Hutton Early Research Opportunities

The student will do a deep dive in market research beyond Kelley to understand the reasons for undergrad students who might be underemployed not to pursue a one-year graduate degree at the Kelley School of Business. The student will do some secondary research first before managing a focus group plus developing and applying a survey.

Expected skill development:

• Focus group management
• Survey development
• Data analysis

In this project, students will be working in a group to assess the current landscape of information systems programs, curricula, and job positions. Agentic AI systems will be built to automatically collect, assess, and categorize these data. Interfaces, widgets, and other outputs to map out current job roles, course trajectories for students, and more.

Expected skill development:

• Python
• Web scraping
• Interface development

Digital humans are highly realistic virtual representations of people that look and sound like real individuals and can be controlled by artificial intelligence (AI). You speak with them, rather than typing. Organizations are beginning to deploy them for e-commerce, including sales (e.g., presenting products and answering questions), and after-sales support (e.g., customer assistance and support). Most digital humans operate over the web but the newest form has them able to join Zoom calls and participate in group meetings as a human participant would. But how would you feel if one of your team members was not human? This project will focus on user reactions to digital humans on zoom calls.

Expected skill development:

• The student will gain research design skills, as well as practical insight into how to design and deploy the next generation of AI tools in business settings.
• We have technical experts on the research team, so no technical skills are needed, although the student can take time to learn the technical details if desired.

Grounded in Signaling Theory, the study examines how the appearance of social media endorsers influences consumers’ responses to luxury products. Participants will be exposed to otherwise comparable social media posts featuring either a “looksmaxxed” endorser whose appearance reflects intensive aesthetic optimization through grooming, styling, fitness, cosmetic treatments, or procedures, or a more natural-looking endorser promoting the same luxury product. Looksmaxxed appearance is conceptualized as a costly visual signal of status, discipline, self-investment, and social desirability that may reinforce the symbolic meanings associated with luxury consumption. The study will compare purchase intentions across the two conditions and assess whether social media usage frequency strengthens these effects through repeated exposure to optimized beauty standards. Findings will clarify whether highly optimized endorsers increase aspirational value and persuasive effectiveness or reduce authenticity and relatability, thereby illuminating how appearance-based signals shape luxury consumption in social media environments.

Expected skill development:

• Conducting literature review/finding gaps in literature/building on extant literature
• Learning about consumer behavior/communication theoretical frameworks
• Crafting an adequate research method
• Quantitative data collection
• Data analysis through SPSS
• Reporting findings in an academic format

Light art refers to the visual art form where physical light is the main medium of creation. This can include natural or artificial light sources, projection mapped video, sculptural light, or even designed light for at home use. Many of the surfaces that videos are typically projected onto and the materials for light art installations are made of PVC plastic, which take many years to naturally decompose. Bioplastics are materials produced from renewable biomaterials such as corn, yeast, seaweed, and sugarcane. Since these materials are derived from natural sources, they are biodegradable and compostable. This project will test and develop a variety of different bioplastic recipes to determine a sustainable solution for the creation of surfaces within light art. We will then test these materials for durability and usage within projection and light art. The selected student will have the opportunity to display their artworks created during the research period for the upcoming BioArt/BioHacking 2027 symposium in the spring that I am co-organizing. This will be a great networking opportunity for the student to learn from the 6 invited guest speakers and 20 conference presenters in the field. The student will also have the opportunity to assist with a public workshop on biomaterials with one of the invited guests, sharing their new knowledge with the rest of the IU community.

Please note that the EcoMedia Lab is located at the Research Teaching Preserve, and students will need to have transportation to and from the lab.

Expected skill development:

• experience combining art and science
• sustainable material research
• new media art skills
• hands-on fabrication

This project will investigate how Artificial Intelligence (AI), particularly Large Language Models (LLMs), can support cybersecurity analysis and decision-making. The student will work with simulated cyber scenarios containing events such as suspicious activity, compromised systems, and potential attacks. The project will examine how effectively AI models can interpret these situations, identify relevant security concerns, and suggest appropriate defensive actions.

The student will gain experience with preparing cybersecurity data, interacting with open-source AI models, designing prompts, and evaluating model performance. The work may also involve comparing different models or prompting strategies and analyzing the types of situations in which AI performs well or makes mistakes.

The project is designed to provide an accessible introduction to AI research while contributing to broader efforts on intelligent and automated cybersecurity systems.

Expected skill development:

• Learn how to prepare cybersecurity data and use, fine-tune, and evaluate open-source AI models for security-related decision-making.
• Develop practical experience with prompt design, model inference, and performance
• Build foundational programming and machine learning skills using programming tools and agentic frameworks.
• Develop research, technical writing, and communication skills by analyzing results and contributing to project reports, presentations, or research papers.

As China and the United States compete over the development of artificial intelligence, analysts have trained their lens on the traditional framework of power transitions - how material changes alter the balance of power between a rising and declining power, allowing the rising power to overtake the declining one. As a result, the dominant indicators follow developments in compute and model capability. When China released DeepSeek, for instance, US investors and policymakers put the news in a familiar frame: great-power competition in AI, as in other domains, would be decided by which country possessed the superior technology.

But parallel to the competition in developing frontier artificial intelligence models, China is actively advancing an institutional architecture for global governance. In 2018, China hosted the World Artificial Intelligence Conference, an annual event held every July in Shanghai, that has become increasingly institutionalized. In 2024, China advanced visions of global AI governance at the WAIC that included issuing the Shanghai Declaration on Global AI Governance. And in 2026, 29 countries signed the agreement establishing the World Artificial Intelligence Cooperation Organization. Alongside these efforts, China has promoted resolutions at the United Nations and launched the Global AI Governance Initiative.

International relations scholars have long understood that rising powers are motivated to redraft the rules of the game by reforming existing institutions and building new ones that embed its preferences into the fabric of international relations. But as Beijing focuses on building international institutions while Washington does not, an important question emerges: what are the implications of this difference in strategies? Walt describes the strategy of binding - using norms and institutions to constrain US freedom of action. How does a rising power seek to constrain a dominant power through international rules and how do institutions impact great power competition?

Expected skill development:

• case study approaches
• critical thinking
• organization
• literature review

Please note that students with Chinese language skills will be eligible to help with the first stream of research on this project through reviewing the writing of Chinese experts in global governance and AI to understand what benefits they see international institutions providing China.

Sustainable Development Goal Target 7.2 calls for a substantial increase in the share of renewable energy in the global energy mix by 2030. Target 9.4 calls for upgrading infrastructure and retrofitting industry toward greater resource efficiency and adoption of clean technologies.

The scale of capital committed to the energy transition is considerable. The American Recovery and Reinvestment Act of 2009 alone directed over $90 billion toward clean energy and related technologies, covering renewable generation, grid modernization, advanced vehicles, carbon capture, and clean energy manufacturing, at the time the largest single national investment in clean energy in history. That scale has since grown much larger worldwide.

Solar power and onshore wind have for ten consecutive years been the least expensive way to add new electricity generation in the United States without subsidy, and battery storage costs have fallen alongside them. Battery costs have fallen 93 percent since 2010. Renewable energy nonetheless supplies only 4.3 percent of the energy consumed in transportation worldwide.

This research will focus on the puzzle: Why has this scale of policy-induced investment, together with inexpensive renewable generation and storage, not translated into a satisfying pace of progress on targets 7.2 and 9.4?

Expected skill development:

• Economic analysis: Applying concepts such as incentives, technology adoption, market structure, externalities, and investment barriers to the energy transition.
• Literature review and evidence synthesis: Identifying and critically assessing academic research, government reports, and energy-sector data.
• Critical thinking: Evaluating competing explanations, recognizing limitations in available evidence, and drawing conclusions.
• Research communication: Presenting findings clearly through oral presentations.

The CoDesign Commons is a unique 'living laboratory' where we bring together older adults, community organizations, researchers, and policy makers to work on complex problems in aging. The Commons runs community outreach programming two days per week (Mon/ Fri) at the Endwright East Active Living Community Center in College Mall. We also facilitate research studies for IU researchers working with older adults. This role would help facilitate community outreach and events with older adults, as well as conduct ongoing evaluation of the Commons itself (via interviews/ focus groups, surveys, record keeping). Students may also work with ongoing 'CoDesign Teams' to support research and community outreach projects in the area (e.g., dementia care/ support). Students would assist with data collection and analysis, writing, and presentation of scientific findings to varied audiences.

Expected skill development:

• Build interpersonal (and intergenerational) skills in working with community partners/ organizations and older adults
• Develop a research question for program evaluation
• Create a research plan to address the stated question(s)
• Collect and analyze program evaluation data
• Write/ present study results via research poster/ talk, community presentation, and scientific writing. 

The Arctic is warming four times faster than the global average, driving rapid environmental shifts that disproportionately impact rural, coastal, and Indigenous communities. This project investigates the extent to which environmental changes influence migration decisions and practices in coastal Alaska, generating empirical insights to help individuals, families, and communities plan for current and projected changes. In a place where both seasonal and permanent migration have been long-standing patterns of life, and where many push and pull factors coincide, it can be challenging to definitively link current patterns of migration to environmental shifts and hazards. Furthermore, decisions to relocate or remain in place are deeply shaped by governance frameworks, relocation costs, resource availability, and community social dynamics. Working alongside faculty and graduate mentors, the student will conduct statistical analysis of household survey data and/or thematic (qualitative) analysis of knowledge holder interviews from Western Alaska to develop a deeper understanding of the relationship between environmental changes, migration decisions, and adaptive community actions. The student will also have the opportunity to explore publicly available datasets (e.g., Alaska school enrollment data) for signals of environmental migration and participate in a comparative review of community relocation policies from across the circumpolar Arctic. Deliverables include statistical analysis of survey questions relating to migration and/or thematic analysis of interviews; a series of high-impact visualizations suitable for publication and presentation; and a final written deliverable such as a research report, a conference paper, or a poster presentation for the Hutton Honors College Undergraduate Research Conference.

Expected skill development:

• Expertise in mixed-methods Arctic research, combining quantitative survey data with qualitative thematic analysis of community knowledge holder interviews using NVivo.
• Proficiency in statistical programming and data manipulation using R and RStudio for cleaning, subsetting, and analyzing longitudinal demographic indicators and household migration survey data.
• Experience working with datasets from publicly accessible sources, including the Alaska Department of Labor and Workforce Development and Alaska school enrollment records.
• Strong written and verbal communication skills for policy synthesis and evaluating Arctic community relocation frameworks, presenting findings through research posters, policy summaries, or conference papers.

Consumer animosity, the deep-rooted anger and antipathy that consumers feel toward a foreign country because of its past or present military, political, or economic actions, has become a powerful force shaping global markets. Since Klein et al. (1998) introduced the foundational animosity model, researchers across marketing, international business, and consumer psychology have examined how and why consumer hostility toward foreign nations affects purchasing behavior, firm strategy, and market outcomes. Yet despite three decades of accumulated evidence, we lack a comprehensive understanding of the 1) antecedents, 2) consequences, 3) mechanisms, and 4) boundary conditions of consumer animosity and its nuanced implications for international business. We propose a systematic review of consumer animosity research published in leading business journals. Our review proposal synthesizes initial findings from consumer animosity research studies (108) and develops a new organizing integrative conceptual model. Based on our review, we identify what we know about the antecedents, consequences, mechanisms, and boundary conditions of consumer animosity, and we formulate recommendations for future research. Our proposed review will advance IB scholarship by connecting consumer animosity to firm global strategy and the dynamics of retreat from hyperglobalization.

Expected skill development:

Students will develop the skills required to conducting a systematic literature review, including but not limited to:

• critical thinking and evaluation
• literature search
• data synthesis and mapping
• research skills
• working with the coding scheme
• project management
• academic writing

The research placement will build upon an existing geospatial and machine-learning approach and codebase developed by the faculty mentor to identify industrial facilities, with an initial focus on concrete batch plants. The existing framework uses satellite imagery and publicly available spatial datasets to identify potential facility locations that may be missing from or incompletely represented in environmental inventories.

Undergraduate researchers will learn to apply and evaluate this existing framework using Google Earth Engine and related analytical tools. Students will work with satellite imagery, land-cover data, facility databases, and other geospatial datasets.

Their activities primarily include preparing and validating data, running the existing detection workflow in new geographic areas, evaluating classification results, visually reviewing candidate facility locations, and investigating false positives and false negatives.

Through these activities, students will gain hands-on research experience in environmental data science, remote sensing, GIS, and machine learning while contributing to the validation and further development of an ongoing research project with real-world environmental applications.

Expected skill development:

• Geographic Information Systems (GIS) and spatial data analysis
• Remote sensing and interpretation of satellite and aerial imagery
• Google Earth Engine and cloud-based geospatial analysis
• Working with Sentinel-1, Sentinel-2, NAIP, land-cover, and other environmental datasets
• Data cleaning, integration, and management of geospatial datasets
• Fundamentals of supervised machine learning and classification
• Training and evaluating random-forest models
• Creating and validating labeled training and testing datasets
• Assessing model performance, including false positives, false negatives, and detection accuracy
• Visual interpretation and validation of candidate industrial facilities
• Basic programming and reproducible research workflows
• Scientific problem-solving and interpretation of quantitative results
• Documentation and communication of research methods and findings
  

MosqUS is a digital research and documentation platform that traces the evolving architecture and lived histories of Muslim communities and mosque buildings in the United States. Rather than treating mosques as static objects or aesthetic expressions of religious identity, MosqUS examines buildings as living records shaped by worship, everyday practices, community needs, adaptation, and change over time. The platform brings together architectural documentation, spatial analysis, oral histories, archival materials, photographs, demographic and geographic data, and community narratives to reveal how Muslims create, inhabit, transform, and sustain places of belonging. MosqUS documents purpose-built mosques alongside buildings adapted for Muslim use—including former churches, storefronts, warehouses, offices, and homes—recognizing adaptation itself as an important form of placemaking. By connecting physical spaces with the people, practices, histories, and circumstances that continually reshape them, MosqUS offers a dynamic account of Muslim presence in the United States and preserves the stories embedded within its evolving built environment.

Expected skill development:

• Analytical reading and writing
• Conducting literature review
• Summarization and synthesis. 

This project investigates how environmental change, extreme weather, and slow-onset hazards shape population dynamics, human mobility, and community responses across diverse geographic contexts. Working closely with faculty and graduate mentors, the student will analyze real-world spatial and demographic datasets, which may include household surveys, longitudinal residential address histories, mobile phone mobility data, demographic records, and high-resolution environmental indices. The student will explore pressing questions related to environmental migration, hazard risk perception, place attachment, wellbeing, and community adaptation strategies under changing local conditions. Methodologically, the student will apply quantitative and spatial methods to organize and integrate multiple data sources, construct measures of mobility and environmental exposure, conduct GIS and statistical analyses, and explore computational approaches such as spatial clustering, machine learning, and automated spatial data workflows in R or Python. Deliverables include cleaned and integrated spatial datasets; high-impact cartographic and statistical visualizations suitable for publication; and a final written deliverable such as a research poster for the Hutton Honors College Undergraduate Research Conference, a conference abstract, or a project report.

Expected skill development:

• Proficiency in data cleaning, restructuring, integration, and quality checking in R or Python.
• Expertise in GIS and spatial analysis, including geocoding, spatial joins, distance calculations, and linking individual records to U.S. Census and environmental hazard data.
• Advanced data visualization and cartographic mapping using industry-standard GIS software (ArcGIS or QGIS) and programmatic visualization packages (e.g., ggplot2, sf).
• Interdisciplinary quantitative reasoning for interpreting spatial and statistical results in relation to substantive questions about population dynamics, environmental hazards, and community resilience.
• Strong written and verbal communication skills for presenting geospatial and demographic findings to interdisciplinary audiences, including conference posters and project summaries.

Of the world’s approximately eight billion people, about two billion lack reliable access to safe drinking water. Understanding and protecting water resources is essential to addressing this global challenge.
This project uses publicly available geochemical data from the U.S. Geological Survey and geological surveys of other nations to evaluate groundwater resources and water quality. By examining data from different regions, we seek to understand how natural conditions and human activities influence the quality of water people depend on.

Students will help organize data, explore patterns, and create maps and summaries that communicate their findings. Each student will investigate a manageable question tailored to their background and interests, with faculty guidance throughout the research process. The project offers an opportunity to connect computer and environmental science with an urgent societal need while contributing to research that supports informed decisions about water resources and safe drinking water.

Expected skill development:

• Finding and organizing publicly available environmental data.
• Evaluating data quality and interpreting evidence.
• Analyzing patterns and creating clear graphs and maps.
• Understanding connections among groundwater, water quality, and societal needs.
• Communicating research findings through writing and presentations.

The FAMILIA lab focuses on understanding and addressing contributing factors in Alzheimer’s disease and related dementias (ADRD) among groups at higher risk for the disease, including Hispanic older adults and their caregivers. this includes examining cultural and other factors that contribute to cognitive decline, neuroimaging and blood based biomarkers, and an NIH funded clinical intervention project where we are working on a Focused Adaptation of a Mobile Intervention for LatIno cAregivers (FAMILIA).

Expected skill development:

• Students will develop skills to identifying research questions, literature reviews, study design, participant recruitment, informed consent, data collection, research protocols, and human-subjects research ethics.
• Students will learn foundations of Alzheimer’s disease and related dementias, cognitive aging, risk and protective factors and health disparities in aging.
  
  

The student would choose a financial reporting research question from a selection offered (or come up with another with my help), engage with the literature to find possible explorable testable hypotheses relating to the research question, and work with me to design an experiment to test those hypotheses. If I deem that the research project has promise, it could progress to a full experimental investigation and paper. Throughout the process, the student will meet with me periodically (roughly every 2-3 weeks, with more frequent "touch base" communication as needed) to provide direction and assess progress.

Please note that this placement would only be appropriate for students who are majoring in accounting at the Kelley School of Business or have previous accounting coursework experience, as it is very domain-specific to the financial accounting area.

Expected skill development:

• Engage critically with the literature in financial accounting
• Learn how to find and evaluate academic research
• Develop strong skills using survey tool instruments such as Qualtrics
• Likely become CITI certified so they understand and become authorized to administer lab experiments of minimal risk
• Depending on the research question, students may also develop skills in technical research (e.g., collecting company information data from the Internet)

Students will join the FIT 360 Exercise Psychology & Behavioral Science Lab, a student-powered research lab focused on understanding what helps people adopt and sustain healthy behaviors and thrive across life, work, movement, and play. Students will gain hands-on experience contributing to ongoing research in exercise psychology, behavioral science, health behavior change, and health and wellness.

Depending on current projects and student interests, students may assist with literature reviews, participant recruitment, data collection and management, survey development, program evaluation, data analysis, and dissemination of research findings. Students will also participate in lab meetings and work alongside undergraduate and graduate students on collaborative research projects.

No previous research experience is required. Students will receive mentorship and opportunities to progressively build research skills, explore their interests, and contribute meaningfully to real-world research. As students develop, they may also have opportunities to contribute to research posters, presentations, manuscripts, and other scholarly products.

Expected skill development:

• literature searching
• participant recruitment
• data collection and management
• introductory data analysis
• scientific communication

Practices of sharing food play an important role in maintaining local food systems, continuing traditional protocols, and buffering individuals and households from shocks. Sharing practices can be particularly important for the well-being of people who continue to harvest food from the land for subsistence. However, many of the plants and animals that have been locally harvested by Indigenous Peoples of the Arctic for millennia are experiencing declines in abundance and uncertainty in where they will be, and when. This directly challenges the ability for people to rely on them. Working alongside faculty and graduate mentors, the student will investigate how recent shifts in local environment, abundance, and harvest success relate to changes in sharing practices among residents of the Upper Copper River Basin of Alaska, while exploring the strategies community members use to sustain food sharing networks under changing conditions. The project employs an interdisciplinary mixed-methods approach, drawing on household survey responses, interviews with community knowledge holders, and secondary data from harvest surveys. The student will have the opportunity to engage with any or all of these data sources and analytical approaches—learning technical skills and gaining insight into the strengths and limitations of diverse social science data types. Deliverables include statistical analysis of survey questions relating to food sharing and/or thematic analysis of interviews; a series of high-impact visualizations suitable for publication and presentation; and a final written deliverable such as a research report, a conference paper, or a poster presentation for the Hutton Honors College Undergraduate Research Conference.

Expected skill development:

• Expertise in qualitative and mixed-methods research, including interview transcription, thematic coding, and qualitative data management using NVivo.
• Proficiency in statistical programming and data manipulation using R and RStudio for cleaning, subsetting, and analyzing household survey datasets.
• Experience working with datasets from publicly accessible sources, including the Alaska Department of Fish & Game’s Community Subsistence Information System (CSIS) and Technical Paper Series.
• Strong written and verbal communication skills for reporting complex statistical and thematic findings in a clear and persuasive manner, including research posters and conference presentations.

Artificial intelligence has helped scientists increase the number of manuscripts they write. Preprint repositories such as SSRN, arXiv, HAL, and more allow researchers to quickly disseminate their findings before, during, and after peer review. With a proliferation of manuscripts and published articles, it is becoming increasingly difficult for researchers to understand the state-of-the-art literature on a specific topic, which has led to issues related to construct proliferation (e.g., the same construct used for different explanatory purposes) and fragmented discourses (e.g., researchers in the same community who do not build on each other’s research stream). We introduce a novel methodology, entitled the Visual Literature Review (VLR), that synthesizes constructs and variables researchers operationalize in their studies to create customizable nomological networks of these constructs using network science techniques. VLR leverages large language models to parse hypotheses, research models, and results into adjacency matrices that are later used to visualize the relationships between constructs. The purpose of this HERO proposal is to develop a website researchers can use to visualize nomological networks of constructs and their broader families to identify significant and non-significant construct relationships to assist in their theory-building activities.

Expected skill development:

• Application of a theory-based knowledge harness to perform construct synthesis
• Theory-building concepts related to nomological networks, explanatory theory, predictive theory, and more
• Behavioral research hypotheses to understand construct relationships
• Network science concepts related to adjacency matrices, edge lists, and centrality measures
• Web app development, including front-end user interface and user experience design
• Web hosting

The Holdeman Lab of Russian Old Believer Studies opened at IU in the fall of 2026. It engages undergraduate students in various research projects on the ethno-confessional group known as Russian Old Believers, working with published materials (contemporary and historical) and ethnographic field materials. Projects range from archiving to data preparation to transcription of original materials (audio, photographic, video) to work in faculty research projects to community service projects. Students begin with tasks that develop research lab procedural skills, academic formatting, familiarity with digital humanities technologies, and attention to detail, then move on to documentation and analysis tasks of increasing complexity and scope. HHC students in the Hutton Early Research Opportunities Program will benefit from the structure and resources of the Lab, as well as the sense of community and shared mission among its members, while also working closely with the faculty director on projects that help them explore their interests and apply and develop their skills.

Expected skill development:

• Increased language proficiency (especially reading and listening) in Russian, Polish, Ukrainian, or Lithuanian for students with relevant language experience
• Research lab procedure adherence
• Data analysis and methodsA
• rchival procedures
• Academic research and citation
• Use of digital humanities technologies
• Personal Skills: attention to detail, time management, communication with faculty, deductive reasoning, prioritization, problem-solving 

This project will analyze the trend of homeowners choosing not to purchase insurance on their homes. The project will measure the aggregate trend and examine trends with respect the locations and values of the homes. The project will also determine the effect of other factors such as weather on the trend.

Expected skill development:

• Learn to search for articles and gather data from various sources
• Learn statistical analysis, such as comparing averages and measuring trends

Students will join the Hoosier Sport research team, contributing to applied research focused on improving youth sport and physical activity opportunities for young people in Indiana. Students may assist with literature reviews, participant recruitment, data collection and management, data analysis, and sharing research findings. The placement provides hands-on experience with community-engaged research while contributing to projects designed to better understand and improve youth sport experiences.

Expected skill development:

• Leadership
• Teamwork and critical thinking
• Literature searching and reviewing
• Quantitative research methods
• Participant recruitment and data collection
• Data management
• Scientific writing and communication

This research examines how cultural variables — including local-global identity, tightness-looseness, and power distance belief — shape everyday consumer behavior, such as variety seeking, recycling and sustainable consumption, coupon use, and price sensitivity. Students joining this placement will work alongside the professor and their collaborators on active projects that combine survey research, laboratory and field experiments, and secondary data analysis. Depending on interest and project timing, students may help design survey and experimental materials, conduct literature reviews, assist with data collection (including running participants in lab studies), code and organize datasets, and help summarize findings for presentations or manuscripts.

This is an excellent opportunity for students curious about psychology, marketing, or business to see how a research idea moves from a research question to a publishable finding. Students will gain full exposure to the full arc of the research process in a collaborative, mentorship-rich environment.

Expected skill development:

• Conducting literature reviews and synthesizing academic research
• Designing and pretesting survey and experimental materials
• Assisting with data collection, including running lab study sessions
• Basic data coding, cleaning, and organization
• Understanding of experimental design and the scientific research process
• Exposure to statistical analysis and interpretation of results
• Scientific writing and summarizing findings for presentations
• Collaborating with faculty and doctoral student researchers in an academic setting

How does social behavior evolve? Research in this lab focuses on understanding fundamental principles by which genetic variation in behavior shapes the social environment and beyond. By measuring diverse genotypes of the fruit fly, Drosophila melanogaster, under a variety of important social conditions, we can rigorously test hypotheses about how genetic variation shapes social behavior, social networks, social selection, and ultimately evolution. 

Expected skill development:

The student will participate in ongoing scientific research in this lab. Exact tasks vary, but include:

• rearing different genotypes of Drosophila melanogaster under controlled conditions,
• preparing behavioral trials, 
• measuring behavior
• analyzing the resulting data
• reading and discussing relevant scientific literature
• learning about norms and career paths in academia

These tasks go beyond route repetition to facilitate the student’s deep understanding of current research in the lab as well as the practice of science in general.

Students will contribute to ongoing research in the Human Integrative Physiology Laboratory in the School of Public Health-Bloomington. The Human Integrative Physiology Laboratory conducts high-resolution phenotyping studies in human subjects. Amongst other studies, students will contribute to the data collection efforts for research studies aimed at improving human resiliency to cold stress. The goal of this study is to determine whether increasing brown adipose tissue improves physiological and perceptual responses to extreme cold stress. Data collection efforts include recruitment of human subjects, prepping human subjects for research protocols, conducting research protocols, data extraction, and dissemination.

Expected skill development:

Students will receive a hands on learning experience focused on human subjects research. Many of the skills that students will learn will be applicable in future careers in the health professions (e.g., physician, physical therapy, personal training, nursing, etc.). In this context, students will learn how to perform a variety of measurements such as continuous blood pressure, electrocardiography, infrared thermography, oxygen consumption, energy expenditure, body temperature, and heart rate variability among others. Students will also learn how to implement a variety of research protocols that are used to interrogate body temperature regulation, nervous system activation, and blood pressure control. Students may be exposed to additional protocols as research advances. Students will also learn how to prepare and present a research poster presentation at the School of Public Health-Bloomington Research Day or at a national conference. 

This research placement will examine how people interact with and evaluate solutions generated by generative artificial intelligence (GenAI). The project focuses on an important question: when people use GenAI to help solve a task, does thinking carefully about the task requirements before seeing an AI-generated solution help them better identify and correct errors? The student will help to design a controlled behavioral experiment in which participants review an AI-generated workflow and make decisions about whether and how it should be revised. The experiment varies the sequence in which participants analyze task requirements and view the AI-generated solution, as well as whether the initial AI output contains an error. The student will assist with reviewing relevant literature, refining experimental materials, pilot testing the study, organizing and checking research data, and conducting introductory analyses. This research placement will provide hands-on exposure to research at the intersection of generative AI, human decision making, and information systems.

Expected skill development:

• Understanding how behavioral experiments are designed and conducted, including research questions, hypotheses, experimental conditions, and outcome measures.
• Conducting academic literature review and summarizing prior research on generative AI, human decision making, and human–AI interaction.
• Developing and pilot testing survey and experimental materials.
• Organizing, cleaning, and documenting experimental data.
• Conducting basic descriptive and statistical analyses and interpreting empirical results.
• Developing familiarity with how GenAI systems can affect human judgment, problem solving, and oversight.
• Communicating research findings through short written summaries, tables, figures, and research presentations.

Selected student will work with the faculty mentor on reserach related to AI and its impact on early career job market expectations.

Expected skill development:

• Data collection
• Cleansing and analysis
• Strcutured and unstructured qualitative analysis
• Data visualization and presentation of results
• Communication skills

The faculty mentor for this placement is open to working with students on topics that interest them, as long as the project broadly falls within the innovative use of AI, such as AI-human interaction, AI-mediated communication, AI coordination and orchestration, and related areas.

Expected skill development:

• Research question development and conceptualization
• Literature review and theory development
• Experimental design
• Quantitative and/or qualitative data analysis
• AI application and prototype development
• Research communication and presentation

This project explores a bold usage of AI agents and large language models (LLMs): building large-scale artificial environments that simulate real-world business mechanisms and socioeconomic systems.

In the first phase, we will develop simulations involving up to tens of thousands of AI agents. These agents will represent heterogeneous individuals or organizations with different personas and behavioral characteristics.

In the second phase, we will investigate how knowledge generated from these large-scale simulations can improve real-world prediction, decision-making, and representation learning. In particular, the project will explore the development of foundation models that learn from diverse simulated environments and can generalize across different business problems and application contexts.

Expected skill development:

• Python Programming: Develop research-grade Python skills including data processing, object-oriented programming, and debugging.
• Data Mining and Machine Learning: Build predictive and representation-learning models using pandas, scikit-learn, and statsmodel.
• LLM API Management: Learn to programmatically access and manage frontier LLMs through OpenAI, Anthropic, and Google APIs, with LiteLLM or similar unified interfaces for model routing, batching, retries, token/cost tracking, caching, and structured outputs.
• Large-Scale AI-Agent Simulation: Build simulations involving thousands to tens of thousands of LLM-driven agents using frontier Python frameworks such as AgentSociety and AgentTorch, together with distributed-computing tools when needed. Students will implement heterogeneous personas, agent states, interaction rules, social/business networks, interventions, and emergent system-level outcomes.
• Foundation Model Development: Gain introductory hands-on experience with PyTorch and Hugging Face Transformers to work with pretrained models, generate embeddings, fine-tune models on new datasets, and evaluate model performance across different tasks and application settings.

This placement would offer an undergraduate student the opportunity to contribute to a research project mapping domestic terrorism and mass violence incidents across the United States, using geospatial data and methods. The student will help obtain and process violence data and, subsequently, build out maps showing where incidents cluster over time and assist in exploring associations with community-level factors such as poverty, firearm policy, and political context. Depending on the student’s interests and prior coursework, responsibilities may include literature review, data cleaning and management, building basic maps in Rstudio, and basic spatial or statistical analyses. No prior coding or spatial analysis skills necessary though may be helpful. This placement is well suited to a student interested in public health, criminology, or geography and who wants exposure to applied spatial research methods within a supportive mentored environment, with opportunities to contribute to conference presentations or manuscript preparation as the project develops.

Expected skill development:

• Data cleaning and management of large public datasets
• Geospatial and mapping techniques using Rstudio (or other preferred GIS software)
• Basic statistical/spatial analysis (e.g., cluster or hot-spot analysis)
• Mentored literature review and research question development
• Understanding of ethical considerations given with sensitive nature of violence-related data
• Data visualization and scientific communication experience
• Manuscript development and conference presentation skills

In this work, students will focus on collecting large quantities of agent skills from major marketplaces such as SkillsMP, and others. Significant vulnerability assessment of these agent skills will be conducted, subsequently followed by categorization, analysis, and visualization.

Expected skill development:

• Python
• Agentic AI
• Vulnerability scanning
• Interface development

The student will be engaged in reading and coding marketing leadership profiles for a research project that uses LinkedIn data and LLM based textual coding.

Expected skill development:

• Human - LLM interaction
• Marketing leadership path development
• Doing empirical research in marketing

This research project is focused on mental health mobile applications, specifically survey and sensor signal data. Students will learn how to conduct a literature review, analyze data, propose their own work, work in a team, and present their work.

Expected skill development:

• Conducting a literature review
• Data analyses
• Create a poster, and possibly a conference paper and/or journal paper.

The student would join an early-stage national research team across multiple institutions. The project utilizes an ethnographic approach to exploring the ways college students develop identities around notions of smartness and competency. The student will have the opportunity to also develop their own research project related to the topic, if interested.

Expected skill development:

• Learn how to conduct ethnographic interviews and observations
• Learn how to collaborate as part of a research team
• May also design their own research project, if that is something they are interested in doing

In the Observing Civic Engagement (OCE) Lab (oce.indiana.edu), we seek to uncover how people interact within civil society organizations (CSOs) and how those interactions can strengthen organizations, promote civic engagement, and improve quality of life in communities. We study what goes on inside CSOs using an innovative adaptation of the data collection technique known as systematic social observation (SSO). Trained observers attend meetings, events, and activities of organizations and use a specially designed form to record comparable, quantitative data from each gathering. They meet regularly with other lab members to improve their research skills and to reflect on the nature of our local civic community. Some go on to do student thesis projects and to careers in research or civic leadership.

Expected skill development:

• Students will learn the specific social scientific observational research skills associated with our SSO methodology, broader scientific skills of inquiry and interpretation, and generally-applicable professional skills including teamwork, organization, interpersonal interaction, and community engagement.
• There are also opportunities to take on additional specialized tasks with the lab including data analysis and communications for which there is additional training, coaching, and skill development.

The word play has many definitions, but scholars have shown that it provides physical, social, emotional, and intellectual benefits for all. Play gives us a foundation for collaboration, influences our creativity, improves our capacity to solve problems, and enhances an individual's overall wellness. Despite these advantages, many lose their sense of play over time. The structure and stress of adulthood are just two of the many factors that lead to this decline. Compared with children, adults find it harder to explore and engage in unforced activities with little extrinsic reward. However, one group may have an elevated sense of play because of their daily pursuits. They are student-athletes. Student-athletes face the challenges of becoming adults, but most are intrinsically motivated when "playing" games that provide joy and well-being. Therefore, this investigation aims to compare the perceptions and skill sets of young children (access through parental reports) and student-athletes across specific dimensions that are related to play.

Expected skill development:

• Assist in the development and submission an IRB proposal to ensure the safety and confidentiality of the study's participants.
• Obtain an understanding of the fundamentals of subject selection.
• Gain experience in data collection and analysis of subject responses.
• Assist with the dissemination of findings through a formal presentation given at The Bloomington Symposium on Play.

The Global Health Impact Project (global-health-impact.org) at IU's Tobias Center for Innovation in International Development and is offering a few exciting opportunities for students.

Computer Programmer
The responsibilities of this position include programming software applications and building statistical models to automate data collection/analysis process for the Global Health Impact Index, and may include programming websites of the Global Health Impact Project. Proficiency in Python, JavaScript, HTML/CSS, etc. is strongly recommended.

Graphic Design Intern
The responsibilities of this position include designing publications and websites for the Global Health Impact Project. The applicant should have high levels of graphic designing skills and attention to detail. Skills in web programming and knowledge in languages such as HTML/CSS, JavaScript, and Python are a plus but not required.

Research Assistant (Data Intern)
The responsibilities of this position include collecting and analyzing data through research and systematic review. The intern will build statistical models for the Global Health Impact Index, as well as important communication and teamwork skills. Required skills include proficiency in Microsoft Excel. Proficiency in R is strongly recommended.

Research Assistant (General Intern)
a. The responsibilities of this position include editing and referencing academic papers to be published, systematically reviewing academic papers to collect relevant data, preparing papers for submission, and writing grant letters. The intern will learn important writing, communication and teamwork skills. The applicant should have attention to details, high levels of collegiate writing, and proficiency in Microsoft Word and other document editing software applications.

Social Media Intern
The responsibilities of the position include managing social media outlets such as Twitter, Facebook, Instagram, and YouTube, and taking photos and videos of relevant events. The intern will also be responsible for the GHI social media campaign to increase publicity. The intern should have skills relevant to social media managing, video editing, and photography/videography.

Grant Writing
The responsibilities of the position include: 1) researching funding opportunities; 2) maintaining knowledge of proper protocols required for the proposal submission process; 3) assisting in the writing of grant proposals; 4) submitting grant requests within specific guidelines.

All positions with the GHI project, these included, require a commitment of 10 hours per week. Each week you will be responsible for several tasks assigned to you by your team leader, preparation of a weekly update of your progress, and mandatory attendance at a two-hour meeting.

Please note that HERO applicants may be considered for any of these opportunities and should indicate specifically which option(s) they are applying for in their personal statement.

Expected skill development:

Please see descriptions above as this varies by position.

Quantum error-correcting codes are designed to protect quantum information from noise and to enable the reconstruction of lost or corrupted information transmitted through noisy quantum channels. An important and widely studied class is given by Calderbank–Shor–Steane (CSS) codes, which can be constructed from suitable classical linear codes. This makes the search for structured families of classical codes particularly relevant to quantum coding theory.

In this direction, group codes provide a rich algebraic source of examples. While much of the classical theory has focused on abelian group codes, the non-abelian case has recently attracted increasing interest. Recent work of Sales-Cabrera, Soler-Escrivà, and Sotomayor develops methods and examples of group codes arising from cyclic, dihedral, and quaternion groups, as well as from direct products of these groups.

Motivated by these developments, we will study codes associated to small Hopf algebras, which are a generalization of groups, as potential building blocks for CSS quantum error-correcting codes. This computational laboratory could generate new constructions of classical codes with properties suitable for quantum error correction

Expected skill development:

• Programming in Python or Sage, with prior experience using Mathematica, Matlab, or similar computational tools.
• Gaining familiarity with proof techniques and strengthening mathematical problem-solving skills.
• Developing an understanding of the fundamentals of linear and quantum error-correcting codes, and building introductory familiarity with group algebras and Hopf algebras.
• Developing computational skills for constructing examples, calculating code parameters, and exploring algebraic structures.
• Building oral communication skills for presenting and discussing research findings.
• Strong written communication skills for mathematical exposition, with an emphasis on clearly explaining definitions, computations, and proofs.

This research project examines the relationship between Indigenous cultural revitalization and ecosystem restoration, with a particular focus on Runa (Quechua) communities in Cusco, Peru. In preparation for future ethnographic research with Pachaillariy, an Indigenous-led NGO, undergraduate researchers will investigate scholarship and policy concerning ecosystem restoration, international development, Indigenous environmental governance, and cultural revitalization. A central question guiding the project is whether cultural revitalization—often treated as a beneficial outcome of environmental restoration—might instead create the social, political, and institutional conditions that make restoration possible.

Students will help situate this question within existing academic and policy debates while exploring how concepts such as communal governance, ancestral ecological knowledge, reciprocity, and Allin kawsay (good living) complicate conventional approaches to conservation and development. Their research will contribute to the conceptual and methodological groundwork for an ethnographic study of how Indigenous governance and cultural practices shape environmental restoration and community self-determination.

Expected skill development:

Students participating in this project will receive training and hands-on experience in several stages of the qualitative research process, including:

• Literature review and synthesis: Locating, evaluating, organizing, and synthesizing interdisciplinary scholarship across anthropology, development studies, Indigenous studies, and environmental studies.
  Research database development: Developing systematic strategies for tracking sources, themes, concepts, and debates and creating an annotated bibliography or literature matrix that can support future research.
• Policy analysis: Identifying and analyzing relevant conservation, development, and Indigenous governance policies and examining how policy frameworks conceptualize environmental restoration, culture, community participation, and Indigenous knowledge.
• Research question development: Learning how broad theoretical interests are translated into focused, empirically researchable questions and how existing scholarship informs the design of ethnographic research.
• Ethnographic methods: Training in participant observation, fieldnotes, semi-structured interviewing, qualitative data collection, and the opportunities and limitations of ethnographic research.
• Interview protocol development: Translating research questions into culturally appropriate, open-ended interview questions and developing semi-structured interview guides for different research participants.
• Research ethics and IRB preparation: Learning principles of human-subjects research, informed consent, confidentiality, data management, and ethical research with Indigenous communities while contributing to the preparation of IRB materials.
• Community-engaged research: Considering questions of reciprocity, collaboration, positionality, and accountability when conducting research with community organizations and Indigenous partners.
• Qualitative research design: Participating in the development of an ethnographic research plan that connects theoretical questions, existing scholarship, methods, ethics, and fieldwork logistics.
• Scholarly communication: Developing skills in concise analytical writing, research summaries, annotated bibliographies, and presenting research findings to academic and potentially non-academic audiences.

The work of the School Based Mental Health Research and Training Initiative promotes the development and implementation of multi-tiered systems of support (MTSS) focused on improving student well-being, school climate, and academic engagement. The research team allows for student-faculty collaborations in the areas of trauma-informed practices and school-based mental health, broadly inclusive of K–12 students and educators, and emphasizes research, training, and service delivery. I lead several partnerships with local school districts to expand access to comprehensive, school-based mental health services by providing high-quality, applied training in evidence-based, systems-level intervention. Current research projects include examining the effectiveness of school-based mental health interventions for youth through pre- and post-data collection on self-reported mental health outcomes, examining multidisciplinary school mental health team membership and leadership practices, and examining the effectiveness of implementing a gratitude intervention to enhance educator mental health and well-being. Projects are at various phases of completion (e.g., development, data collection, data analysis). Students will have the opportunity to work alongside a team of school psychology doctoral graduate students on a weekly basis to move projects forward from conceptualization to publication.

Expected skill development:

Depending on project participation, students will have the opportunity to learn several essential skills to support their development as researchers and to enhance their skills for post-baccalaureate training. Skills may include: study conceptualization (e.g., identifying gaps in the literature base, developing research questions), research design, with a heavy emphasis on qualitative and single-case research methodologies, best practices for study recruitment and data collection techniques, and development and submission of IRB protocols. For projects in which data have already been collected, students will have the opportunity to clean data for quantitative data analysis, support qualitative data analysis using reflexive thematic analysis, and participate in manuscript preparation and/or presentation proposal preparation.

Selling financial products such as annuities and life insurance poses a distinct marketing challenge. These products are complex and hard to evaluate even after purchase, creating an information asymmetry [the seller knows more than the buyer] that leaves consumers reliant on the salesperson who recommends them. As such, how firms manage their salesforce shapes what consumers buy. Firms pay salespeople largely through commissions and support them with calls and product training. Several marketing questions remain open. How do salespeople choose what to recommend to whom, and do they use their knowledge advantage to inform customers or steer them? Does sales support make salespeople more productive and better advisors, or simply push particular products? Which salespeople benefit most from support, and how should firms allocate it? How does regulation change what gets sold? With Americans holding almost $30 trillion in retirement savings, the answers matter for firms and consumers alike.

Expected skill development:

• Hard skills: managing large datasets, and using quasi-experimental & regression methods that separate cause from correlation
• Soft skills: synthesizing research across marketing, finance and economics, and explaining data-driven complex findings clearly to industry audiences

This project will replicate and extend a recently developed clinical AI simulation framework for studying diagnostic reasoning. Students will first implement the simulation environment locally and reproduce the basic workflow in which a large language model investigates a patient case by requesting laboratory tests, imaging, physical examinations, and other diagnostic procedures before submitting a diagnosis. Students will then build a larger corpus of clinical cases by identifying, downloading, and processing published medical case reports for use in the simulator.

Using this expanded environment, students will evaluate different AI approaches to diagnostic reasoning, beginning with zero shot off the shelf language models and then testing alternative prompting, reasoning, retrieval, and agent based strategies. The longer term goal is to move beyond benchmarking existing models and develop approaches that make better diagnostic decisions while also using healthcare resources more efficiently, for example by improving diagnostic accuracy while reducing unnecessary testing, cost, time, invasiveness, and physician effort. Depending on progress, the project may also explore fine tuning or training specialized diagnostic models or decision policies.

Expected skill development:

• Python programming and software implementation
• Reproducing and extending research code
• Working with large language models and APIs
• Collecting and preprocessing clinical case reports
• Building structured datasets from unstructured documents
• Prompting, retrieval, and agent based AI methods
• Experimental design and benchmarking
• Evaluation of diagnostic accuracy and resource efficiency
• Machine learning model training and fine tuning
• Reproducible research and collaborative software development

Students will join ongoing research at the intersection of marketing, public policy, and consumer behavior. Current projects examine how state policies shape firms and consumers, including minimum wage increases, changes in alcohol regulation, and retail theft. A second line of work studies how experiential learning shapes students' career beliefs and gender stereotypes, tested through online experiments and AI-based simulations. Depending on their interests, students will help locate and summarize prior research, assemble and clean datasets from public and commercial sources, design pretest survey instruments in Qualtrics, and run and interpret basic analyses in R or Stata.

Expected skill development:

• Finding, reading, and synthesizing academic research in marketing, economics, and public policy
• Designing survey experiments and building them in Qualtrics
• Collecting, cleaning, and organizing real-world data, including policy records, sales data, and survey responses
• Basic data analysis and visualization in R
• Interpreting results and judging the strength of evidence, including the difference between correlation and causation

Beyond its well known role in medical diagnostics, ultrasound has become an exciting tool for studying speech and language. In my research, I use ultrasound to track the shape and position of the tongue during speech, to identify and quantify the differences in articulation of speech sounds between languages or different dialects, or to investigate the process of learning a foreign language, changes in pronunciation when speakers are multilingual, or when in some singing techniques. Working with faculty and graduate assistants, students will have a chance to get involved in projects concentrating on multiple languages (Bosnian/Serbian/Croatian, Ukrainian, Chinese, SJQ Chatino), learn more about these languages, research in general, get hands-on experience with 3D ultrasound images and data annotation using programming and analysis environments such as MatLab, Praat, and AAA (Articulate Assistant Advanced).

Expected skill development:

• Understanding the key stages of planning and executing an experimental project.
• Understanding basic principles in research involving human subjects in social sciences.
• Collaborating effectively within a research team and contributing to shared goals and workflows.
• Analyzing and manipulating three dimensional ultrasound images of the tongue.
• Identifying major anatomical landmarks of the tongue on ultrasound images.
• Designing an effective annotation scheme and annotating linguistic data.
• Using specialized ultrasound analysis software to visualize articulatory data and extract quantitative measurements.
• Recognizing in spectrograms acoustic patterns characteristic of major speech sound classes.
• Working in programming environments such as MATLAB or Praat to generate figures, run basic analyses, or design perceptual experiments.

The faculty mentor is developing a new research project that examines the usage of classicizing monuments across Indiana. Building off of a growing disciplinary interest in the field of reception studies, this project examines when and where Indiana's communities adopted classicizing forms and themes in monumental sculpture and architecture. The research assistant for this project will help to identify examples of classicizing sculpture set up in (and on) Indiana's county courthouses and their squares, search ProQuest databases for newspaper stories about these statues, and contact local historical societies to identify archival resources. They will also work with a graduate research assistant (already hired) to prepare this information for inclusion in a web-based resource to communicate their findings to the public.

Expected skill development:

• Archival research
• Communication with professionals and the broader public
• Organization of information
• Critical reading
• Teamwork

The Little 500 is the largest collegiate bicycle race in the United States. Teams of four riders compete in a relay-style race for men (50 miles = 200 laps) and women (25 miles = 100 laps). However, one group has not yet participated in this event. They are students who have physical limitations. Through a small working group comprised of members from the School of Public Health, we have set out to determine the feasibility of incorporating opportunities for this group of students during the race weekend. The intention is not to diminish the men's and women's events, but to add to this point of pride for our campus.

To present a proposal to the decision-makers of the Little 500, as well as other campus administrators, we must collect preliminary data and develop feasibility models for the document. These elements would include participation profiles (based on disability type), pro forma financial statements, and an overall operations assessment for the venue. Therefore, we believe this pilot project would be a great opportunity for undergraduate students to play an integral role in launching the IU's adaptive version of the Little 500.

Expected skill development:

• Assist with a feasibility study to determine best practices for adaptive bicycle racing.
• Obtain an understanding of the fundamentals in data collection/analysis to identify participant profiles.
• Gain experience creating pro forma financial statements.
• Understand the theory and process of conducting venue/operations assessments.

Interrogating the principled monarchist loyalism of the American Revolution and resurgent interest in explicit monarchism and unitary power in the present day, this project examines the persistence of monarchism and royalism in post-revolutionary American political thought. Using both historical and contemporary archival approaches, this project aims to recover and analyze political ideas thought to have been rejected during the Founding era in both their eighteenth-century and present versions.

Expected skill development:

Students will develop skills in archival research and contemporary theory, as well as related logistical skills. Students will be trained (and asked) to conduct substantive archival work, as well as logistical work with historical societies, archives, and libraries. Students will also be encouraged to conduct their own related research (if desired), leading to the possible presentation of a conference poster and/or initial work on a future research/honors project.

Undergraduate researchers will work with the newly established Translation Research Commons (TReCs), an interdisciplinary humanities initiative that investigates translation as both an object of study and a mode of scholarly inquiry. Students will help research and document IU's rich but dispersed history of translation, including published translations and scholarship, archival collections, graduate research, and the work of translators and interpreters associated with IU. Depending on their interests, students may conduct bibliographic and archival research in the Lilly Library, investigate historical figures and episodes such as IU alumnus and diplomatic interpreter William Hopkins, or contribute to research on translation and generative AI. Students will learn how humanities researchers move from an initial question to the identification, evaluation, organization, and interpretation of evidence. Their work will contribute to an evolving research hub documenting translation at IU and may support future TReCs publications, grants, and research projects.

Expected skill development:

Depending on project assignment and prior skills, the following are among the skills students can expect to develop:

• Archival and primary-source research;
• Bibliographic and library-database research;
• Source evaluation and verification;
• Citation management and research documentation;
• Metadata creation and organization of research data;
• Close reading and comparative textual analysis;
• Formulating and refining humanities research questions;
• Synthesizing evidence from multiple kinds of sources;
• Collaborative research practices;
• Writing for scholarly and public-facing audiences;
• Critical understanding of translation as a research practice;
• Critical AI literacy.

Student will research different theories of how jurors determine facts and are persuaded of guilt or innocence. There is a rich literature which students will read and synthesize. I will make time to discuss the work with students and provide detailed feedback about student's writing. This is a for a project proposing a new psychological theory of how jurors are affected by negative character evidence, suing the emotion of anticipatory regret.

Expected skill development:

• Research legal materials
• Distill complex material
• Improve writing skills
• Work professionally with a supervisor

My next research project concerns meaningful patterns of casualties in Homer's Iliad. In assisting me in this research project, the student would read Iliad books 3-24 very carefully, keeping track (with an Excel spreadsheet) of all recorded casualties. The student would note down the killer, the victim, the weapon used, and the body-part(s) affected, along with any other potentially relevant information (occupation, place of birth, lineage, relationship to other characters in the poem). The student would then cross-check their data with those collected by other scholars (Seymour 1908; Bassett 1932; Garland 1981; Stoevesandt 2004; Gainsford 2023) and investigate any discrepancies, keeping track of all disagreements. Upon completion of this step, the student would, with my assistance, analyze it to see if any significant patterns emerge (they will). Finally, the student would consult the Index and Brill's New Pauly (available online via Wells) to research what we know (if anything) about the named victims in the Iliad, considering to what extent our knowledge of the victims' identity adds to our understanding of casualty patterns in the Iliad. Because this is research I have largely already conducted, the student would assist me by verifying the data I collected and gathering the results of relevant scholarship that I have already identified.

Expected skill development:

• develop skills in close and attentive reading
• learn how to collect and analyze data from a work of literature
• learn how to analyze a text with a specific research agenda
• learn how to identify meaningful patterns in ancient literature
• learn how to critically examine data of other scholars and reassess one's own
• data in light of the findings of others
• gain detailed knowledge of a foundation work of literature

Students join the Social Media & Hate Research Lab as members, not as assistants on a single task. The lab studies how antisemitic and other hateful narratives circulate on Instagram, TikTok, X, and YouTube — much of it implicit rather than overt: coded references, conspiracy framings, comment-section pile-ons. You attend the weekly lab meeting, where the full team presents work in progress, and you join one project subgroup: annotating posts against a research codebook, labeling accounts and groups, testing how AI language models handle contested political claims, or background research for a study in preparation. Subgroups meet separately, so you work closely with the doctoral and postdoctoral researchers on your project. The first weeks are training — you calibrate your judgments against already-classified material before contributing new data. By the end of the semester you present something of your own to the lab. No technical background required. Some material is hateful; we limit session length, discuss it openly, and you may opt out of any subcategory.

Expected skill development:

• Applying a formal research codebook — turning a contested concept into consistent, defensible classification decisions
• Recognizing implicit and coded bias in online content: irony, dog whistles, conspiracy framings, visual and comment-thread context
• Measuring and interpreting agreement between coders, and diagnosing why trained readers disagree
• Handling large public social media datasets: sampling, organizing, and documenting data in spreadsheets and shared research tools
• Structured evaluation of AI language model outputs against defined criteria
• Research ethics and responsible work with sensitive material
• Locating and synthesizing scholarly literature in antisemitism studies, communication, and social science
• Communicating work in progress to a research team — short analytic memos and an end-of-semester presentation

This project investigates public resistance to government surveillance technologies, focusing on automated license plate readers (ALPRs) deployed by Flock Safety. We examine how grassroots collective action bridges digital mapping, online discourse, and municipal governance. Specifically, the study traces how citizens identify and crowdsource camera coordinates on open-mapping platforms (such as DeFlock and OpenStreetMap) and mobilize debate across social media forums. The research analyzes how this online chatter translates into direct offline civic engagement, such as testimony at city council and town hall meetings, and whether these efforts drive municipal contract cancellations, non-renewals, or moratoriums.

Under faculty mentorship, the student will engage directly in the research process for 8-10 hours per week. Responsibilities include gathering and cross-referencing open-source mapping data, coding qualitative civic discourse from social media posts and public meeting minutes, and tracking legislative outcomes across target municipalities. This opportunity gives early honors students hands-on training in open-source intelligence (OSINT), using machine learning (ML/AI) for social media text analysis, and applied econometrics to measure policy outcomes.

Expected skill development:

• Open-Source Intelligence (OSINT): Navigating open platforms (e.g., OpenStreetMap, DeFlock) to gather, verify, and map surveillance infrastructure data.
• Machine Learning (ML/AI): Using natural language processing to analyze large volumes of social media chatter and public sentiment.
• Applied Econometrics: Assisting with quantitative models to measure the causal impact of citizen mobilization on local government policy decisions.
• Municipal Policy Research: Locating and extracting data from public records, city council meeting minutes, and local procurement contracts.
• Data Management: Synthesizing spatial, qualitative, and legislative data into clean, structured datasets for academic analysis.

The City of Austin has formally denied exactly one apartment project in twenty years of records. Yet thousands of proposed buildings there died before a shovel touched the ground. Where did they go, and who was holding the clock, the city or the developer? This placement builds the first multi-city record of how apartment projects move through municipal approval, review round by review round, from zoning application to site plan to building permit. You will work like an investigative researcher: file public-records requests under state open-records laws, talk with city planners, decode the permitting systems cities run on, and turn what comes back into clean data no one has assembled before. The data feed two live studies: one on how delay and uncertainty in city approvals shape housing supply and rents nationwide, and one evaluating Texas's 2025 by-right zoning law, which lets developers build apartments on commercial land without a city vote. You will watch your work travel from a phone call with a records clerk to a figure in a working paper to the national debate over why housing costs so much.

Expected skill development:

• Filing and tracking public-records requests with city governments
• Professional communication with city planners and records officers
• Reading municipal codes, ordinances, and council records
• Data cleaning: turning city extracts and portal records into usable datasets
• Organizing data: consistent file naming, documentation, and codebooks
• Excel and basic Python for cleaning, merging, and plotting
• How housing supply and zoning work, and how economists study them
• Communicating research: written status notes, a poster or talk, and credit in a working paper