Research: Where AI, Equity & Human Services Converge
My research examines how emerging technologies — particularly artificial intelligence and advanced analytics — shape leadership, decision-making, and equity within public-sector and nonprofit organizations. I am motivated by a central question: How can technology be designed, implemented, and governed in ways that enhance human judgment, strengthen organizational capability, and promote equitable outcomes?
Current Dissertation Research
Human-AI Collaboration and Equity in Food Access: How Emerging Technology Shapes Leadership, Decision-Making, and Outcomes in Human Services
PhD Dissertation | University of Denver, Daniels College of Business
Faculty Advisor: Daniel W. Baack, PhD
Data Partner: Plentiful (1,400+ food pantries, 2.5 million users)
Expected Completion: Spring 2027
Food insecurity affects over 47 million Americans, yet the organizations addressing this crisis operate in fragmented ecosystems with uneven technological capacity. This three-paper dissertation examines how AI shapes frontline authority, technology adoption, and service equity within a real-world food access system.
Human-AI Collaborative Leadership Emergence
Qualitative — How do frontline workers negotiate authority between human judgment and AI recommendations? Using semi-structured interviews (n=25-35) with food pantry coordinators, SNAP caseworkers, and nonprofit administrators, this paper develops a typology of human-led, AI-led, and shared-leadership configurations.
Key Theories: Perceived Mind Theory, Emergent Leadership
Technology Adoption & Diffusion
Mixed Methods — What predicts sustained technology adoption across fragmented human service organizations? Using event-history modeling of adoption timing paired with an administrator survey (n=100-150), this paper identifies organizational conditions that predict equitable versus inequitable adoption.
Key Theories: Diffusion of Innovation, Resource-Based View
Technology & Access Equity
Quasi-Experimental — Do technology-enabled access pathways reduce administrative burden equitably? Using difference-in-differences analysis leveraging state-level SNAP technology adoption, this paper estimates whether efficiency gains distribute equitably across communities differentiated by deprivation, rurality, and language access.
Key Theories: Administrative Burden, Distributional Equity
Theoretical Framework
The integrative proposition holds that technology designed to reduce burden can instead redistribute it — from organizations to clients, from advantaged to disadvantaged populations — depending on how human-AI collaboration is structured. This framework grounds all three dissertation papers and extends into Jasmine's broader research streams on algorithmic transparency, the nonprofit data divide, and computational social science methods including NLP, machine learning, and text-as-data approaches.
Published Research, Patents & Scholarly Contributions
Jasmine's scholarly and creative work spans peer-reviewed publications, conference proceedings, book contributions, patents, and editorial leadership. Her research has been published in IEEE conferences, military operations research symposia, and systems engineering proceedings, reflecting her interdisciplinary approach to solving complex problems at the intersection of technology, leadership, and human services.
Book Contribution
Motupalli, J. (2022). "Iraq and Afghanistan deployment inspired by Ida B. Wells' WWI fight." In J. Orth-Veillon (Ed.), Beyond Their Limits of Longing: Contemporary Writers & Veterans on the Lingering Stories of WWI (pp. 123–125). MilSpeak Books.
Case Study
"How Gusto improves customer experience by finding actionable insights faster with Sisu" (2021)
Editorial
Lead editor and co-editor, Proceedings of the Annual General Donald R. Keith Memorial Conference (2016, 2017). Society for Industrial and Systems Engineering.
Peer-Reviewed & Conference Publications
- Sheetz, L., Ivy, S., Conn, D.B., & Motupalli, J. (2019). "Developing a Model for Increasing Leadership and Diversity in STEM." IEEE Integrated STEM Education Conference, 129–135.
- Hess, C., Kubisch, J., Motupalli, J., et al. (2018). "Optimizing UAS Mission Training Resource Allocation." Keith Memorial Conference.
- Bearden, M., et al. & Motupalli, J. (2017). "Optimizing UAS Mission Training Needs through Tradespace Analysis." Keith Memorial Conference. Best Paper — 2nd Place
- Motupalli, V. & Motupalli, J. (2016). "Predicting Risk for Incidences of Homelessness among Veterans of Iraq and Afghanistan." Army Operations Research Symposium.
Patents (Pending)
Entity Issue Models
Machine Learned Entity Issue Models for Centralized Database Predictions (29740-55756/US)
Resource Allocation Models
Machine Learned Resource Allocation Models for Centralized Database Predictions (29740-55757/US)
Entity Action Models
Machine Learned Entity Action Models for Centralized Database Predictions (29740-55758/US)
Empowering the Next Generation of Data Leaders
Jasmine brings nearly a decade of classroom experience to her teaching, spanning West Point, the University of Denver, Maven, and General Assembly. Her approach combines rigorous quantitative methods with real-world application, empowering students to source, clean, analyze, and communicate insights from data. She emphasizes empathy-driven analytics — the idea that data should ultimately serve people. Whether she's guiding cadets through systems design capstones with Boeing and Lockheed Martin or helping online learners master customer analytics with R, Jasmine creates learning environments that challenge students intellectually while connecting analytical skills to meaningful, real-world outcomes.
Current Teaching Roles
University of Denver, Daniels College of Business
2026 – Present
INFO 1020: Analytics II — Business Statistics & Analysis
Guest Lecturer: Strategic Alliances for Impact
Maven (Online Ed.)
2024 – Present
Customer Data Analytics with R
Covers R programming, statistical inference, hypothesis testing, A/B testing, ANOVA, regression modeling
Student Rating: 4.9 / 5.0
Custom Curriculum Developer
2024 – Present
Custom courses in R/Python foundations, data wrangling, visualization, statistical testing, modeling, sentiment analysis, and automated reporting
Previous Teaching Experience
West Point, Department of Systems Engineering
2014 – 2018
- SE350: Systems Modeling & Design — Predictive modeling, optimization, business analytics
- SE375: Statistics for Engineers — Hypothesis testing, regression, experimental design
- SE450/402/403: Systems Design Capstone — Industry-sponsored projects with Boeing, Lockheed Martin, Army R&D
- MX400: Officer Leadership & PL300: Military Leadership
Graduate, Master Teacher Certification Program (2016). Guest Lecturer at Columbia University School of International and Public Affairs.
General Assembly
2021 – 2023
Guest Instructor & Panelist — Workshops on data science careers and empathetic analytics, inspiring the next wave of data professionals to bring both technical rigor and human compassion to their work.