2017-2018 REU-W Projects

The following are research projects included in the CEWIT REU-W program for the fall 2017 and spring 2018 terms:

  • Big Data Analysis of Indiana Non Profit Organizations

    Student Researchers:

    Payton Goodman

    Payton Goodman
    Sophomore
    Political Science
    College of Arts and Sciences

    Katherine Stewart

    Katherine Stewart
    First Year Student
    Journalism
    The Media School

    Faculty Mentor: 

    Kristen Gronbjerg

    Kristen Gronbjerg
    School of Public and Environmental Affairs

    Project Description:
    We will be in the middle of processing a (hopefully) very large survey of Indiana nonprofits. There will be opportunities for students to directly experience the process of collecting, cleaning, processing, and/or analyzing survey data, along with the process of preparing summaries of survey findings for distribution to responding organizations and posting on the project website (see www.indiana.edu/~nonprof).

    Technological or Computational Component:
    Students will obtain experience with ACCESS databases (how we are managing contacts with responding organizations), statistical programs such as SPSS, SAS or STATA (how we will analyze the survey data), EXCEL (how we will produce tables and graphs for use in summary reports).

    Preferences:
    Comfort with EXCEL and numbers, strong teamwork skills, ability to work independently, careful attention to detail, critical thinking (e.g., ability recognize when something doesn't look right).

  • The Influence of Exposure to Odorants and Pheromones on the Behaviors of Mice

    Student Researchers:

    Alexa Akers

    Alexa Akers
    First Year Student
    Biotechnology
    College of Arts and Sciences

    Manpreet Kaur

    Manpreet Kaur
    First Year Student
    Neuroscience
    College of Arts and Sciences

     Abriana Warren

    Abriana Warren
    First Year Student
    Neuroscience
    College of Arts and Sciences

    Faculty Mentor:

    Sachiko Koyama

    Sachiko Koyama
    School of Medicine

    Project Description:
    Many animals communicate with each other's odors. This is called olfactory communication. For example, dogs sniff each other or some spots on the ground to collect information of others. Collected information will affect their behaviors and sometimes their physiological conditions. Recent studies have shown that some odorants from plants also affect our physiological conditions (for example, medical aromatherapy). In my project, we will investigate the influence of exposure to odorants and pheromones on the behaviors of mice. You will learn about mice biology, olfactory communication, you will have chances of handling mice, analyzing behaviors using computer software, and summarizing the results at the end.

    Technological or Computational Component:
    Using video image analyzing software, the student will analyze the behaviors of mice. The results will be recorded in Excel spreadsheet and evaluate with statistical analyses.

    Preferences:
    Some lab experience helpful, but not required.

     

  • The Intersection of Human Computer Interaction (HCI) and Privacy/Security

    Student Researcher:

    Morgam Brockman

    Morgam Brockman
    First Year Student
    Political Science
    College of Arts and Sciences

    Anna Casey

    Anna Casey
    Sophomore
    Informatics
    School of Informatics, Computing, and Engineering

    Faculty Mentor: 

    Sameer Patil

    Sameer Patil
    School of Informatics, Computing, and Engineering

    Project Description:
    The research projects in my lab are at the intersection of Human Computer Interaction (HCI) and privacy/security. Some examples include: information disclosure on social media, user experience of IU's two-step login, motivations and barriers for use of privacy/security tools (e.g., Tor), privacy permissions on mobile devices/apps/social media/etc., examining human and design factors that drive susceptibility to online deception (e.g., phishing/spear phishing, fake news), etc.

    Technological or Computational Component:
    All research projects in my lab involve examining the use of technology using a variety of empirical methods. Technologies we study include: social media, mobile devices, authentication techniques, online messaging, privacy/security tools, software development tools, email, Web, etc.

    Preferences:
    I prefer mentees who are curious, enthusiastic, creative, and willing to learn. I strongly encourage mentees to take ownership of the project and provide ample opportunity for them to develop leadership and independence.

  • Prospective and Current Students' User Experience with Customer Relationship Management (CRM) Technologies

    Student Researcher: Faculty Mentor:

    Shannon McGuire

    Shannon McGuire
    First Year Student
    Business
    Kelley School of Business

    Lesa Huber

    Lesa Huber
    School of Public Health

    Project Description:
    In this research, an undergraduate will work with a small faculty team to design a study measuring prospective and current students' user experience with customer relationship management (CRM) technologies. In addition to user experience, the study will include the effect of the CRM on student recruitment and retention efforts. CRM technologies are used in industries including academia to increase capacity and improve the user experience. In a university setting, a CRM technology can be used to help students on their academic journey by tracking and facilitating their progress from initial interest to graduation. The School of Public Health is the first School on the Bloomington campus to use Salesforce CRM for online student recruitment and retention. Internal and external stakeholders are interested in the results of this research. The undergraduate mentee will gain valuable research skills in user experience, data analysis research, and recruitment and retention processes. She will work with a small research team composed of faculty and doctoral students and have several opportunities for presentation and publication. She will also become proficient in the use of customer relationship management technologies, a valuable asset to any resume. 

    Technological or Computational Component:
    Salesforce is the leading innovative CRM technology across the globe for a wide range of industries. In a university setting, Salesforce social, mobile, and cloud technologies to connect information from students, their home school, and larger university systems. The student researcher will work within the Salesforce environment to connect with students and facilitate their progress in their academic journey. The student researcher will work with faculty and doctoral students to design and deploy a Salesforce user experience survey. The student researcher will also analyze data provided by Salesforce to assess the effectiveness of the technology in improving student recruitment and retention. Finally, working with the team, the student prepare a poster, oral presentation, and written article for publication on her work. 

    Preferences:
    No previous skills or knowledge is required.

  • Using Human-Centered Robotics and Telepresence Activities to Teach Scientific Principles and Technology Design to MS and HS Students

    Student Researcher: Faculty Mentor:

    Skye Huffman

    Skye Huffman
    First Year Student
    Secondary Education
    School of Education

    Cindy Hmelo-Silver

    Cindy Hmelo-Silver
    School of Education

    Project Description:
    Involving more students from urban and rural areas in science, technology, engineering and mathematics (STEM) fields and careers has been at the forefront of national education reform. Engaging these students in STEM activities relevant to their everyday lives is critical to increasing their motivation, interest, learning, and participation in STEM. This project addresses this need through engineering and computer science activities aimed at helping middle and high school students grasp the intricacies of scientific principles and technology design using a teaching and learning model that will integrate human-centered robotics and telepresence theme-based activities in a problem-based learning and systems thinking environment. 

    Technological or Computational Component:
    The project is performed in collaboration with colleagues at University of Alaska Fairbanks, and targets students in Alaska and Indiana. Human-centered robotics involves the development of robotics technologies and applications for everyday use while telepresence robots enable better communication, operation, and exploration across enormous distances. This dual strategy makes the proposed technological approach highly relevant to the daily needs of students in Alaska and Indiana. The research team has developed a curriculum that addresses the technical and societal aspects of the human-centered robotics and telepresence that underlie the engineering and computer science concepts students will learn about. For the past three years, middle and high school students have been actively engaged in a problem-based learning using two basic open-architecture platforms based on the Arduino microcontroller, and other telepresence platforms. Students were able to customize these platforms through design variations and with the addition of new sensors, actuators, program parts, and other technology-related functions. Hence, this project helped students develop technology-based products adaptable to peoples' daily environments, needs, and practices in some meaningful way, which in turn, could increase student's motivation and interest in STEM and STEM careers. Students working on the project will be involved in research existing scientific literature related to the project, developing and testing curricula for human-centered robotics education, working with robotic platforms used in the research, and analyzing data from several classroom interventions with the human-centered robotics from both Indiana and Alaska. 

    Preferences:
    Students should have good communication and collaboration skills and an interest in education and/or robotics and/or the relationship between society and technology. 

  • Affect of Posture While Watching Video Messages to Attention and Emotional Response

    Student Researcher: Faculty Mentor:

    Arden Floom

    Arden Floom
    First Year Student
    Media
    The Media School

    Rob Potter

    Rob Potter
    The Media School

    Project Description:
    How does the posture that a person is in while watching video messages might affect their attention to the messages and their emotional response to them? In this experiment some subjects watch the videos on an iPad with their neck tilted down at an angle (like everyone looks at their mobile devices on the bus or while sitting in the IMU). Other subjects will watch the same videos on the iPad with their neck in a neutral position (straight ahead). This is a project in the early stages, and the apprentice will help in all aspects as we attempt to gather pilot data for a grant submission. This project involves collecting and/or analyzing psychophysiological data associated with cognitive processing of media messages.

    Technological or Computational Component:
    The student will use a variety of psychophysiological data collection programs. Depending upon the project they choose, they will use Qualtrics, audio editing software, and experimental control software. If interested, data analysis techniques on SPSS or R may be a part of the year's project.

    Preferences:
    Interest in being a team player, but also willing to take initiative. The best experience is likely to be had by a student who doesn't mind interacting with new people during data collection. However, if students prefer not to interact with volunteer participants, we have plenty of data already collected that needs to be analyzed. Good time management skills are recommended, and the willingness to ask questions when you don't know what I'm talking about.

  • Robot-Assisted Language Learning: How the Timing of Non-Verbal Cues Affects Foreign Word Learning in HRI

    Student Researcher: Faculty Mentor:

    Lexi Floom

    Lexi Floom
    First Year Student
    Computer Science
    School of Informatics, Computing, and Engineering

    Selma Sabanovic

    Selma Sabanovicr
    School of Informatics, Computing, and Engineering

    Project Description:
    The work described below is affiliated with the R-House Human-Robot Interaction Lab, which is a collaborative research group that brings together faculty and students who study human-robot interaction (HRI). HRI is a field that explores how people perceive, respond to, and interact with robots, and how to better design robots so they can be used in everyday contexts, such as the home, work, education, or healthcare. If you are interested in such topics, we invite you to join us in our studies on the design and evaluation of robots to serve community goals and robots that can assist with second language learning. Robot-Assisted Language Learning: How the Timing of Non-Verbal Cues Affects Foreign Word Learning in HRI: The aim of this project is to test how non-native word learning is affected by the timing of non-verbal cues (gaze, joint attention, gesture) when performed by a robot tutor. Although there is general consensus that greater contingency leads to greater learning in human interaction, in robot-assisted learning, conflicting literature has emerged. An experiment will be devised to test whether this is due to a) varying perceptions of the robot, and/or b) leading (vs following) behavior in the low contingency condition, prompting increased joint attention and diminished distraction. Research activities for undergraduates on the project would include running participants through the study protocol, programming and controlling the robot in the study, and collecting, managing, and analyzing textual, audio, and video data. Along with the above mentioned research activities for each project, all REU students are expected to work with project team members to discuss study design, results, and implications, and attend regular lab and project meetings. There is a possibility for participating students to continue working with the group following the CEWIT REUW experience through other funding sources or course credit (e.g. National Science Foundation). 

    Technological or Computational Component:
    Our research investigates the connection between robots, as embodied computing technologies, and people. While working on the project, students would be able to become familiar with interactive robotic technologies, study how different aspects of robot design affect people's perceptions of and reactions to robots, work on controlling and programming robots, design robot prototypes, and help us develop design recommendations for future robotic technologies. We will also discuss the potential societal implications of the robotic technologies we are developing.

    Preferences:
    We do not require prior knowledge of the research area or particular skills (these can be learned on the job). We hope that you are enthusiastic about doing research and enjoy working with people and robots. If you are familiar with programming, you can contribute more to preparing and handling the robots in the studies. If you have experience with studies or work involving human subjects (e.g. interviewing for a school newspaper, running or participating in psychology experiments) or qualitative or quantitative data collection and/or analysis, that will give you some prior knowledge related to lab and field studies. Your duties on the project will be assigned depending on your prior knowledge, skills you want to develop further, and interests in particular aspects of the study.

  • Understanding Fundraising Mechanisms of Organizations Combating Human Trafficking

    Student Researcher: Faculty Mentor:

    Sophia Lahey

    Sophia Lahey
    Sophomore
    Public Policy Analysis
    Kelley School of Business

    Matthew Josefy

    Matthew Josefy
    Kelley School of Business

    Project Description:
    Human trafficking is a significant problem in today's world. Over 700 organizations have been founded in the U.S. to combat trafficking and modern-day slavery; over half of these have emerged in the last 10 years. My research seeks to build an understanding of these organizations, particularly how their various foci affect their ability to raise money from donors. Currently I am in need of an apprentice who desires to be involved in this research. Depending on their skills, the tasks may vary, but for instance we need to develop scripts where possible to scrape information from each organization's website. We also need to visualize and map the relationships between the organizations. Businesses are required to release a significant amount of information every year to the Securities and Exchange Commission. Advances in natural language processing mean that we can develop new automated tools for ""reading"" this material, converting the information into useful knowledge. For instance, we can consider the networks of relationships between executives of the company and their contacts at other companies. We would welcome you as a member to our team of 6-10 people who are working on mining these business filings, using a combination of Python and custom-built tools to parse and interpret the information.

    Technological or Computational Component:
    The technological component is to identify the most efficient ways of collecting data from websites in a consistent manner on 700 organizations. I already have gathered financial information and now am looking to collect more information about the organization's marketing approaches, the people involved in their organization, and any other information they use in seeking to raise money. A student with one of two profiles would be best able to contribute: 1) previous Python experience, or 2) previous experience visualizing large data sets. The technological component is natural language processing. We have a number of tools in process, including web retrievers that obtain information from the SEC EDGAR database, a parser to break the sentences into the components that are of interest to us, storage of the information, and mapping the relationships between the information we obtain. A student with a strong coding background would be most comfortable in joining the team. 

    Preferences:
    The most important skills are programming (particularly Python) and the most important quality is that the student is a self-starter, dependable, and able to document their work and communicate questions in a professional manner.

  • Data Mining Business Filings to the Securities and Exchange Commission

    Student Researcher: Faculty Mentor:

    Gracie Renfro

    Gracie Renfro
    First Year Student
    Computer Science
    School of Informatics, Computing, and Engineering

    Matthew Josefy

    Matthew Josefy
    Kelley School of Business

    Project Description:
    Businesses are required to release a significant amount of information every year to the Securities and Exchange Commission. Advances in natural language processing mean that we can develop new automated tools for "reading" this material, converting the information into useful knowledge. For instance, we can consider the networks of relationships between executives of the company and their contacts at other companies. We would welcome you as a member to our team of 6-10 people who are working on mining these business filings, using a combination of Python and custom-built tools to parse and interpret the information.

    Technological or Computational Component:
    The technological component is natural language processing. We have a number of tools in process, including web retrievers that obtain information from the SEC EDGAR database, a parser to break the sentences into the components that are of interest to us, storage of the information, and mapping the relationships between the information we obtain. A student with a strong coding background would be most comfortable in joining the team.

    Preferences:
    The most important skills are programming (particularly Python) and the most important quality is that the student is a self-starter, dependable, and able to document their work and communicate questions in a professional manner.

  • Evaluating Amazon Echo in Shared Environments Using Human Computer Interaction (HCI) Design Methods

    Student Researcher: Faculty Mentor:

    Evangeline Mattioli

    Evangeline Mattioli
    Sophomore
    Accounting
    Kelley School of Business

    Norman Su

    Norman Su
    School of Informatics, Computing, and Engineering

    Project Description:
    Digital personal assistants like the Amazon Echo are fast becoming a permanent fixture of our homes. However, the Amazon Echo is largely seen as a device that is used by only one person at a time. Users are also generally unaware of the privacy implications of the Amazon Echo. In this study, students will use human-computer interaction (HCI) design methods to deploy and evaluate the Amazon Echo in shared environments (e.g, apartments, dorms). Students will identify design opportunities to improve and extend the Echo's design. Students will gain experience conducting user studies with qualitative and quantitative methods. Following these users studies, students may design and evaluate prototypes. This would be an excellent project for anyone interested in HCI or UX (user experience). There is also a potential for co-authorship of a manuscript based on this project.

    Technological or Computational Component:
    Students will be evaluating the Amazon Echo. Students will also be involved in logging data collected by the Amazon Echo (e.g., user queries to the Echo). Lastly, students may use tools to design prototypes.

    Preferences:
    Students should be detail orientated and hardworking as well as comfortable recruiting and interviewing participants. Experience with scripting, programming, and/or HCI and design (e.g., graphic design, prototyping) is not necessary but a plus.

  • Motor Cortex Physiology in Healthy Human Subjects in Various Circumstances, Including after Sub-Concussive Head Impacts

    Student Researcher: Faculty Mentor:

    Courtney Siegel

    Courtney Siegel
    Sophomore
    Neuroscience
    College of Arts and Sciences

    Hannah Block

    Hannah Block
    School of Public Health

    Project Description:
    The motor cortex of the human brain is important for planning and executing movement. We will examine motor cortex physiology in healthy human subjects in various circumstances, including after sub-concussive head impacts, to better understand how changes in cortical excitability affect function. You would help a PhD student collect and process data using non-invasive brain stimulation and various behavioral tasks. This is an opportunity to gain experience with sophisticated technologies used in human behavioral and neurophysiology research. 

    Technological or Computational Component:
    This is a motor control neuroscience project involving data collection with sophisticated technology. The student will assist a PhD student and learn how non-invasive brain stimulation and behavioral research is done in humans. Specifically, the student will be trained to assist with transcranial magnetic stimulation (TMS), neuronavigation, and electromyography (EMG). She will monitor the EMG signal for involuntary muscle contractions, record TMS coil positions in Brainsight, and adjust the stimulator settings as instructed by the PhD student who will administer the stimulation. She will also learn how to instruct and monitor subjects doing the behavioral component of the experiment.

    Preferences:
    The student should be detail-oriented, organized, and responsible.

  • Universal Heath Care Coverage: Understanding Why Low Income Amercians Do Not Obtain Insurance

    Student Researcher: Faculty Mentor:

    Tonya Dickie

    Tonya Dickie
    Senior
    Liberal Studies
    College of Arts and Sciences

    Dan Sacks

    Dan Sacks
    Kelley School of Business

    Project Description:
    A major policy goal of the Affordable Care Act, as well as other legislation, is to achieve universal health insurance coverage among all Americans. Achieving this coverage has proven surprisingly difficult, however, despite a mandate to obtain coverage, and generous subsidies for low-income Americans to help make insurance affordable. This project seeks to understand why low-income Americans do not obtain insurance. It will explore the hypothesis that people do not get insurance because they have access to uncompensated care in the form of charity care and medical bankruptcy, which can act as implicit insurance. This project will have a heavy statistical computing component. 

    Technological or Computational Component:
    This project will use historical data and survey data sets to measure the association between availability of uncompensated care and uninsurance among low income Americans. The computational aspects of the project involve assembling and cleaning the data set, implementing estimators, and developing and maintaining replication code. The student would be involved in all aspects. The student would learn, with my supervision, the basics of project workflow and statistical computing with Stata.

    Preferences:
    The most important qualities are an interest in learning, an ability to work independently, and a willingness to experiment. Some knowledge of statistics or experience with programming is helpful but not required.

  • How Competitive Interactions Affect the Evolution and Coexistence of Species

    Student Researcher: Faculty Mentor:

    Raelyn Phillips

    Raelyn Phillips
    Sophomore
    Biology
    College of Arts and Sciences

    Farrah Bashey-Visser

    Farrah Bashey-Visser
    School of Informatics, Computing, and Engineering

    Project Description:
    Our lab focuses on how competitive interactions affect the evolution and coexistence of species. Our goal is to test theoretical models explaining the maintenance of genetic variation. Specifically, we work on bacteria that are insect pathogens and mutualistic partners of nematodes. These bacteria produce anti-competitor toxins that can kill closely related bacterial strains. We are characterizing the degree to which these toxins are beneficial in a competitive context, and in what ways their production can be costly in other contexts. We are also examining sequence variation among natural isolates these bacteria examine variation in toxin loci vis-à-vis diversity in other parts of the genome.

    Technological or Computational Component:
    I would be interested in a student who is keen to operationalize and test theoretical models of species competition. This would involve gaining facility with population ecological models and statistical approaches to parameterize these models. Alternatively, projects requiring students to develop bioinformatics skills are available.

    Preferences:
    Basic courses in biology, differential equations, statistics, bioinformatics and programming would be helpful, but are not required. Intellectual curiosity and a strong work ethic are a must. 

  • Human Computer Interaction (HCI) and Design Improvement to Enhance Data Visualization Tools for Non-Technical Users

    Student Researcher: Faculty Mentor:

    Annie Aguiar

    Annie Aguiar
    First Year Student
    Journalism
    The Media School

    Olga Scrivner

    Olga Scrivner
    School of Informatics, Computing, and Engineering

    Project Description:
    The research focuses on computer-human interaction and design improvement to enhance data visualization tools for non-technical users. This project includes a task analysis (what a future user might need and what steps are necessary for that task) and a user design analysis (how a computer and a human will interact, how optimal is the current design for various interactions). A student will be also introduced into R programming language and web-based interactive design for data visualization.

    Technological or Computational Component:
    The project is built on data visualization and data processing computational algorithms, written in R. Initially, the student will have the opportunity to learn about information visualization and text-mining via graphical user interface. After gaining knowledge about R coding, the student will participate in enhancing data visualization applications.

    Preferences:
    Some programming knowledge is a plus, and the willingness to learn new language (if unfamiliar with R). The student will be also asked to participate in some R Reading group meetings next year (based on the student's availability, of course).

  • The Impact of Medicaid Design on Hospital Investments in Quality

    Student Researcher: Faculty Mentor:

    Ailish Cornwell

    Ailish Cornwell
    First year student
    Human Biology
    College of Arts and Sciences

    Victoria Perez

    Victoria Perez
    School of Public and Environmental Affairs

    Project Description:
    This project will involve using statistical analytical software to assess the impact of Medicaid design on hospital investments in quality. The purpose of this project is to shed light on uncertainties regarding how states should best contract out the provision of public insurance. We will be using novel data, assembled by the Centers for Medicaid and Medicare Services specifically for this project. There is a wide margin for the student to consider avenues of investigation independently in this area. There is considerable uncertainty about how to best managed private plans within Medicaid and this paper would identify which aspects of contracting and reimbursement matter most for patient quality and public finance outcomes.

    Technological or Computational Component:
    The majority of data cleaning and analysis will be done in Stata. There is also the possibility of coding in Python to webscrape patient views. The student would be given sample code and instruction with which to address the study's questions.

    Preferences:
    An understanding of Stata and Latex would be helpful, but not necessarily required.

  • The Biomechanics and Mechanisms of Running Related Overuse Injuries

    Student Researcher: Faculty Mentor:

    Natalie Kosnik

    Natalie Kosnik
    Sophomore
    Biology
    College of Arts and Sciences

    Allison Gruber

    Allison Grube
    School of Public Health

    Project Description:
    The biomechanics and mechanisms of running related overuse injuries has been investigated for over 30 years but the risk of developing these injuries remains high. The chance of experiencing a running related injury is like flipping a coin - approximately 50% of runners will experience at least one injury per year. There are many known risk factors for running injuries including running mileage, skeletal malalignment, and the loading to the body that occurs every time the foot makes contact with the ground. Cumulative loading - the total amount of loading that occurs over a single bout, a week, or lifetime of running - has become a new focus for biomechanics researchers investigating the mechanisms of running injuries. Previous studies have quantified that running more than 20-40 miles per week may significantly contribute to the development of an overuse running injury. However, running experience may play an important role in the threshold of running volume that causes a running injury. Preliminary data from the IU Biomechanics Laboratory suggests that lifetime physical activity history and current running training load may explain why novice runners experience an increased rate of injuries than more experienced runners. A long history of being physically active may protect an individual from experiencing a future injury because their tissues have adapted to handle greater loads than those who have not been physically active in their lifetime. However, not integrating appropriate rest periods within current training programs may lead to tissue degradation and injury due to cumulative micro-damage. The IU Biomechanics Laboratory is conducting three studies in this area of research currently. Your role as a CEWiT-REU research assistant will be to assist with collecting three-dimensional running gait data, examine physical activity patterns, and monitor weekly training volumes of participants enrolled in these studies.

    Technological or Computational Component:
    The student mentee will assist with three-dimensional motion capture data collection and processing. The three-dimensional motion capture system in the biomechanics laboratory is the same technology used to put real people into video games and put characters like Groot into movies. The student mentee would also assist in using software programs to build models of the human body based on the participants measured in the lab. The student mentee will also assist with examining the data collected by training logs and the data obtained by the FitBit activity monitor. These technologies are essential for anyone wishing to pursue careers in clinical gait and movement analysis, biomedical engineering, ergonomics, sports equipment and design, sports performance, motor vehicle safety and many other fields within biomechanics.

    Preferences:
    A successful undergraduate student working in the Biomechanics Laboratory will have a strong work ethic, good organization skills, and is able to troubleshoot and juggle a number of tasks at once. A moderate level of computer skills and experience is desirable. Strong math skills relating to algebra and trigonometry or higher is required. Experience with MATLAB or other code writing programs and courses in anatomy and physiology and physics are desirable but not required.

  • Technology Development for Aiding Discordant Chronic Comorbidities (DCC) Patients Manage Treatments

    Student Researcher: Faculty Mentor:

    Gabrielle Cantor

    Gabrielle Cantor
    Sophomore
    Intelligent Systems Engineering
    School of Informatics, Computing, and Engineering

    Patrick Shih

    Patrick Shih
    School of Informatics, Computing, and Engineering

    Project Description:
    1 in 4 American adults has 2 or more chronic conditions. Discordant Chronic Comorbidites (DCC) is a term that describes having multiple chronic conditions with differing treatment instructions. This study aimed to identify problems faced by DCC patients, and begin to develop a technology to aid them in successfully managing their treatments.

    Technological or Computational Component:
    The student could (1) understand stakeholders' technical needs using qualitative methods, (2) design scenarios and low fidelity mockups for evaluation, (3) develop apps, platforms, or hardware for deployment study, or (4) re-appropriate existing commercial technology for deployment study.

    Preferences:
    Requirement: (1) need to be interested in technology and its impact on the society, and (2) enjoys interacting with people and animals. Optional skills that may be useful but not necessary: (1) ability to program smartphone apps, (2) ability to program Arduino / Raspberry Pi platforms, or (3), ability to program augmented/virtual reality apps.

  • Developing Intelligible Pronunciation in Learners of English as a Second Language

    Student Researcher: Faculty Mentor:

    Zoie Hancock

    Zoie Hancock
    Sophomore
    East Asian Language and Cultures
    College of Arts and Sciences

    Isabelle Darcy

    Isabelle Darcy
    College of Arts and Sciences

    Project Description:
    Goal: This project's goal is to compare the efficacy of various pronunciation instruction methods in English as a second language (ESL) classes, in order to better understand the mechanisms behind how second language (L2) learners acquire pronunciation skills. This project is at the crossroads of cognitive science, psycholinguistics, and second language acquisition. Rationale and background: The goal of teaching pronunciation is to help L2 learners achieve accurate pronunciation in any communication situation (high or low stakes), in order to be comfortably intelligible (= speaking in a way that most listeners, both native and nonnative speakers, can understand without too much effort or confusion). Developing comprehensible and intelligible speech in a L2 is particularly crucial in settings where learners need to integrate into society personally and professionally, such as in the case of ESL learners who live in an English-speaking environment (Derwing, 2008). However, intelligible pronunciation is difficult to learn, and difficult to teach. There is little guidance and no well-established systematic way of deciding what to teach, and when and how to do it (Derwing & Foote 2011). Research data are not yet sufficient to allow us to say which method of teaching pronunciation is most effective, yielding measurable improvement over a short period of time, and allowing students to transfer what is learned in class to "real life" situations, that is, when using the language in various communication contexts outside of the classroom. We also do not know which method yields truly durable improvements that remain visible after the end of the course. Method: The experimental method that I anticipate using during the course of the project is a controlled "pre- and post-test" design, where we assess and compare performance before and after instruction, in order to evaluate the efficacy of the different ways of teaching. Student learning outcomes, technological and computing skills: While working with me, the student will learn about various pronunciation teaching methods, and understand the psycholinguistic underpinnings of these methods in terms of second language speech development. She will also learn how to design a controlled research protocol involving human participants, how to develop a research question, how to generate and test experimental hypotheses and their predictions. The project involves speech recordings and speech analysis, some computer programming to design assessment tools with which we will measure improvements in speaking and listening performance, and statistical analysis. The student will also become familiar with a variety of practical computing and analysis skills to conduct successful laboratory phonology experiments.

    Technological or Computational Component:
    While working with me, the student will learn about various pronunciation teaching methods, and understand the psycholinguistic underpinnings of these methods in terms of second language speech development. She will also learn how to design a controlled research protocol involving human participants, how to develop a research question, how to generate and test experimental hypotheses and their predictions. The project involves speech recordings and speech analysis, some computer programming to design assessment tools with which we will measure improvements in speaking and listening performance, and statistical analysis. The student will also become familiar with a variety of practical computing and analysis skills to conduct successful laboratory phonology experiments. The student will be involved in all aspects of the research.

    Preferences:
    Ideally, the student would have some background knowledge in second language acquisition. I would also like someone who is reliable, organized, and enthusiastic about the project.

  • I CAN PERSIST STEM Initiative Among Minority Women

    Student Researcher: Faculty Mentor:

    Briana Hollins

    Briana Hollins
    First Year Student
    Informatics
    School of Informatics, Computing, and Engineering

    Kerrie Wilkins-Yel

    Kerrie Wilkins-Yel
    School of Education

    Project Description:
    Mentee will assist with the development and implementation of the I CAN PERSIST STEM initiative. A research program designed to promote STEM persistence among minority women. Specifically, students will assist with the development of project-based learning experiences that expose high school girls of color to the real world application of STEM concepts. ***Here STEM refers to programs such as the Sciences (physics, chemistry, biology), Technology (computer science and informatics), Engineering, and Mathematics. 

    Technological or Computational Component:
    The project-based learning (PBL) component of this initiative will require the development of 3-4 projects that are related to any one or a combination of STEM fields. Mentee will gain hands on experience with developing these (PBL) activities. Mentee will also gain teaching and leadership skills as she guides high school girls of color through the developed PBL activities.

    Preferences:
    Strong communication skills, willingness to work in a team setting, dependable, conscientious. Preferably enrolled in an academic major in the sciences (physics, chemistry, biology), technology (computer science and informatics), engineering, AND/OR mathematics.

  • FitBit Data Analysis

    Student Researcher: Faculty Mentor:

    Leah Wentzel

    Leah Wentzel
    First Year Student
    Epidemiology
    School of Public Health

    Stephanie Dickinson

    Stephanie Dickinson
    School of Public Health

    Project Description:
    I manage the Biostatistics Consulting Center and we work with faculty on various research projects doing methods, tools, and data analysis. The specific project I have in mind is assisting with data management/analysis for a large dataset of Fitbit data. The research team has collected Fitbit data every minute and every second for an entire year for about 30 participants who joined the study. The participants are all regular runners (10 miles a week), and the researchers are looking at their patterns of activity and rest. Ultimately the researchers are studying how their running patterns relate to possible injuries.

    Technological or Computational Component:
    The technical components of this project have multiple pieces. We are using Python to “scrape" the participant data from the Fitbit site, and then push it into a MySQL database. Then we use R to import the data from MySQL, and we have created an R Shiny app online so the team can visualize and interact with the data. The student could work on any or all of these pieces. They would have to be able to code in Python, MySQL, or R, and learn the pieces they don't know to implement the task. Each piece is currently in place, but it needs improvement to be fully functional, and the project is flexible where we can grow in any of the areas that the student is interested. For example, the Python code pushes the Fitbit data to MySQL, but it could be improved to check for duplicates in the records first. Or we could change how often to run the Python code to pull in new data, etc. The student would have to do independent research to learn the relevant pieces of code needed and be comfortable with trial and error! A separate task is available if a student is skilled in helping to set up a virtual server to host the Shiny app.

    Preferences:
    In addition to Python, MySQL, or R, the student should be motivated to learn and try new things, have an analytical mindset, enjoy problem-solving, be curious, and also be considerate of others while working in a team.

  • Data-Driven Analysis of User-Generated Book Reviews Posted to GoodReads.com.

    Student Researcher: Faculty Mentor:

    Audrey Lee

    Audrey Lee
    Sophomore
    English
    College of Arts and Sciences

    Vivian Halloran

    Vivian Halloran
    College of Arts and Sciences

    Project Description:
    My research question is: Does Emma Watson's "Our Shared Shelf" virtual book group hosted by GoodReads.com function like an Massively Open Online Course (MOOC)? To help answer that, the student will help determine whether and how book group participants describe their learning process about feminism within their GoodReads.com book reviews and posts in response to structured prompts. We will use data-mining of both reviews and responses posted by GoodReads.com users who have joined OSS. We will also use content grouping and narrative analysis of the assembled data set, the student and I will be able to assemble a list of the self-reported learning outcomes users mention most often. 

    Technological or Computational Component:
    We'll use data-mining of both book reviews and responses to post posted by GoodReads.com users who have joined OSS. We will also use content grouping and narrative analysis of the assembled data set to determine the self-reported learning outcomes users mention most often.

    Preferences:
    Familiarity with Python, and/or data mining experience if possible, but not required.

  • Effect of Online Visibility in Success/Failure in Professional Life Using Twitter

    Student Researcher: Faculty Mentor:

    Kulsoom Tapal

    Kulsoom Tapal
    First Year Student
    Informatics
    School of Informatics, Computing, and Engineering

    Rakibul Hasan

    Rakibul Hasan
    School of Informatics, Computing, and Engineering

    Project Description:
    My current research project is to analyze and predict the effect of online visibility in success/failure in professional life. In particular we are looking at twitter data and trying to see the effect of posting frequency, tweet types, number of followers etc. in helping the user achieve higher professional goals or ruining the his/her career.

    Technological or Computational Component:
    This project will involve collecting and analyzing twitter data using tools from data science and machine learning domain. It will also involve manually verifying profiles in social media websites of sub-sampled population for who we will collect data. A basic knowledge of python programming language will help.

    Preferences:
    Curious mind, basic python knowledge, helpful, but not required.

  • Investigation of the Elusive Nature of Interior Atmosphere by Leveraging Digital Fabrication, Material Construction, and Augmented Technologies

    Student Researcher: Faculty Mentor:

    Grace Murphy

    Grace Murphy
    First Year Student
    Environmental Science
    School of Public and Environmental Affairs

    Jiangmei Wu

    Jiangmei Wu
    College of Arts and Sciences

    Project Description:
    Per Gestalt psychology, when we enter an interior space, what is first and immediately perceived is neither the subjective sensation nor shapes, colors, or objects, but rather, atmosphere. Spatial atmosphere is part our everyday vocabulary, words such as "warm", "cozy", "uplifting", "hectic" are often used to describe the atmosphere of an interior that is generated by either physical space or people. However, atmosphere is also an elusive philosophical and aesthetic concept that is difficult to define and capture. German philosopher Gernot Böhme has also expanded on spatial atmosphere by exploring the relationship between the embodied subject and the perceptible surrounding space. For Böhme, the atmosphere is not objective, and yet it is tangible and perceptible, articulating the quality of things, in interior lighting and in furniture and finishes, as they are felt and sensed by the subjects' bodily presences in space. Spaces of atmosphere that are shaped through the engagement of subjects' bodily presences and senses are closely linked to the philosophy of phenomenology, an area that has been explored by architects such as Peter Zumthor, Steven Hall and Juhani Pallasmaa, and others. As architectural spaces "lose their plasticity and their connection with the language and wisdom of the body, they become isolated in the cool and distant realm of vison." And yet every touching experience of spatial atmosphere is multi-sensory, and must met "equally by the eye, ear, nose, skin, tongue, skeleton and muscle." Compared to architectural design, interior design is more capable of creating an encapsulating and calculated environment that has the potential to evoke a deep sensory experience. Interior atmosphere was the focus of research by a group of academics, theorists, interior architects, architects, and industrial and furniture designers whose studies suggested several new directions for expressing interiority and atmosphere. These new directions in expressing the elusive concept of interior atmosphere include sensory aspects of material construction and augmented technologies, the experience of interior space and its site, and the temporal qualities of exhibition and installation. This research project aims to investigate the elusive nature of interior atmosphere by leveraging digital fabrication, material construction, and augmented technologies. It focuses on how augmented technologies can be used to enrich our senses and perception by engaging bodies as the overall affects that constitute the interior atmosphere; how to design augmented atmosphere in which the experiences of various roles of the bodies in space, either as spectators or as performers, are fully considered; and finally how to seamlessly integrate the augmented technologies with the material construction to create an all-embracing augmented interior. Research activities for undergraduates on this project include discussing research design, making digitally fabricated (laser cutting or 3D printing) prototypes, working in augmented design environments such as projection mapping, sensors, and processing, and others. 

    Technological or Computational Component:
    Research activities for undergraduates on this project include discussing research design, making digitally fabricated (laser cutting or 3D printing) prototypes, working in augmented design environments such as projection mapping, sensors, and Processing, and others.

    Preferences:
    Self-motivated and responsible. For technical skills, I prefer someone with some programming experience and are comfortable working with technologies such as digital sensors, etc., but not required.

  • Measuring Serotonin Levels in Male Mice when Exposed to Female Vocal Signals

    Student Researcher: Faculty Mentor:

    Ellen Engro

    Ellen Engro
    First Year Student
    Jacobs School of Music

    Christopher Petersen

    Christopher Petersen
    College of Arts and Sciences

    Project Description:
    The neurotransmitter serotonin has been implicated in various social, affective, and sensory disorders, many of which co-occur in affected individuals. One hypothesis as to the why these disorders tend to be comorbid is that psychopathologies rarely result from a perturbation at a single neural locus; rather, they are emergent properties of dysfunction across broadly interconnected circuits. However, little is known about how serotonin affects neural processes at the circuit level. In order to address this question, we designed an auditory playback study where male mice with either increased or decreased levels of serotonin were exposed to female vocal signals. As mice rely on vocal signals to convey contextual information during social encounters, this project tests the hypothesis that serotonergic manipulations will affect patterns of neural activity across the brain's social behavior network within the limbic system. To this end, students will learn immunohistochemistry and fluorescent microscopy in order to visualize protein markers for neural activation within the brain. Further, students will employ quantitative techniques to analyze expression of these markers across various regions in the amygdala and hypothalamus.

    Technological or Computational Component:
    Students will gain hands on experience with immunohistochemistry (IHC), a process which includes making solutions, calculating various dilutions, and working with previously cut mouse brain tissue. Students will be trained on a well-established IHC protocol in order to label mouse brains for Fos, a protein marker for neural activation. In order to visualize Fos labeled neurons, students will use the Leica SP8 scanning confocal microscope within the IUB Light Microscopy Imaging Center located in Meyers Hall. This process includes helping to optimize microscope settings, identifying specific regions in the amygdala and hypothalamus by characteristic landmarks, and taking high-resolution photomicrographs within which we can analyze Fos labeled neurons. Finally, students will employ automated and/or manual cell-counting techniques in order to quantify the number of Fos labeled neurons between control mice, and mice who have had their levels of serotonin pharmacologically increased or decreased.

    Preferences:
    Knowledge: Basic college-level or AP courses in biology, chemistry, and/or Neuroscience strongly preferred. Skills: Motor coordination for tissue work... I have found a background in music or sports helps but is absolutely not required! Qualities: Reliable, self-motivated, and patient... in that order. 

  • Investigation of the Role of Serotonin in Perception of Female Rejection in Mice

    Student Researcher: Faculty Mentor:

    Elizabeth Gonzalez

    Elizabeth Gonzalez
    Sophomore
    Human Biology
    College of Arts and Sciences

    Kayleigh Hood

    Kayleigh Hood
    College of Arts and Sciences

    Project Description:

    In order to navigate social communication, humans and animals must be able to place cues in their appropriate context. While context is an important factor in interpreting social cues, little is known about how context is encoded in the brain. To a male mouse, the squeaks of a female indicate that the females will reject the male only during the first ten minutes of the interaction, while later squeaks do not predict future female behavior. The timing of these squeaks is likely encoded in the male brain by the serotonergic system which responds to changes in social environment and female rejection in auditory regions of the mouse brain. We will investigate the role of serotonin in perception of female rejection cue by pharmacologically changing serotonin in the auditory regions of the brain and then measure male response using behavioral and electrophysiological methods.

    Technological or Computational Component:
    The students will be directly involved in designing and performing experiments that examine the role of serotonin in the vocal courtship of male mice. As a part of these experiments they will learn to use Avisoft audio recording and editing software as well as learn how to perform behavioral experiments and administer drug treatments. Additionally students will learn how to collect, stain, and categorize epithelial cells to monitor reproductive state and become proficient in using excel, R, and SPSS software to collect, manage, and analyze the data they will collect during their experiments.

    Preferences:
    The students will be directly involved in designing and performing experiments that examine the role of serotonin in the vocal courtship of male mice. As a part of these experiments they will learn to use Avisoft audio recording and editing software as well as learn how to perform behavioral experiments and administer drug treatments. Additionally students will learn how to collect, stain, and categorize epithelial cells to monitor reproductive state and become proficient in using excel, R, and SPSS software to collect, manage, and analyze the data they will collect during their experiments. I would prefer a student that is independent, motivated and is interested in communication or animal behavior.

  • Business Earnings Target Analysis

    Student Researcher: Faculty Mentor:

    Calista Furlano

    Calista Furlano
    First Year Student
    Accounting
    Kelley School of Business

    Bree Josefy

    Bree Josefy
    Kelley School of Business

    Project Description:
    Have you ever tried to shoot an arrow at a moving target? Or a target that gets bigger and/or smaller every time you go to shoot? This is how some companies feel during earnings season. "Earnings season" occurs once every quarter when companies announce quarterly accounting information (i.e., net income, revenues, etc). Missing an earnings target can be very detrimental to a firm's market value while meeting or beating an earnings target can increase the company's share price and shareholders' net worth. An important question, then, is what happens when the earnings targets are not precise? In this project, we will examine some of the determinants of earnings targets (i.e., how do analysts and investors determine what the earnings target should be) and whether/how firms meet earnings targets that are not precise or clear. During the project, you will write data gathering programs using SAS and data analysis programs using SAS or STATA. 

    Technological or Computational Component:
    In this project, students will use SAS and STATA to write programs that will gather company's financial information and analyze relationships among the data.

    Preferences:
    Knowledge of at least 1 programming language - skills can be transferred to SAS and STATA programming. Curiosity and motivation to learn about companies and accounting information.

  • Building a Neurocognitive Model of Central Language Properties in Native and Non-Native Speakers

    Student Researcher: Faculty Mentor:

    Rose Poplawski

    Rose Poplawski
    First Year Student
    Neuroscience
    College of Arts Sciences

    Laurent Dekydtspotter

    Laurent Dekydtspotter
    College of Arts & Sciences

    Project Description:
    Research apprentices will join pilot research on building a neurocognitive model of central language properties in native and non-native speakers. National Science Foundation funding has been sought. The attainment and performance of non-native speakers have long been compared to those of natives and the subject of intense debate. Representations, real-time computations, and neural bases have been argued to distinguish native from non-native language. Our research addresses all three levels. Electroencephalography (EEG), with the digitization of head shape/EEG sensors combined with structural and functional MRI scans of each subject, will be used to reliably estimate the sources of neuronal activity linked to crucial processing moments. This will shed light on the role of brain physiology in a central aspect of language, with repercussions for understanding real-time hemisphere interactions with a level of granularity hitherto not done with possible repercussions in the area of bilingualism in health and aging, Languages of research focus are French, English and Spanish.

    Technological or Computational Component:
    Undergraduate research trainees would be mentored in EEG and MRI laboratory techniques, in research design and implementation software, and in data preprocessing and analysis programs such as MATLAB, EEGLAB and/or SPM12 software (Fieldtrip) running under MATLAB, as well as R.

    Preferences:
    An interest in languages, ability to work in a group.

  • The Impact of Technology in the Group Development of the Afro Beat Dancing Trio, the CEO Dancers'

    Student Researcher: Faculty Mentor:

    Rose Poplawski

    Clare Chapman
    First Year Student
    Marketing
    Kelley School of Business

    Laurent Dekydtspotter

    Betty Dlamin
    School of Global & International Studies

    Project Description:
    My research interest is Theatre for Development, whereby theater includes performance arts such as songs, dances, drama and more. In this project, I focus on African Diaspora Dance by young women, and my special case study is the UK based dance trio, CEO Dancers, who are the pioneers of Afro beat dancing in the western world since 2010, and who have grown to be opening acts in BET Awards and British awards, and dancing with and for renowned artists such as Rihanna, Drake to name a few. The research focuses on the role and impact of technology in the group's development since 2010. As the dance group is based in the UK, the research will utilize technology. The project will involve interviewing, transcribing and analyzing data. I have already started the interview process with some of the artists involved in the development of CEO Dancers.

    Technological or Computational Component:
    We will use technology to conduct the interviews, and we will utilize the CEO Dancers' Youtube videos for more analysis.

    Preferences:
    The four communication skills of speaking, listening, reading and writing.

  • Research, Design and Development of 'Bunch'- An Octopus-Like Robot for Community Interactions

    Student Researcher: Faculty Mentor:

    Jayla French

    Jayla French
    Sophomore
    Neuroscience
    College of Arts and Sciences

    Selma Sabanovic

    Selma Sabanovic
    School of Informatics, Computing, and Engineering

    Project Description:
    The work described below is affiliated with the R-House Human-Robot Interaction Lab, which is a collaborative research group that brings together faculty and students who study human-robot interaction (HRI). HRI is a field that explores how people perceive, respond to, and interact with robots, and how to better design robots so they can be used in everyday contexts, such as the home, work, education, or healthcare. If you are interested in such topics, we invite you to join us in our studies on the design and evaluation of robots to serve community goals and robots that can assist with second language learning. Research, design and development of 'Bunch'- An Octopus-like Robot for Community Interactions: We conceptualized an octopus-like table top social robot to bring individuals in a community together by allowing multi-user interactions and plug and play activities. Specifically aimed at elderly users in the context of retirement centers, the robot would encourage community play and promote collective expression by allowing users to control its movements and responses for a certain time, thus also encouraging frequent interactions. For the elderly, it would provide hands on sensory and cognitive activity and a medium to express their emotions, while for the community it would mean dynamic representation of its members. The project is in its early stages with long term potential for research and development. The design of the robot would benefit from interviews and participatory activities with elderly residents, to be conducted in one of the retirement communities in Bloomington, IN. Activities for the REU would include building an early prototype of the robot, assisting in conducting research interviews and workshops with older adults and their caregivers, and collecting, managing, and analyzing textual, audio, and video data. Along with the above mentioned research activities for each project, all REU students are expected to work with project team members to discuss study design, results, and implications, and attend regular lab and project meetings. There is a possibility for participating students to continue working with the group following the CEWIT REUW experience through other funding sources or course credit (e.g. National Science Foundation).

    Technological or Computational Component:
    Our research investigates the connection between robots, as embodied computing technologies, and people. While working on the project, students would be able to become familiar with interactive robotic technologies, study how different aspects of robot design affect people's perceptions of and reactions to robots, work on controlling and programming robots, design robot prototypes, and help us develop design recommendations for future robotic technologies. We will also discuss the potential societal implications of the robotic technologies we are developing.

    Preferences:
    We do not require prior knowledge of the research area or particular skills (these can be learned on the job). We hope that you are enthusiastic about doing research and enjoy working with people and robots. If you are familiar with programming, you can contribute more to preparing and handling the robots in the studies. If you have experience with studies or work involving human subjects (e.g. interviewing for a school newspaper, running or participating in psychology experiments) or qualitative or quantitative data collection and/or analysis, that will give you some prior knowledge related to lab and field studies. Your duties on the project will be assigned depending on your prior knowledge, skills you want to develop further, and interests in particular aspects of the study.

  • Substance Abuse Disorder and Evidence-Based Treatment Methods

    Student Researcher: Faculty Mentor:

    Kat Zoeller

    Kat Zoeller
    Sophomore
    Public Policy
    Kelley School of Business

    Jody Madeira

    Jody Madeira
    Maurer School of Law

    Project Description:
    We are creating an interactive multimedia program to educate college students at Indiana University's Health Center about substance abuse disorder and evidence-based treatment methods. This program will include a series of video modules on alcohol, marijuana, opioids, and stimulants. It will also include a health tracking application that can be used on smart devices (tablets, phones, etc.) that will allow students to record health patterns related to mood, cravings, and other elements. This is the first module of its kind to be created. The research apprentice will help to compile and evaluate existing research on similar applications, as well as to analyze data from the project throughout the following year, and collaborate on research papers for publication credit.

    Technological or Computational Component:
    The programs that we are creating are being programmed for both computer and smart devices, and they will include a web portal that allows researchers and professionals to view data. Students will analyze existing research concerning similar applications, assist with evaluating the effectiveness of this technology, and develop ideas to improve these applications. The ultimate goal is to use this as a pilot project to develop a similar application for community treatment settings.

    Preferences:
    Attention to detail, ability to work independently with guidance, curiosity, creativity, interest in how technology can be used to solve social problems.

  • Bankruptcy Cases of Non-Profit Organizations

    Student Researcher: Faculty Mentor:

    Ritika Mehta

    Ritika Mehta
    First Year Student
    Finance
    Kelley School of Business

    Pamela Foohey

    Pamela Foohey
    Maurer School of Law

    Project Description:
    Every year, over 150 non-profit organizations in the United States file bankruptcy. These organizations include hospitals, YMCAs, museums, orchestras, sports complexes, housing cooperatives, and utility cooperatives. Prior to this research project, there has been no systematic study of the bankruptcy cases filed by non-profits. This project will entail helping to create a database with key information about the universe of non-profits that have filed bankruptcy since 2006. To create this database, research apprentices will be required to devise strategies to search Bloomberg's legal database for relevant court records, to search Guidestar's database for relevant history of the non-profits, and to leverage the Internet generally to complete background checks on the non-profits. Research apprentices initially will enter this information into an Excel workbook with multiple worksheets as based on a pre-defined coding structure. Research apprentices later will have the opportunity to learn how to import the information in the workbook into STATA (a data analysis and statistical software) and complete basic statistical calculations in STATA. The results of this project will be published primarily in law reviews.

    Technological or Computational Component:
    This project almost exclusively is computational and technological. Students will be required to devise strategies to search legal databases (Bloomberg Law primarily), as well as will learn how to complete a thorough background check on a non-profit organization using Guidestar and other Internet tools. All information initially will be entered into Excel based on a coding scheme. This coding scheme itself is based on the coding scheme that has been used by the Consumer Bankruptcy Project for more than a decade. In searching databases for key information and inputting based on the coding scheme, students will learn the limits of these online resources, as well as be able to see how they can be improved with currently available technology. All information initially will be entered into Excel, and the data will be imported to STATA for analysis. It is anticipated that students will have the chance to complete statistical calculations in both Excel and STATA.

    Preferences:
    Attention to detail. Interest in non-profit organizations specifically or businesses generally. Interest in accounting or finance.

  • Integrating human factors into cyber security and cyber security risk modeling

    Student Researchers:

    Kailen Dobias

    Kailen Dobias
    First Year Student
    Computer Science
    School of Informatics, Computing, and Engineering

    Rebekah Gottwald

    Rebekah Gottwald
    First Year Student
    International Studies
    School of Global and International Studies

    Faculty Mentor:

    Diane Henshel

    Diane Henshel
    School of Public and Environmental Affairs

    Project Description:
    Cyber security is a major global concern as cyber attackers and hackers are penetrating networks and clouds, where much of our information is manipulated and stored.  With the increasing intensity and volume of attacks each year, it is important to build robust models capable of assessing cybersecurity risk dynamically. The current state of risk modeling frameworks does not consider human attackers and defenders as risk altering factors, which are potentially essential predictive components of a cybersecurity risk model. We are incorporating attackers and defenders into dynamic risk modeling frameworks, using techniques such as Bayesian belief networks.  Students will learn about human behaviors and society, and apply that work to understanding propensity to behave maliciously in a social or cyber context.  The student will work with the team to identify possible variables to use in characterizing human interactions with cyber networks, and will be trained in some statistical approaches to analyzing data.

    Technological or Computational Component:
    The student will be learning statistical tools and approaches, and will develop an understanding of metrics (what they are, what qualities characterize good metrics) and how to work with and analyze them.  The metrics of interest could be human focused (such as characterizations of human behaviors) or more cyber focused (how to best measure what is happening in a network), depending on the interest of the student.  The student can also choose to learn or expand their programming skills. This work will be guided by both more experienced students and faculty.

    Preferences:
    A willingness to learn is the top quality we look for. Someone who is willing to try to work across disciplines, as all of our work is cross-disciplinary. Don't be afraid to say you don't know and ask for help, but we will also ask for you to independently work through some of the problems so that you stretch your mind, skills, and creativity.

  • Understanding and characterizing the factors affecting adverse impacts on humans or ecosystems within a large, extended watershed.

    Student Researcher: Faculty Mentor:

    Madison Howell

    Madison Howell
    First Year Student
    Law and Public Policy
    School of Public and Environmental Affairs

    Diane Henshel

    Diane Henshel
    School of Public and Environmental Affairs

    Project Description:
    CThe world, especially the climate and weather, are changing more rapidly than predicted. At an eco-regional level, such as when considering impacts on a large watershed, many aspects of the watershed have the potential to be (and are being) impacted, and these different aspects are monitored and evaluated under the rules and approaches of disparate disciplines. Weather and climate will impact the ecosystem, but it will also impact humans: human activities (recreation, industry, agriculture) and human health. Further, human uses of the ecosystem (ecosystem services) are and will be affected by the changing climate. Ultimately, this all means that economic systems will be affected, and policies will need to be adjusted to address the changes in the ecoregion. To understand how to best predict and monitor how complex ecoregions are changing, this student will work with a small team of students (a second year CEWiT student and a PhD student), carving out a piece of the larger problem that is of interest to the student. The approach and measures to be tracked and developed may focus on any of the key aspects of the problem for a specific watershed being studied (the Charleston Harbor watershed): ecological, human health, economic, policy, or management.

    Technological or Computational Component:
    This work will involve learning about how to measure different aspects of ecoregions, what kind of data is available and how to work with the data. The student will have the opportunity to learn about approaches to bigger data (not the biggest types of datasets, but datasets with all of the characteristic problems of big data, such as volume, variability etc). In addition the student will learn both statistical and other analytical approaches to examining and analyzing data, including more visual and graphical approaches.

    Preferences:
    A willingness to learn is the top quality we look for. Someone who is willing to try to work across disciplines, as all of our work is cross-disciplinary. Don't be afraid to say you don't know and ask for help, but we will also ask for you to independently work through some of the problems so that you stretch your mind, skills, and creativity.