Hutton Early Research Opportunities

This project focuses on the hormonal regulation of communication signals in South American knifefishes.  These animals produce weak electrical signals from electric organs in their tails and detect these signals with highly sensitive electroreceptor organs in their skin.  These signals vary across species, sexes, and social contexts, and they are used by the fish both to identify nearby objects and to communicate with each other. In this project, students will examine how social interactions influence plasma levels of steroid hormones (cortisol, androgens, and/or estrogens) and how manipulating these hormones influences the production of communication signals in different social contexts. Depending on the outcomes, the project will culminate in the submission of an abstract and a presentation at the annual conference of the Center for the Integrative Study of Animal Behavior. For more details on the laboratory where the project will be conducted, visit https:/efishlab.org.

Note: Participation in this project is contingent on the student obtaining medical clearance for working with animals in research and completing short online courses on laboratory safety and on the care and use of vertebrate animals.

Expected skill development:

• Recording and analyzing communication signals and other social behavior in fishes
• Animal handling
• Basic laboratory skills (measuring and preparing reagents, pipetting, experimental record keeping)
• Preparing plasma samples and conducting assays to measure hormone levels
• Preparing and administering reagents to manipulate hormone concentrations
• Statistical analysis of behavioral and/or hormonal datasets
• Scientific writing and presentations

This project investigates ice-crystal orientation fabric, a small-scale physical property that a8ects large-scale flow of the Greenland Ice Sheet. In the project, the student will examine existing ice-penetrating radar data, collected by NASA as part of Operation IceBridge, to find signatures of ice-crystal fabric. The student will analyze the extent and strength of the signatures across the Greenland Ice Sheet. The results will be synthesized with other sources of information about ice-crystal fabric, from models, radar, and ice cores, to gain validation and more complete understanding. The student will work with a
graduate student and the PI to evaluate the importance of results for the overall flow of the ice sheet. The central deliverable will be a map of fabric strength across Greenland. There will be an opportunity to present results at the regional Midwest Glaciology Meeting in West Lafayette at the end of the semester and potential for inclusion as a co-author on a publication about fabric in Greenland.

Expected skill development:

• Familiarity with GIS and mapmaking
• Analysis of ground- and ice-penetrating radar data
• Basic geospatial Python programming (for data analysis)
• Oral and written communication skills for report findings

The Early African Cinemas Lab invites Hutton Honors students to participate in a digital humanities project focused on West African film history. Students will contribute to the development of a research database documenting early cinematic practices in Senegal, Burkina Faso, Niger, and Togo during the post-independence period (1955–1981). Tasks include entering archival metadata (including film records, screening reports, and production documents) and annotating films using platforms such as AvAnnotate, linking specific timecodes to historical records and analytical insights.

This hands-on opportunity introduces students to interdisciplinary research methods at the intersection of Francophone, African, and Media Studies. Participants will work closely with faculty and graduate mentors, gaining experience in archival analysis, digital scholarship, and collaborative research workflows. Their contributions will support the Lab’s mission to make African film history more accessible to scholars and the public, while offering students meaningful engagement with primary sources, innovative tools, and emerging research methodologies.

Expected skill development:

• Archival research and metadata entry: Students will learn to interpret and organize historical data from analogue sources, including film records, screening reports, and production documents, and enter structured metadata into the EAC database.
• Film annotation using AvAnnotate: Students will gain hands-on experience with platforms such as AvAnnotate and database systems, learning how to annotate films, manage digital collections, and contribute to public-facing research tools.
• Digital humanities tools and workflows: Through guided annotation of early African films, students will develop critical viewing skills and learn to articulate historical, aesthetic, and cultural insights linked to specific moments in cinematic texts.
• Collaborative research practices: Working alongside faculty and graduate mentors, students will engage in interdisciplinary collaboration, contributing to peer-reviewed projects and participating in the Lab’s scholarly community.
• Exposure to African and Francophone cultural history: Students will assist in preparing annotated film projects for submission to Scholarly Editing, gaining exposure to academic publishing and the peer review process.
• Preparation of peer-reviewed scholarly materials: Students will deepen their
  understanding of postcolonial media, African cultural institutions, and Francophone intellectual traditions through immersive research.

This project investigates the complex relationship between food system resilience and transportation infrastructure in rural Alaska. The rural food systems face diverse and growing pressures. Their capacity to withstand or recover from these pressures, or their resilience, is critical for community well-being. Within food systems research, road access is conventionally understood to promote resilience by strengthening market access and supply chains. However, many rural residents voice concerns that road networks introduce additional stress to their local food systems. The student will investigate this critical tension by developing and applying a novel index of road access. Using publicly available data from the Alaska Department of Transportation and other sources, the student will build a geospatial index of road access. There will be opportunities to enrich this index by layering road data with key socio-economic variables or by incorporating additional dimensions of connectivity. The project will culminate in the creation of a detailed connectivity map for Alaska and a report analyzing the connectivity of selected study sites. Deliverables include a georeferenced dataset of road connectivity for Alaska; a series of high-impact visualizations, including maps and graphs, suitable for publication and presentation; and a conference-style paper synthesizing the methodology and primary research findings.

Expected skill development:

• Expertise in spatial analysis, data management, and cartographic visualization using industry-standard GIS software (ArcGIS).
• Proficiency in statistical programming and data manipulation using R and RStudio for cleaning, subsetting, and analyzing large datasets.
• Experience working with large-scale public datasets from publicly accessible sources, including the Alaska Department of Transportation and the US Census Bureau.
• Strong written and verbal communication skills for reporting complex geospatial and statistical findings in a clear and persuasive manner.

This project investigates how large language models (LLMs) can help social scientists clean, organize, and analyze complex large-scale datasets. The student will gain hands-on experience at the intersection of artificial intelligence and social science. The student will work on practical challenges central to modern computational social science, such as standardizing messy demographic data (e.g., resolving inconsistent location names or ZIP codes) and extracting key themes from vast text collections, including social media posts, community surveys, and historical archival documents. A core component of this research will involve designing and evaluating effective prompts to guide LLMs in performing sophisticated qualitative coding tasks. The student will also apply advanced natural language processing (NLP) and machine learning techniques to capture and analyze local knowledge embedded in text, such as community perceptions of food shortages or citizen-described coping strategies. A primary goal is to connect these nuanced, qualitative insights to broader demographic and environmental patterns, demonstrating the power of AI-driven methods to enrich social science inquiry. Deliverables include cleaned and well-documented datasets, prototype workflows (such as Jupyter notebooks or small-scale demos), exploratory visualizations (maps, graphs, or dashboards), and a final written output such as a short report, poster, or potentially a conference paper.

Expected skill development:

• Advanced data wrangling and standardization in Python using libraries such as pandas and regex.
• Practical experience in prompt engineering and evaluation for state-of-the-art large language models (LLMs).
• Thematic coding and text analysis using leading NLP libraries, including spaCy, Hugging Face, and NLTK.
• Spatial and demographic analysis with data visualization tools like matplotlib, geopandas, and dashboarding platforms.
• Proficiency in written and visual communication for presenting complex research findings to both technical and non-technical audiences.

This research project will investigate the widespread hydrological and societal impacts of Hurricane Helene across the southeastern United States by leveraging remote sensing-based data to map flood volume, extent, and dissipation time. The student will assist in compiling and analyzing satellite-derived flood inundation maps, public soil moisture, runoff, and rainfall indices, and crop loss or infrastructure impact records. This project will largely center on how storm-driven flooding propagates through the landscape, linking antecedent wetness conditions and post-storm dissipation to physical and socio-economic outcomes. The student will contribute to visualizing and quantifying the broader footprint of flooding, yielding valuable insights related to hazard assessment and recovery planning. The primary outcome of this project will be a short report with maps and timeseries documenting findings, with potential for a conference presentation abstract and poster.

Expected skill development:

• Analyze and interpret multi-sensor satellite and in-situ datasets
• Gain basic coding and geospatial data skills (Python, MATLAB, GIS, public APIs)
• Integrate climate, hydrological, socioeconomic, and infrastructure impact data for spatial-temporal analysis
• Communicate science results with clear graphics and a written report

2026 marks the 250th anniversary of 1776.  The occasion will be celebrated at hundreds of locations throughout the United States, and planning committees are currently at work at Indiana University and in the city of Bloomington.  For this project, the student will conduct historical research related to the year 1776 and the Declaration of Independence -- one of 26 surviving copies of which is housed at the Lilly Library here on campus.  Among the many avenues for original archival research are the history of Revolutionary War veterans who moved to Monroe County in the early 19th century; the history of 1776 commemorations in Indiana in 1826, 1876, 1926, and 1976; the history of “Indiana” in the year 1776 (before Indiana existed); the list goes on and on, depending on the student’s own particular interests.  The student will have opportunity to prepare materials contributing to special events at the Lilly Library, IU Cinema, Bloomington Early Music Festival, Wylie House Museum, a “1776 Fest” on campus, two special courses on 1776 to be taught at IU Bloomington in Spring 2026 and Fall 2026, the local county school district, and more -- planning is in progress.  2026 will itself be a historic year!

Expected skill development:

• Original archival historical research
• Experience with translating historical research for diverse public audiences
• Participating in special public history events on campus and in the city
• Strong written communication and oral presentation

This project will provide the first country-wide description of African American Language (AAL) and answer critical questions about language variation and change. Our long-term goal is to model AAL phonological patterns with respect to various social (region, age, socioeconomic status) and linguistic (stress and following phonological environment) factors. The results will provide a rich understanding of the variety of American English more generally, as well as address myths about the monolithic nature of AAL.

In this project, the student will support various phases, including participant recruitment via social media, data collection, and linguistic data analysis. These phases will allow the student to develop familiarity with participant engagement, with methods for remote collection of speech data, and with techniques for sociolinguistic analysis using computer programs for automated transcription, forced alignment, vowel measurement, data visualization, and statistical analysis.

Expected deliverables include a dataset of interviews and vowel formant measurements for 160 speakers, as well as written reports that include description and interpretation of statistical results, including illustrative visualizations.

Expected skill development:

• Online recruitment and remote speech data collection techniques
• Methods for automated and computer-mediated linguistic analysis
• Basic statistical knowledge
• Communicating science to the public
• Strong written and verbal communication for scholarly reporting

Modular categories are sophisticated algebraic structures that play a central role in various areas of mathematics, such as quantum groups, subfactor theory, and knots and link invariants. Beyond their intrinsic mathematical relevance, these categories also appear in diverse applications across high-energy physics, condensed matter physics, and quantum computing.

There are several ways to define what it means for a modular category to be "small". The goal of this project is to explore how much structural information can be obtained by fixing (a small number of) primes that divide the dimension of the category. The project will combine algebraic and number-theoretic methods with computational and numerical techniques to gain new insights into the classification of these categories.

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 divisibility and prime numbers, and building introductory familiarity with category theory.
• Building oral communication skills for presenting and discussing research findings.
• Strong written communication for mathematical expositions (focusing on writing proofs).

This project explores fundamental aspects of time in quantum physics, where time is treated not as a physical observable but merely as a label. This distinction creates significant challenges for consistently measuring time in quantum systems and for reconciling quantum theory with general relativity.

The project has two interconnected research streams. First, we will study the enigmatic nature of quantum tunneling time (the subject of this year’s Nobel Prize in physics), where, depending on the measurement scheme, time can take negative or even imaginary values. We will investigate various theoretical approaches and experimental measurements of tunneling time, seeking a unified framework within unitary quantum theory. Building on this foundation, we will investigate periodically driven quantum systems, which can exhibit unusual temporal behaviors, such as functioning as a "time machine" that manipulates quantum states in ways impossible in time-independent systems.

Together, these projects contribute to our understanding of the "problem of time" in quantum physics, a fundamental challenge in contemporary physics, while providing students with opportunities to gain perspective on this complex issue through analytical and numerical analysis, literature surveys, and focused studies of specific quantum phenomena.

Expected skill development:

• Analytical skills in linear algebra and differential equations
• Mathematical modeling and programming in Python, Mathematica, or MATLAB
• Physical intuition for concepts such as quantum tunneling and quantum time
• Understanding connections to quantum information and quantum computation
• Literature review and independent project development
• Clear written and oral communication for presenting findings

Humans routinely adapt to difficult situations rather than giving up. This project explores the brain mechanisms that support context-dependent, flexible decision-making using a game-based paradigm. We invite human subjects to play a Pacman-themed maze navigation game in which they make serial decisions in a dynamic environment. We will collect neuroimaging data to monitor brain activity throughout the gameplay; we will also let advanced artificial intelligence (AI) models play the same game to compare the brain and machine side-by-side. In this project, the student will participate in the game design as well as behavioral and neuroimaging data collection. Deliverables include a gamified task suitable for both human and AI players, a literature review on computational models of decision-making and corresponding neural substrates, and a conference abstract reporting the initial behavioral and/or neuroimaging findings.

Expected skill development:

• Basic knowledge of computational cognitive neuroscience and AI
• Programming in Python (experiment design, data processing & analysis)
• Game-based task design for both human and AI players
• Hands-on experience collecting human behavioral and neuroimaging data
• Basic neuroimaging data processing and analysis

This project uses original data contained in a sample of 1,363 letters resolving federal Title IX complaints to examine how sex discrimination unfolds on college campuses and the role of schools and the civil justice system in attempting to remediate it. In this project, the student will qualitatively and quantitatively code information in these letters to capture the stories of individual experiences of and institutional responses to sex discrimination in colleges and universities; review social scientific literatures on discrimination, education, and legal mobilization, and assist with data cleaning and formatting in STATA. Deliverables include a report documenting dataset construction, a literature review, and write-up of emergent findings.

Expected skill development:

• Understanding how to construct an original social scientific dataset.
• Expertise in reading and analyzing legal documents.
• Familiarity with STATA.
• Experience working as part of collaborative research team.
• Strong written communication for reporting findings.

Mapping Material Stories: a Digital Network of García Lorca Archives, is a collaborative digital humanities project that I’m leading with Professors Andrew A. Anderson (UVA) and Christopher Maurer (Boston University) that aims to construct an interactive transnational mapping of archives known to hold materials related to Spanish modernist writer Federico García Lorca (1898-1936). Arguably one of the most renown of 20th-century Hispanic writers and one who still looms large in contemporary cultural politics, Lorca was assassinated by fascists when he was only 38 at the start of the Spanish Civil War, and his papers were scattered globally in the fallout of war, exile and dictatorial repression. To this day, materials related to Lorca (visual art, manuscripts, letters, photographs, etc.) continue to be discovered around the world and are held in all types of situations: in personal and family archives, state archives, and private foundations. This project aims not only to identify and map the location of relevant archives, but also to tell the fascinating stories behind them. In its first prototype phase, using ArcGIS and StoryMaps, it will provide the first interactive transnational mapping of known Lorca-related archives and their stories, as well as a separate mapping of the movement of particular materials over time, allowing users to visually understand the effects of war and diaspora on material and cultural history, identify gaps and lines of future research, and recuperate human stories. Mapping Material Stories works at the intersection of memory, archive, material culture, and diaspora, and is meant both for scholars and a general international public.

Please note: The ideal candidate for this placement will be able to speak/read in Spanish. Collaboration will be in English, but some of the research carried out may be in Spanish.

Expected skill development:

• Learn how to conduct honed bibliographic searches for scholarly articles in different fields
• Learn how to write a literature review
• Learn how to scaffold research from smaller to larger projects
• Learn to manipulate and manage qualitative data for data visualization projects
• Learn about different humanities-based uses of data visualization, particularly for historical and cultural studies projects
• Gain experience in ArcGIS and StoryMaps, as well as with selection, preparation and curation of visual materials for public use
• Gain experience working within a collaborative scholarly environment
• Gain experience working in both English and Spanish in a scholarly setting

This project explores how vocabulary is learned and associated in the mind of adult learners of Spanish. We explore these associations by showing pictures of different objects to Spanish learners and recording their eye movements while they listen to a prompt or tell a story. Eye movements are a good indicator of attention allocation, prediction, and planning, but they can also indicate word associations. For example, if a person sees an image of a saxophone and a couch, while hearing the word “piano”, they are more likely to look at the saxophone, because piano and saxophone are both musical instruments, and are probably related in the person’s mind. We use these data to examine the strength of vocabulary networks after learning with different methodologies.

Expected skill development:

• Data coding with Praat (acoustic analysis software)
• Data annotation with MaxQDA or similar software for qualitative data analysis
• Data visualization
• Strong oral and written communication for reporting findings

This project is about understanding how people find words—and what goes wrong after a stroke when language is affected. Many stroke survivors have “anomia,” which means trouble pulling out the right word even when they know it. Current tests only look at what someone can say out loud, but that misses what’s happening in their mind. For example, sometimes people know the word “cat” in their head but can’t get it out, or they get stuck in a “tip-of-the-tongue” state.

We’re developing a new way to capture these hidden steps by asking participants to report what they experience internally while trying to name pictures. We’ll combine these reports with how clear and confident they feel about their inner words. This will help us build a new “Covert Access Index,” a tool that measures what’s happening inside the language system, not just what comes out.

As an undergraduate researcher, you would contribute to a project that could change how clinicians diagnose and treat language disorders.

Expected skill development:

• Designing and running experiments
• Collecting and coding responses
• Analyzing Data

This project investigates perceptions and safety concerns related to e-scooters among normally sighted, visually impaired, and blind individuals - including both e-scooter users and non-users. With the rapid expansion of e-scooter use in urban environments, issues such as unregulated parking and unpredictable riding behavior have raised accessibility and safety challenges. These issues are particularly problematic for visually impaired and blind pedestrians, who may face increased mobility risks due to tripping hazards and obstructed pathways.

Students participating in this project will play an active role in multiple aspects of the research process, including data collection, such as conducting visual assessments on normally sighted and visually impaired individuals, and administering structured surveys using REDCap. They will also assist in managing and analyzing data using SPSS to examine differences in perceptions and safety experiences across groups. In addition, students will develop analytical skills through interpreting findings, applying results to real-world accessibility and safety challenges and contributing to the writing of study findings. This project provides valuable exposure to applied research in optometry and vision science, as well as in related fields such as rehabilitation sciences, accessibility, and transportation safety.

Expected skill development:

• Conducting visual assessments and participant interactions in people with different amounts of remaining vision
• Administering and managing surveys using REDCap
• Performing statistical analyses in SPSS
• Developing data-driven critical thinking and scientific writing skills
• Understanding applied research methods

The growing presence of Muslim communities across the United States stands in contrast to the lack of reliable information about those communities’ most important institutions. This project, MosqUS, identifies, aggregates, and organizes textual and visual data about mosque building in the United States on an W3CAA-compliant mobile-enabled website, both to preserve the vital history of American Muslims and to serve as a resource for future mosque designs. Through intimate stories of mosque construction, MosqUS shares oral histories, architectural drawings, images, and other archival materials of mosques. It is the first centralized database serving as a resource for educators, historians, researchers, and members of the public seeking rigorously researched and publicly aggregated information about mosques in United States civil society. Students who participate in this project will assist with (1) conducting archival research, (2) leading outreach efforts to gather information from mosque communities, (3) transcribing interviews, (3) managing and organizing archival materials on omeka.net, and (4) co-writing contextual stories on several of the mosques.

Expected skill development:

• Archival research: Learn how to locate, interpret, and analyze historical documents.
• Oral history methods: Gain experience in interviewing, transcribing, and preserving community stories.
• Community engagement: Develop outreach skills by connecting with mosque communities across the U.S.
• Digital humanities tools: Build technical skills by organizing materials on net(a digital archiving platform).
• Research communication: Practice co-writing contextual stories that make complex histories accessible to the public.

This project is titled “Developing the ‘Triage of Reliable and Unreliable Social media Topics on Emergency Contraception’ (TRUST-EC) Classifier: A mixed methods assessment of the EC social media ecosystem.” Social media is ubiquitous in the U.S. and is a widespread and influential source of guideline-discordant health information. Engaging with information about emergency contraception (EC) that is guideline-discordant may undermine its use, leading to adverse outcomes. The TRUST-EC Project is a three-phase, mixed methods, interdisciplinary, and community-engaged study that will leverage a hybrid human and computational approach to gain a comprehensive understanding of the EC social media landscape. First, it will qualitatively evaluate EC content and information quality on three social media platforms. Second, it will assess social media post characteristics with natural language processing and machine learning. Third, it will build the TRUST-EC classifier with the goal of identifying and surveilling guideline discordant social media content over time. This role will be a core member of the research team, with the primary responsibility of contributing to the qualitative aim of the project that will categorize social media information and evaluate its accuracy with validated information-quality-assessment-tools. We expect that this work will result in manuscripts, conference presentations, and real-world impact.

Expected skill development:

• Working in a multidisciplinary research team
• Conducting qualitative research based on textual data
• Gaining familiarity with social media computational research approaches
• Developing public-facing and scholarly research communication

The IU Biomechanics Lab is conducting several projects across the realms of aging, sports performance, and clinical rehabilitation to better understand their effects on human movement. Hutton Honors students will assist with the collection, processing, and analysis of biomechanical data. Specific ongoing projects include a footwear-based intervention to assess the effects of foot strength on gait, balance, and fall risk with aging; validation of a sensor-enabled ankle brace against gold-standard lab measures; and examining the effects of bicycle crank-arm length on triathlon performance. These projects emphasize the use of biomechanical and physiological assessment technologies (e.g., force plates, motion capture, inertial measurement units, metabolic cart) and software (e.g., Qualisys Track Manager, Visual3D, MATLAB) to develop a comprehensive picture of human performance across diverse populations and contexts. Deliverables include a literature review on a subtopic within the student’s project of choice and a Visual3D pipeline for data processing. Students will be expected to produce a conference-quality abstract and subsequent poster or podium presentation as output. Opportunities to contribute to published manuscripts are available.

Expected skill development:

• Experience with human subjects data collection
• Familiarity with biomechanical hardware and software
• Understanding functional human anatomy for research and clinical applications
• Written scientific communication

What fuels college success? In this hands-on research project, you'll explore the psychological forces that enhance student learning and academic performance. Using experimental methods, you’ll help design and test brief, evidence-based interventions aimed at helping IU students thrive. These strategies may include self-persuasion, social connectedness, gratitude, and more. You'll gain practical experience by developing online surveys, implementing interventions, and analyzing real-world data—all while discovering what truly helps students learn better and achieve more. Hutton Honors student researchers are expected to present at conferences and participate in the Learning, Innovation, and Teaching Excellence (LITE) lab. In this lab, students will meet to learn about research methods and collaborate on scholarship of teaching and learning research projects.

Expected skill development:

• conduct library research
• design Qualtrics surveys
• administer Qualtrics surveys based on experiment vs. control groups
• use SPSS for data analysis
• -develop strong administrative skills to stay organized and complete tasks in a timely manner

This project focuses on the development and application of a matrix completion algorithm to uncover potential molecular targets and target tissues of environmental chemical exposures. In the project, the student will explore how coupled matrix–matrix completion (CMMC) can integrate heterogeneous biological and chemical datasets, and identify methodological challenges related to data sparsity, model generalization, and biological interpretation. The project emphasizes applying and extending matrix completion techniques to publicly available toxicogenomic and chemical screening datasets, with the goal of generating new hypotheses about chemical–target interactions. Expected deliverables include a literature review of current computational toxicology methods, an implementation of the CMMC algorithm adapted to environmental exposure data, and a written report analyzing predictive performance and biological insights. A manuscript or conference presentation may result from this work.

Expected skill development:

• Programming in Python, C++, and R (data cleaning, analysis, and visualization)
• Familiarity with toxicogenomic databases
• Basic bioinformatics knowledge
• Strong written communication for reporting findings

The project, Incarceration 101: A National Examination of Jail Handbooks, examines the health-related information that people in jails receive using a modified legal epidemiological approach. The student will contact county jails to request their jail handbook, code handbooks using a modified legal mapping process, and participating in the dissemination of our findings in journal articles or policy briefs. Expected deliverables include handbooks from jails in each state, and briefs/fact sheets/other materials to disseminate our results to the jails.

Expected skill development:

• Data collection
• Communication
• Organization
• Time management
• Policy surveillance
• Legal mapping

FIT 360 Lab is a student-powered research and applied-learning community in Indiana University’s School of Public Health–Bloomington. Grounded in exercise psychology and motivation science, we study how people make and sustain healthy choices—and how students can help lead that change. Through campus and community partnerships, we design and evaluate programs that promote mental and physical well-being in the places people live, move, and work (e.g., campus wellness, workplace health, community initiatives).

Undergraduate students contribute to literature reviews, survey and protocol development, IRB preparation, data collection and management, and dissemination (posters, briefs, manuscripts). Depending on interest and training, students may also support program delivery and coaching-adjacent activities under supervision. Our mentorship model emphasizes skill-building, reflection, and professional growth in a collaborative environment. Alumni are well prepared for graduate study and careers in public health, psychology, wellness, and related fields.

Expected skill development:

• literature reviews
• survey and protocol development
• IRB preparation
• data collection and management
• Dissemination of results (posters, briefs, manuscripts)

Hoosier Sport is a dynamic research initiative dedicated to harnessing the power of sport to improve the health, leadership, and well-being of children in local schools. Supported by the American Heart Association, Gainbridge, Parity, the Women’s Sports Foundation, and the Indiana Department of Health – and recognized by the Aspen Institute as a 2024 Project Play Champion – we are committed to advancing innovative, child-focused interventions that promote physical activity and positive youth development. We are highly motivated, producing competitive grants, published peer-reviewed manuscripts, and programming that makes a real difference in the lives of children and college students.

We strive to mentor undergraduates in a collaborative, fast-paced environment where their contributions matter. Alumni of the program are well-prepared for careers and graduate study in public health, medicine, psychology, sport management, education, and related fields.

Expected skill development:

• Hands-on experience at the intersection of sport, public health, and research
  • Data collection and management
  • Survey Development
  • Program implementation in schools
  • Literature reviews
  • Contributing to manuscripts and grant proposals
• Build valuable skills in teamwork, communication, leadership, and community engagement while learning how research translates into practice 

This research opportunity focuses on how multinational enterprises (MNEs) respond to non-market challenges in today’s dynamic global environment. It examines two key areas: (1) how firms and international content creators adapt their strategies and content in response to rising geopolitical tensions, and (2) how companies manage shifting environmental, social, and governance (ESG) expectations—for example, by reconfiguring their supply chains in response to global ESG disruptions.

The work involves literature retrieval and review, data collection, and basic analysis. These tasks help build core research skills that are valuable across academic and professional settings.

This opportunity is well-suited for students with an interest in strategy, international business, or sustainability. It offers hands-on experience in exploring how global organizations navigate political and regulatory uncertainty while adapting to broader societal expectations.

Expected skill development:

• Conduct literature searches and summarize academic sources clearly.
• Collect and organize qualitative or quantitative data.
• Perform basic data analysis or be prepared to so using tools like Excel, Stata, or Python.
• Manage documents and references with accuracy and attention to detail.
• Communicate effectively and work independently to meet deadlines.

This project investigates how inter-firm networks drive innovation, focusing on how incumbent automakers form strategic alliances around self-driving car technologies. It builds on alliance portfolio literature to examine how firms’ network structures align with evolving technological knowledge architectures—specifically, the interdependencies among AI, sensor, and automotive engineering components.

The study demonstrates that firms aligning their alliance networks with the underlying interdependencies of technological domains generate more innovation and competitive advantage. Conversely, misalignment between knowledge networks (e.g., AI and robotics) and alliance structures can constrain innovation and reduce robotaxi performance.

This research contributes to our understanding of how network resource interdependencies,alliance configurations, and technological co-evolution shape strategic value creation in complex innovation ecosystems.

Expected skill development:

• Gain deep industry insight into how the autonomous vehicle sector evolves, and how firms strategically build inter-organizational networks (via acquisitions, R&D alliances, and corporate venture capital investments) in response to technological change.
• Learn to conduct network data analysis in Python, with hands-on training using the NetworkX package for mapping and analyzing alliance and knowledge networks.
• Develop practical skills in data collection and cleaning from large-scale industry and patent datasets (e.g., Crunchbase, SDC Platinum, USPTO).
• Learn how to visualize complex network relationships using Python and interpret structural metrics such as centrality, modularity, and clustering.
• Build an understanding of how network theory and innovation strategy intersect—applying concepts from organizational theory, data science, and technology management to real-world corporate ecosystems.

This research opportunity is a chance for a student to be engaged in strategy and entrepreneurship research, catered in part to their own interests. Presently, the faculty mentor is most focused on understanding the human capital decisions of firms who have recently completed their Initial Public Offering and islooking for help in tracking and visualizing the layoff announcements of firms within the first 5 to 10 years following their public listing. This entails use of major datasets, examining media announcements, and then working with the data in both Excel and STATA (a software for statistics) to match each company announcement with additional data. Layoffs are of tremendous importance, so understanding them better is critically important. 

Expected skill development:

• Project development
• Excel and STATA software
• Data analysis

This project uses large-scale neighborhood-level movement data, detailed indicators of well-being, and both quasi-experimental and real-world variation to examine how physical movement across cities shapes social inequality and intergenerational income mobility. Using geospatial data (cellphones), consumption data (foot traffic at stores), economic connectedness (Raj Chetty - Meta) and administrative data (city dashboards), we look at the relationship between interclass physical connection and outcomes such as mental distress, obesity, youth unemployment, voting behavior, and future income. Students will work with big data and learn to manage and analyze complex datasets. They will gain a strong foundation in quantitative methods used in economics and behavioral science research. Research in this area is forthcoming at Harvard Kennedy School’s Review of Economics and Statistics, and the current project targets the Journal of Marketing.

Expected skill development:

• You will learn to manipulate Census, geospatial, and consumption data in R. Some background in R is helpful.
• Use of supercomputing resources for large datasets.
• Training in statistical analysis
• You will get a good foundation in economic and behavioral science research, and may do some editing and literature review. Research in this area is forthcoming at Harvard Kennedy School's Review of Economics and Statistics. The current project is targeting the Journal of Marketing.

This is an early stage project that uses NBA post game interviews as its key dataset to understand linguistic patterns in post game interviews. We begin by building and refining a large dataset of interviews from the NBA playoffs, ensuring accurate labeling of players, coaches, and reporters, along with proper time codes. The next stage involves automating parts of this labeling process using speech recognition and natural language processing (NLP) tools such as Whisper, followed by regular collection and organization of interviews throughout the NBA season. The project may later extend to the WNBA and college basketball for players in that league (since nearly all W players also play in college). Students will gain experience managing real-world media data, preparing it for computational analysis, and contributing to early-stage AI research at the intersection of media, entertainment, sports, language, and athlete performance.

Expected skill development:

• Text and audio labeling and cleaning for research datasets
• Use of Python, Whisper, and NLP tools
• Experience handling large, unstructured datasets
• Foundations in linguistic analysis (an interest in sports analytics is helpful but not required)

Research on warmth and competence has proliferated over time, showing that both are crucial for marketing outcomes, yet guidance on when to prioritize one over the other is lacking because of the highly fragmented results that are difficult to reconcile within the current theoretical discourse. In this project, the student will learn the process of and contribute to a large-scale meta-analysis of the marketing literature on warmth and competence. The project emphasizes understanding of the relative effectiveness of warmth versus competence in driving marketing outcomes both in general as well as under specific circumstances. Expected deliverables include a literature review on warmth and competence in marketing, identifying and encoding information from relevant academic papers, creating tables and figures with examples, observing industry practice and searching for relevant case studies, AI prompting to obtain relevant information, creating visualizations (graphs/charts) that summarize trends, and a brief, practitioner-style paper synthesizing key findings.

Expected skill development:

• Familiarity with prompting large language models
• Basic programming in a statistical package of your choice like R, SPSS, STATA, SAS… (data collection, cleaning, and analysis)
• Basic statistical knowledge
• Understanding of current marketing practices
• Strong written communication for reporting findings

The project integrates computer science, psychology, and marketing to capture and analyze social-media content, engagement behaviors, and daily mood using natural-language processing (NLP), machine learning, and ecological momentary assessments (EMA). Undergraduate research assistants will help design and test web/app interfaces, annotate social-media data, run sentiment and emotion analyses, and assist with survey implementation and data visualization. Students will gain hands-on experience with real-world research methods in computational social science and digital health, working closely with faculty and graduate mentors. This position is ideal for motivated students interested in data science, psychology, or marketing who wish to strengthen their research and technical skills for graduate study or industry careers.

Expected skill development:

• Develop practical skills in data cleaning, natural language processing (NLP), sentiment and emotion analysis, and machine learning.
• Learn to design and conduct ecological momentary assessments (EMA) and integrate survey and behavioral data.
• Gain experience with Python-based analytics, data visualization, and reproducible research workflows.
• Strengthen understanding of research ethics, privacy protection, and responsible data management.
• Acquire professional skills in collaborative research, scientific communication, and academic writing.

Artificial intelligence (AI) is transforming how cybersecurity teams detect and respond to threats. This project explores how to make these AI-driven systems more trustworthy and resilient. Large language models (LLMs) are increasingly being employed in cyber defense systems to help generate automated mitigation responses. However, these systems can sometimes be misled by ambiguous or misleading inputs. In this project, the student will design and evaluate a robustness assessment framework that safely tests how AI-enabled cyber defense systems handle such inputs. The goal is to strengthen their reliability, transparency, and safety. The student will work with a team simulating real-world scenarios in a controlled environment, analyze system behavior, and propose methods (such as improved input validation, explainability tools, or adaptive safeguards) to enhance AI assurance. An expected deliverable is a conference paper presenting the findings of this study.

This project is ideal for students interested in AI security, trustworthy automation, and robust system design, offering hands-on experience in building the next generation of defensive AI technologies that can withstand evolving cyber challenges.

Expected skill development:

• Gain hands-on experience with LLMs for cybersecurity applications.
• Learn how to train, fine-tune, and evaluate AI models to make them more reliable and secure.
• Apply AI and machine learning techniques to test how systems handle confusing or unexpected inputs.
• Explore human-AI interaction and implement explainability tools to improve transparency and trust in automated systems.
• Develop technical writing and communication skills by organizing your findings and helping prepare research materials, such as a project report or conference paper.

This research examines the impact of agentic AI systems—autonomous AI agents capable of perceiving, reasoning, and acting with minimal human intervention—on employee behavior, motivation, and adaptation within organizational business processes. As organizations increasingly automate decision-making and task execution through AI agents, understanding the behavioral and psychological implications of such automation becomes crucial. Drawing on theories from information systems, organizational behavior, and organizational routines, the study investigates how employees perceive agency, accountability, and control in AI-automated processes. Specifically, it examines (1) how perceived autonomy of AI agents influences employee trust and engagement; (2) how shifts in responsibility and control affect individual and team performance; and (3) how organizations can design governance mechanisms to balance efficiency with human oversight. The study will employ a multimethod design, combining survey-based experiments with field data from organizations that implement AI-driven automation. Findings will contribute to both theory and practice by providing insights into managing behavioral dynamics and ethical tensions in AI-enabled work environments.

Expected skill development:

• Systematic Thinking: Learn to define and structure complex research problems logically.
• Theory and Hypothesis Development: Build conceptual models grounded in behavioral and organizational theories.
• Research Design: Create innovative, valid, and feasible study designs using experimental and field methods.
• Data Collection and Analysis: Gain experience with survey design, experimental control, and statistical tools (e.g., SPSS, R, or STATA).
• Scholarly Writing and Communication: Develop academic writing skills for conference presentations and research papers.

Agentic AI offers great promise and peril for aiding student learning. We are assessing use of various AI orchestration platforms like n8n.io, Microsoft’s Copilot, and others to assess if or how faculty can wisely use AI-generated feedback to enhance the education process. We are currently testing several platforms, multiple AI, and many uses cases to learn if or how to do this well.

Candidates with these skills are especially of interest:

• An aptitude for understanding and configuring technology
• Natural curiosity
• Ability to write clearly in documenting experiments and results

It is not a traditional programming type of role, but some basic understanding of cloud APIs and keys would be helpful. Otherwise, we can train the right person in these skills. The results of this work could have high influence on the methods and use of Agentic AI at the Kelley School and for the campus.

Expected skill development:

• Deepen hands-on experience with managing data, AI prompt tuning, workflow design, running experints to document results, and making suggestions to improve multi-agent AI workflows.