experience along with data and cost analytics.

Majors

Sustainability, Conservation, Biological Sciences, Mathematics

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Sustainability

Measuring the Costs and Benefits of Conservation Planning and Interventions *

Project # 106

Center name: Conservation Innovation Lab
Campus/Location: Tempe, Fully remote
Faculty lead: Leah Gerber

Project description

Conservation planning and decision making needs to consider both the costs and the benefits of potential actions. However, estimating the expected costs of conservation interventions has proven difficult. We have been compiling a database of studies that report on the costs of conservation interventions. This project takes the next step and extracts action types and costs from these studies to populate a database of average costs of conservation interventions.

Special skills needed

This project involves extracting cost and action data from previous summaries and then compiling that information into a new database that the Barrett Fellow helps design; other skills needed include research experience, data analysis, decision making, and planning.

Majors

Sustainability, Conservation, Biological Sciences, Data Analytics

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Sustainability

Assessing the Role and Effectiveness of U.S. Ethnic Media’s Counter-misinformation Work *

Project # 108

Center name: Media Information, Data and Society Lab (MIDaS Lab)
Campus/Location: Downtown
Faculty lead: Hazel Kwon

Project description

This research project explores editorial counter-misinformation efforts by the U.S. ethnic media outlets. It will attempt to assess the effectiveness of these efforts and identify good practices for news outlets responding to the problem of mis/disinformation among ethnic communities. Misinformation has been persistent in Spanish-speaking communities in the U.S., while Black Americans were disproportionately targeted during the 2016 Kremlin-linked Internet Research Agency (IRA) disinformation campaign, as well as during the COVID-19 pandemic. In addition, disinformation narratives associated with religious fundamentalism, nationalism, far-right movements, and Chinese-sponsored influence operations have spread into Asian, Asian-American or Pacific Islander (AAPI) communities. Communities of color in the U.S. have displayed a strong affinity, trust, and solidarity with ethnic media. The existing trust relationship between ethnic media and their audiences suggests that ethnic media have the potential to serve as community hubs in countering mis/disinformation. However, there is limited research on the effectiveness of ethnic newsrooms’ counter-disinformation work. As part of the project, Barrett Fellows will assist Dr. K. Hazel Kwon’s research team to create an ethnic media database, overview the ethnic media ecosystem, and conduct case studies either qualitatively studying their websites or quantitatively/computationally analyzing text data (depending on the student's skillset).

Special skills needed

Required skills include familiarity with Excel; savviness using Google folder for team work; familiarity with basic descriptive statistical concept (e.g., mean, standard deviation, correlation, frequency analysis, etc.); skill using statistical analysis software (for example, SPSS, R, Python, etc.) or having a willingness to learn the software use as part of this project. Preferred skills include data science skills (for example, knowing how to use Python packages for text data analytics) and bilingual (Spanish or any Asian language).

Majors

Media, Journalism, Communication, Computer Science, Political Science, (Social) Statistics

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Journalism, Communication, and Mass Media

Proliferation of Lawyers as a Runaway Selection Process

Project # 109

Center name: Complexity Economics Lab
Campus/Location: Tempe
Faculty lead: Joffa Applegate

Project description

The industrial structure of the United States has changed dramatically over the last few decades, with the share of employment in manufacturing declining and those in professional services, including legal services, increasing. Daniel Markovits, in The Meritocracy Trap, explains through a dynamic where workers with more education change their jobs to include technologies that require increasingly higher degrees of education. While we agree that some form of niche construction is occuring, we suspect the explanation has more to do with a response to changing business trust and scale patterns rather than technology. Furthermore, this increase in legal services may also have a dampening effect on entrepreneurship and innovation, which suggests a runaway selection effect like that of the peacock's tail. Our project analyzes public employment data sources to craft a socially-oriented story around this increase and its consequences, employing evolutionary concepts such as niche construction and selection.

Special skills needed

Knowledge of R and statistical regression.

Majors

Economics, Business, Social Science

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Social and Behavioral Sciences

Developing a Productivity Picture of Maricopa County

Project # 110

Center name: Complexity Economics Lab
Campus/Location: Tempe
Faculty lead: Joffa Applegate

Project description

Productivity is commonly measured as GDP per worker hours, and since GDP and National Income are nearly identical, this is the same thing as measuring productivity as average wage. But this tell us nothing about what is being produced in an economy or how. Two hundred years ago Charles Babbage wrote "On the Economy of Machinery and Manufacturers" in which he detailed the production of contemporary industries by visiting factories, counting and describing the machinery used and how the workers spent their time, and the standard production outputs in terms of quantity and quality. This project will apply creative thinking and data sleuthing to collect a simlar dataset for modern Maricopa County.

Special skills needed

Knowledge of R; data sleuth and out-of-the-box thinking.

Majors

Business, Data Science, Economics, Engineering

Years

2nd-year students, 3rd-year students

Themes

Cross-listed with the following themes:

Business and Entrepreneurship, Data Analytics and Mathematics

Mechanical Tree Data Analysis

Project # 113

Center name: Center for Negative Carbon Emmissions
Campus/Location: Tempe
Faculty lead: Matt Green

Project description

The Mechanical Tree pilot plant in ASU Tempe campus is a successful demonstration of the carbon capture technologies that can play a key role in developing a circular carbon economy and eliminating fossil-based CO2 emissions. The pilot plant has an industrial automation system together with sophisticated analysis equipment, which generates a flow of substantial amount of real time data. The data collected presents an excellent opportunity to be a part of a team that aims to correlate scientific principles and process results at an industrially relevant scale for a technology important for the future generations.

Special skills needed

Data analysis and Python.

Majors

Chemical Engineering, Computer Science

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Engineering

Arizona Friends of Foster Children Foundation Analyses *

Project # 119

Center name: DataDevils
Campus/Location: Tempe
Faculty lead: Connor Sheehan

Project description

Working with ASU’s Office of University Affairs and Social Embeddedness, Dr. Sheehan seeks to collaborate with Barrett Fellows to conduct quantitative and qualitative analyses to evaluate the effectiveness of an intervention to improve outcomes for Arizona’s Foster Children. Arizona Friends of Foster Children Foundation (AFFCF) is a well-established organization entering its 40th year of operations. Since 2014, they’ve invested in a unique program, Keys to Success, that supports foster care kids as they transition into adulthood. The program, a first-of-its-kind in Arizona, provides activities related to career, education, employment for youth leaving the foster care system. The CEO is interested in the impact of the Keys to Success Program as well as which program elements were the primary drivers for that impact. AFFCF is also interested in documenting the program as a replication model. We are interested in working with Barrett Fellows to evaluate the effectiveness of this program.

Special skills needed

Research experience, statistical skills, and experience working with focus groups.

Majors

Any major in the Social Sciences or Liberal Arts

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Health and Wellness, Social and Behavioral Sciences

Enhancing scikit-bio

Project # 120

Center name: Biodesign Center for Fundamental and Applied Microbiomics
Campus/Location: Tempe
Faculty lead: Qiyun Zhu

Project description

Dr. Qiyun Zhu is leading a Department of Energy funded project to enhance scikit-bio (https://scikit.bio/), a renowned open-source Python library for bioinformatics. Scikit-bio offers a range of algorithms and data structures extensively utilized in biological data analysis. Our goal is to augment its capabilities, particularly in handling and interpreting large-scale, multi-layered biological data (multi-omics), crucial for unraveling the intricate interactions among organisms and the environment. Specifically, we seek to add and refine functionalities for 1) efficient processing of diverse data types, 2) seamless integration of multi-omic datasets, and 3) characterization and labeling (annotation) of biological elements. This opportunity is a gateway for Barrett Fellows to collaborate with leading bioinformatics experts and software engineers. Ideal candidates should have a keen interest in open-source scientific computing and a basic understanding of programming, preferably in Python. Students from diverse academic backgrounds who are enthusiastic about merging computing with biology are encouraged to apply. As part of this project, Barrett Fellows will receive hands-on experience in contributing to high-quality software, valuable mentorship, and skill development that is highly desired in both academic and industry settings. This experience will be a significant stepping stone for those seeking careers in bioinformatics, software development, or data science.

Special skills needed

Knowledge and skills of programming (preferably in Python) are highly desired; additional knowledge in fields such as Bioinformatics, Biostatistics, Molecular Ecology, Ecology, and Evolutionary Biology are a bonus to this project.

Majors

Open to any major.

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics

Sleep/Circadian Rhythm Disturbances and Chronic Pain

Project # 121

Center name: Biobehavioral Pain, Addiction, Sleep, and Momentary Experience (Bi-PAS ME) Rese…
Campus/Location: Downtown
Faculty lead: Chung Jung Mun

Project description

Join Dr. Mun's lab and contribute to an NIH-funded research project investigating the mechanisms behind multiple chronic pain conditions. Emerging scientific evidence points to sleep and circadian rhythm disturbances playing a significant role in the progression of chronic pain and psychological distress. In this study, we aim to recruit 300 participants with chronic low back pain and follow them for 12 months. Barrett Fellows will have opportunities to learn about sleep and circadian rhythm assessments using cutting-edge tools, such as ambulatory sleep EEG machines, 24-hour urine assessments, and smartphone and wearable devices. They will also have unique opportunities to work directly with clinical pain populations, as well as to gain hands-on experience in quantitative sensory testing that assesses somatosensory functioning, and conducting structured clinical interviews.

Special skills needed

Strong interest in research, attention to detail, effective communication, empathy, teamwork, critical thinking, and a commitment to research ethics.

Majors

Health Sciences, Medical Studies, Nursing, Public Health, Population Health, Personal Health, Applied Science, Psychology, Nutrition, etc.

Years

1st-year students (new to ASU Fall 2024), 2nd-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Health and Wellness, Social and Behavioral Sciences

Complex Networks and Motifs Census

Project # 122

Center name: Dr. Enrico Borriello's Research Team
Campus/Location: Tempe
Faculty lead: Enrico Borriello

Project description

In network theory, a motif census is a method for the systematic and categorical enumeration of various subgraphs within a network. Understanding the occurrence of motifs is pivotal in comprehending the intricate structure and dynamics of networks. Throughout this project, Barrett Fellows will immerse themselves in the process of describing complex systems as networks, followed by a thorough study of their topology to extract valuable insights about the system, comprehend, and predict their evolution over time. The study provides flexibility, allowing students to choose between analytical, computational, or a combination of approaches. Analytical exploration entails the development of expressions for motif census, while computational exploration focuses on the analysis of real-world network databases using Python. Barrett Fellows are encouraged to apply these ideas to their areas of interest. Additionally, they have the option to participate in an ongoing activity at the School of Complex Adaptive Systems, exploring the application of these concepts to biological networks as well as socio-economic networks.

Special skills needed

Either familiarity or desire to learn programming in Python (alternatively, basic knowledge in linear algebra for the more analytical proposed approach).

Majors

Students with interests in Applied Math, Data Analytics, Physics, or Biology may find this project particularly relevant

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Engineering, Humanities, Social and Behavioral Sciences, Sustainability

How Financial Incentives Improve Medication Adherence and Healthcare Outcomes: Evaluating the Wellth App

Project # 123

Center name: Dr. Chad Stecher's Research Team
Campus/Location: Downtown, Fully remote
Faculty lead: Chad Stecher

Project description

The Wellth app offers daily financial incentives to users for submitting a photo through the app of their prescribed pills in their hand. Research has demonstrated that the Wellth app is feasible, acceptable, and shows preliminary efficacy, but more work is needed to rigorously evaluate the effectiveness of this mobile health approach on medication adherence and subsequent healthcare utilization and outcomes. In 2021, the Wellth app was randomly offered to 3,300 AZ Medicaid enrollees with chronic conditions and who are nonadherent to their prescribed medications over the past year. Using AZ Medicaid insurance claims data for the population of 30,000 Medicaid enrollees who were eligible to receive Wellth (only 3,300 were randomly provided the app), we will estimate the effect of receiving the Wellth app on participants medication adherence, healthcare utilization and cost, and health outcomes. This project will provide invaluable experience working with health insurance claims data, running advanced econometric models, and writing a paper for publication in a top peer-reviewed health economics journal.

Special skills needed

Statistics, Econometrics, or regression analysis; familiarity with data analysis software (for example, SAS, Stata, R).

Majors

Economics, Statistics, Computer Science, Data Analytics

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Social and Behavioral Sciences

AI Second Observer for Detecting Colorectal Cancer

Project # 128

Center name: Banner MD Anderson Cancer Center
Campus/Location: Tempe
Faculty lead: John Chang

Project description

In this project, we are training a UNET to identify colorectal cancer on CT scans of the abdomen and pelvis. This project was born out of our prior retrospective review of the CT scan of the abdomen and pelvis where we found that up to 40% of colorectal cancer is not identified on initial scans. This results in delays in diagnosis and a decrease in 5-year survival. We hypothesize that an AI second observer can decrease incidences of missed diagnoses. Our preliminary findings show that a rudimentary AI UNET model can identify colorectal cancer up to 80%, but can have a large number of false positives. This model was trained with 51 cases and validated on 8 internal cases. We hypothesize that improving the training of model with 1) more diverse range of cancer stage in training cases, 2) more diverse range of diagnosis in training cases, and 3) more training cases with an attempt to identify a minimum number of cases with cancer annotation. Barrett Fellows will complete reimplementation of the AI UNET and convert the DICOM images into suitable format for training. They will also implement the experiments to test the hypothesis above on the new AI models. We hope to publish this work as dictated by the results of the experiments.

Special skills needed

Python coding skills and AI model knowledge are a plus.

Majors

Bioengineering, electrical engineering, computer science

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Engineering, Health and Wellness

Self-supervised Learning AI model for Detecting Colorectal Cancer

Project # 129

Center name: Banner MD Anderson Cancer Center
Campus/Location: Tempe
Faculty lead: John Chang

Project description

One of the major issue in AI model development is that supervised training requires some form of labeling. Although unsupervised training has also been applied to train model to learn imaging features from the unlabeled images, some form of labeling is required to teach the model the correct answer. Recent works has combined both to minimize model training (in this case, model training starts using ImageNet data followed by training on labeled specialized imaging data set). Unlabeled specialized images were then used to help the model further refine special imaging features. For our work, we want to 1) develop a standard, diverse set of specialized images to guide the model learning, 2) use unsupervised training for the model to learn imaging features, and 3) use the errors from predicting the classes of cases to guide updates of the model parameters. We will assess the rate of learning, precision, recall, and F1 score of the model after every 10 epochs. Barrett Fellows will code the model initially and will train the model using the unlabeled images for unsupervised training. We will also decide how many cases should be trained before checking on the model metric as described above and will assess the peak performance of the model.

Special skills needed

Python coding skills and AI model knowledge are a plus.

Majors

Bioengineering, Computer Science, Electrical Engineering, Mathematics, Data Science

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Engineering

AI Model for MR Spectroscopic Signal Reconstruction

Project # 130

Center name: Banner MD Anderson Cancer Center
Campus/Location: Tempe
Faculty lead: John Chang

Project description

We are developing an AI model to allow quick processing of the MR spectroscopy signal. This is expected to improve the MRS acquisition by rapidly removing background noise and reconstruct the MRS peaks without needing iterating the parameter space to reconstruct the MRS peaks. We have generated MRS peaks mixed with background noise and field inhomogeneity which decreases SNR and widens the MRS peaks. These factors can degrade the MRS peaks. However, AI reconstruction in other MR data acquisition has shown ability to yield excellent images even under the most accelerated conditions which typically has very low SNR. Barrett Fellow swill develop/modify the AI model and train the model from the above generated peaks to identify MRS peaks even with the acquisition is not perfect. When the model has achieved precision and recall to over 95%, we will test the model on human subjects.

Special skills needed

Python coding skills, knowledge of MRI and AI models.

Majors

Bioengineering, Physics, Electrical Engineering,

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics, Engineering

Coffee Value Chains in the U.S.

Project # 134

Center name: Food and Agribusiness Lab
Campus/Location: Tempe, Polytechnic, Fully remote
Faculty lead: Alexis Villacis

Project description

This project aims to conduct a comprehensive literature review on coffee production systems in the United States with a specific focus on the unique contexts of Hawaii and Puerto Rico. By synthesizing existing research and scholarly articles, we intend to provide a nuanced understanding of the challenges, innovations, and sustainability practices prevalent in these two regions. The review will delve into various aspects of coffee cultivation, including agronomic practices, environmental impact, economic considerations, and social dimensions. By scrutinizing the literature, we aim to identify key trends, gaps in knowledge, and potential areas for improvement within the coffee production systems of Hawaii and Puerto Rico. This research endeavor not only contributes to the academic discourse surrounding coffee agriculture but also serves as a valuable resource for stakeholders, policymakers, and industry professionals seeking to enhance the resilience and efficiency of coffee cultivation in these unique American landscapes.

Special skills needed

Analytical thinking, communication skills, attention to detail, time management, interdisciplinary perspective, and adaptability.

Majors

Business, Sustainability, Agribusiness, Supply Chain, other related fields

Years

3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Biological, Chemical, and Physical Sciences, Business and Entrepreneurship, Data Analytics and Mathematics, Humanities, Journalism, Communication, and Mass Media, Sustainability

Characterization of Meteorites and Planetary Materials

Project # 138

Center name: Buseck Center for Meteorites Studies
Campus/Location: Tempe
Faculty lead: Rhonda Stroud

Project description

Meteorites are samples of the building blocks of our solar system. Analysis of meteorites with optical, electron and x-ray microscopy can help researchers better understand the formation and evolution of materials in the Universe. Our center has a need for Barrett Fellows to collect and analyze data from meteorite and asteroid samples.

Special skills needed

Introduction to Physics, Chemistry, or Geosciences; Python coding skills.

Majors

Planetary Science, Geology, Astronomy, Physics, Chemistry, Computer Science

Years

2nd-year students, 3rd-year students

Themes

Cross-listed with the following themes:

Art, Architecture, and Design, Biological, Chemical, and Physical Sciences, Data Analytics and Mathematics

Open Criminology and Criminal Justice *

Project # 148

Center name: Open Criminology and Criminal Justice
Campus/Location: Downtown, Fully remote
Faculty lead: Jacob Young

Project description

Dr. Jacob Young is currently working with the Phoenix Open Data Portal to provide real-time infographics about crime in Phoenix. The project seeks to recruit students to help with developing questions that can be addressed with open data, assistance building workflows for answering these questions, and seeking to conduct research within this framework. The ideal Barrett Fellows are those interested in asking questions about crime (using open data to answer those questions) and working together as a team in a collaborative environment.

Special skills needed

Interest in crime and experience with programming (particularly using R).

Majors

Criminology and Criminal Justice, Sociology, Computer Science

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Law, Justice, and Public Service

Keep in School Shape (KiSS) Program *

Project # 156

Center name: Individual Faculty Project: Keep in School Shape (KiSS) Program
Campus/Location: Tempe, Fully remote
Faculty lead: Carla van de Sande

Project description

Data Science is emerging as one of the most sought-after career options in business, education, research, and government. Barrett students who take part in Dr. Carla van de Sande’s "Keep in School Shape" (KiSS) research program will be introduced to some core components of data science and will learn the basics of various software tools that are commonly used to conduct exploratory data analysis (EDA). The KiSS Program delivers mobile review activities daily to undergraduate students over academic breaks so that they do not forget what they’ve learned and need to maintain for their future coursework. Each review activity is housed in an online survey platform, allowing for the unobtrusive collection of data. Barrett Fellows will use this data as their sandbox for learning how to conduct EDA. The skills in data manipulation, visualization, and representation that students acquire while taking part in this research program can be readily transferred to other domains, for example, business intelligence, medical research, government, etc. In addition, Barrett Fellows who take part in this program will be certified to conduct research on human subjects, giving them a solid understanding of the ethics and regulations that govern working with human data.

Special skills needed

Interest in education and some proficiency with Excel would be helpful.

Majors

Mathematics Education, Computer Science, Data Science

Years

1st-year students (new to ASU Fall 2024), 2nd-year students, 3rd-year students, 4th-year students

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Education

Maternal Outcomes - Disparities

Project # 168

Center name: Dr. Roshini Moodley Naidoo's Research Group
Campus/Location: Tempe, Downtown, Polytechnic, West, Fully remote, The role is flexible regarding location
Faculty lead: Roshini Moodley Naidoo

Project description

The project will explore disparities in maternal outcomes through a patient perspective, using published data on patient experience. The aim is to explore associations between different outcome measures, to identify care gaps and to formulate responsive solutions.

Special skills needed

Self-directed and motivated, committed, analytical proficiency

Majors

Health Care majors, as knowledge of the health care ecosystem is important.

Years

3rd Year Students, 4th Year Students- Seniors, ASU Online Barrett Honors Students (fully remote work)

Themes

Cross-listed with the following themes:

Data Analytics and Mathematics, Health and Wellness