Three Minute Thesis Competition

College of Arts and Sciences Pullman Qualifier

Thursday, March 5, 2026 at 3:30 p.m., Goertzen Hall RM21

The 3MT research communication competition challenges PhD students to present a compelling oration on their thesis and its significance in just three minutes—and using just one visual slide—in language appropriate to a non-specialist audience.

3MT is not an exercise in trivializing or “dumbing down” research, but an opportunity for students to consolidate their ideas, their motivation, and their research discoveries so they can be presented to a wider audience.

The CAS 3MT winner of 2026 will receive valuable awards and advance to WSU 3MT Competition on March 25. The CAS qualifier winner of 2026 will receive a $1,000 award and advance to the to the University-wide competition. The CAS second place winner will receive a $750 award, and the third place winner will receive a $500 award.

Rules and Criteria

Visit 3MT Rules for judging criteria and specific guidelines.

Eligibility

The CAS qualifying event is limited to doctoral students endorsed by their academic chair or director. Find more information about CAS 3MT Eligibility.

Past Winners


PowerPoint Template

Download the PowerPoint Slide (ppt)

Questions?

Contact the Office of the Dean at cas@wsu.edu or 509-335-4581.

2026 Participants

>> Watch the the 2026 contest on YouTube.

NameThesis Summary

Safiya Hafiz

Department of Sociology
1st Place

“Justice by Geography”: Examining the Variance in Juvenile Justice System Processing and Outcomes across Washington State

This dissertation addresses the phenomenon known as “justice by geography”, which is the idea that similarly situated youth are sentenced differently according to where they are geographically located. This creates spatial inequalities in sentencing, as similarly situated youth may be treated harsher depending on where they live. This variance in sentencing outcomes across place may be partially explained by political contexts, as politics are a means for allocating resources and the resources used for the juvenile justice system may support various punishment philosophies like rehabilitation or incapacitation. Thus, the research question, “To what extent, if any, does political context explain the variance in sentencing outcomes across place?” is addressed using multilevel modeling to account for both individual and contextual effects on juvenile justice system processing and outcomes. This project builds from past empirical research by examining politics at the county-level, using measures for political context that are less commonly used but in line with theoretical/sociological assumptions about politics, and assessing multiple stages of juvenile justice system processing. To complement the statistical analysis, a qualitative inquiry is undertaken to address how juvenile justice system processing and outcomes vary according to political context by asking, “How are courtroom actors’ decision-making processes affected by their local political context?”. To answer this question, semi-structured interviewing is utilized to elucidate the mechanisms of political contexts’ effect on juvenile justice system processing and outcomes.

McKinley Nevins

Plant Biology, School of Biological Sciences
2nd Place

Integrating above and belowground perspectives to understand the drivers of tree demography in the northwestern United States

Tree survival and growth is dependent upon acquiring resources like water and nutrients. Aboveground, trees may experience competitive and facilitative effects from close tree neighbors in acquiring or conserving resources. Belowground, trees benefit from symbioses with mycorrhizal fungi that increase water and nutrient uptake. Therefore, the drivers of tree survival and growth can be understood as a series of nested abiotic factors and biotic interactions occurring at levels of both the individual tree and the surrounding neighborhood. Yet we still lack a full understanding of how this biotic context, particularly belowground, influences tree demography and responses to environmental stress and disturbance. Filling this knowledge gap is essential in light of changing environmental conditions in the northwestern United States, which are becoming increasingly stressful and altering the availability of resources essential to tree performance. My dissertation research has aimed to elucidate the drivers of tree demography by expanding the consideration of linkages between aboveground and belowground contexts.

My dissertation uses trait-based approaches as a mechanistic way to understand species interactions and responses to the environment. I examined how the leaf and root traits of neighboring trees interact with drought and wildfire stress to influence the survival and growth of focal trees across 56 tree species in the region. These neighborhood-environment interactions produced highly variable demographic responses. To further explore the belowground context, I again applied a trait-based framework to determine how the taxonomic and functional composition of mycorrhizal fungal communities varied with environmental conditions and host tree species across the region. Then I linked tree leaf and root traits with fungal functions to assess the contribution of mycorrhizal communities and tree neighborhoods to the performance of focal trees in an old growth experimental forest. I found that tree hosts and their fungal symbionts displayed variable strategies to cope with local environmental conditions that explained variation in tree growth.

Overall, my research integrates lesser studied belowground traits and biotic interactions of trees to provide a more holistic understanding of the drivers of tree demography and responses to environmental stress in the northwestern United States. These findings underscore that understanding how trees will respond to future environmental conditions requires looking both above and belowground.


Md Borhan Uddin

Statistical Science, Department of Mathematics and Statistics
3rd Place

When Being Right Isn’t Fair: Rethinking Artificial Intelligence

Artificial intelligence is increasingly used to make decisions in healthcare, finance, and public policy. These systems are often evaluated using a single standard, accuracy. However, a model can be highly accurate while still treating different groups of people unfairly.

This research explores the tension between accuracy and fairness in artificial intelligence. Through a carefully designed set of experiments, the study examines how historical bias, group representation, and intervention strategies influence both predictive performance and equitable treatment. The findings show that under certain conditions, improving accuracy can worsen fairness, revealing a fundamental trade-off between the two.

This work argues that fairness should not be treated as an afterthought added after a model is built, but as a core design goal alongside accuracy. As AI becomes embedded in everyday decision-making, understanding who benefits and who is harmed by these systems is essential for building trustworthy technology.

Joseph Akowuah

Political Science, School of Politics, Philosophy, and Public Affairs

Electoral Fraud, Regionalism, and the Mobilization of Opposition Violence in Africa

Why do allegations of election fraud result in large-scale post-election violence in some African countries but not others? While existing research emphasizes incumbent-led post-election violence or treats fraud as an exogenous variable, it offers a limited explanation for cross-national variation in the escalatory actions of opposition actors in response to allegations of election fraud. This study advances an electoral geographic theory that explains opposition post-election violence as a function of allegations of election fraud and enduring regional patterns of election support (regionalism). I argue that allegations of election fraud generate collective grievances across opposition constituencies, but these grievances escalate into large-scale violence when opposition parties possess territorially concentrated support bases. In highly regionalized party systems, dense subnational networks lower coordination costs, intensify collective grievances, and provide the organizational capacity needed to sustain mass post-election violence. Where party support is geographically diffuse, similar grievances are less likely to produce coordinated escalation. I test this theory using causal process tracing in two African cases: Ghana and Nigeria. The evidence indicates that the post-electoral conflict in Nigeria in 2011 was driven by dense social networks of party supporters, who quickly and readily translated allegations of electoral fraud by party elites into large-scale violence. In Ghana, where these mechanisms did not exist, despite similar stakes and allegations of electoral fraud, the country remained peaceful after its 2012 elections. This paper contributes to the existing literature on how narratives about electoral integrity translate into escalatory action and potentially civil wars.

Sarah Farahani

Department of Chemistry

Applications of 3D Printing Towards the Fabrication of a Fully 3D-Printed Electrochemical Setup

The development of reliable, cost-effective, and calibration-free diagnostic tools and analytical devices for point-of-care applications has become a global, interdisciplinary effort involving materials science and analytical chemistry. This proposal aims to advance fully 3D-printed (3Dp) potentiometric sensing systems by developing, optimizing, and integrating 3Dp-ion-selective electrodes (ISEs) and solid-contact reference electrodes (SC-REs) into magnetic levitation (MagLev)-based microfluidic devices. The main hypothesis of this project is that Stereolithographic (SLA)-3Dp-methacrylate-based ion-selective membranes (the sensing element of ISEs) and optimized, novel Fused Deposition Modeling (FDM) 3Dp-ion-to-electron transducers (an inherently hydrophobic conductive filament) can be combined to create a novel fully 3Dp-electrochemical and potentiometric setup. This advanced biosensing technology aims to improve analytical performance, increase resistance to biofouling, enable embedding of various biomarkers or enzymes, and integrate with MagLev-assisted separation platforms. Objective 1 involves fabricating and characterizing 3Dp-ISEs for physiologically relevant electrolytes such as ammonium (NH₄⁺) and chloride (Cl⁻), using SLA and FDM 3D printing techniques to enhance reproducibility and performance. Objective 2 will develop a 3Dp-solid-contact-RE compatible with additive manufacturing for long-term monitoring and sample-independent operation. Objective 3 will engineer enzyme-based 3Dp-potentiometric (bio)sensors that convert neutral biomarkers, such as urea, into detectable ionic forms like NH₄⁺ for indirect potentiometric sensing. Objective 4 will integrate these sensors into a MagLev-assisted microfluidic platform for sample concentration, separation, and electrochemical detection using a protein- and metabolite-loaded bead model system. Additionally, a 3Dp-Zn²⁺-ISE will be developed to quantify Zn²⁺ released from metal-loaded beads and to address potential selectivity challenges in the presence of manganese (Mn²⁺) ions. Overall, this research establishes a foundational framework for fully 3D-printed potentiometric biosensors integrated with magnetic levitation, enabling scalable, multimodal diagnostic tools for decentralized, on-site (bio)sensing.

Shamila Gopalakrishnan

Department of Chemistry

Track! Target! Treat! Ovarian Cancer

Ovarian cancer stands out as one of the most lethal types of gynecological cancer, primarily due to its late diagnosis, aggressive progression, and the development of chemoresistance. Traditional chemotherapeutic agents, such as methotrexate, often lack specificity, resulting in potential harm to healthy tissues.

The primary focus of this thesis was to develop a multifunctional nanotherapeutic platform capable of achieving metabolic targeting, receptor-mediated delivery, and effective chemotherapy. An engineered dendrimer-based nanocarrier was modified to include 2-deoxy-D-glucose (2DG), which targets the high glucose uptake characteristic of ovarian cancer cells. Additionally, folic acid (FA) was incorporated to exploit the overexpression of folate receptors in these cancer cells, while methotrexate (MTX) was used as the therapeutic agent.

This innovative combinational design facilitates the selective tracking, transportation, and delivery of the drug to ovarian cancer cells using two distinct targeting strategies: one that leverages glucose metabolism and another that capitalizes on receptor specificity. By targeting these pathways, the approach aims to minimize side effects, simultaneously depriving cancer cells of their nutrient source while delivering chemotherapy more precisely. The findings of this study present a rationally designed nanoplatform whereby the metabolic dependencies and receptor overexpression in cancer cells are transformed into strategic therapeutic advantages, highlighting a promising avenue for targeted therapy in the treatment of ovarian cancer.