Bridging advanced econometrics, machine learning, and institutional effectiveness to empower data-driven decisions across higher education.
Harnessing the power of artificial intelligence, machine learning, and advanced statistical modeling to transform raw institutional data into strategic intelligence.
Logistic regression, neural networks, and ensemble models to forecast student enrollment probability. Built on multi-year longitudinal pipelines with variables spanning GPA, ACT scores, Pell grants, geography, and demographic trends.
Applied econometric techniques including ARIMA, structural break analysis (Chow tests), heteroscedasticity-robust standard errors, and fixed-effects panel models to support institutional strategic planning.
Deployment of supervised and unsupervised ML algorithms for student success prediction, early-alert systems, and program evaluation — bridging generative AI capabilities with institutional research workflows.
Interactive dashboards translating complex multivariate analysis into executive-ready insights. From enrollment pipelines to comparative university benchmarking (AAU member analysis).
End-to-end AI transformation consulting for colleges and universities — aligning data governance, IR/IE operations, accreditation compliance, and strategic planning with emerging AI capabilities. Drawing on deep experience at community colleges and research universities, with Fortune 500 applied econometrics expertise applied to higher education contexts.
THEC Quality Assurance Funding Evaluation and Independent Programmatic Accreditation Assessment — A Dual-Framework Program Quality Analysis (2020–2025)
"A comprehensive 44-page dual-framework program quality analysis of MTSU's THEC Quality Assurance Funding evaluation and independent programmatic accreditation assessment — covering 300+ programs across 7 colleges, NCAA athletics compliance, SACSCOC reaffirmation, and a red-flag priority action plan with ~$10M+ in QAF funds at stake."
— Data Scientist & Pioneer in AI Application in IR & IE Profession · GHTH.Org · April 2026
Pipeline analysis, prospect-to-enrolled probability modeling, and multi-year cohort tracking using advanced statistical methods.
Peer institution analysis, AAU university comparisons, and strategic positioning reports for leadership decision-making.
Thematic coding, NLP-assisted qualitative analysis, and quantitative synthesis of institutional survey data.
IR/IE data reporting, outcomes assessment, and continuous improvement documentation for regional and programmatic accreditation.
Institutional AI strategy development, data infrastructure assessment, and faculty/staff professional development for AI adoption.
Bespoke econometric and ML models tailored to your institution's unique research questions and strategic priorities.
Our fees are calculated using your institution's 3-year average IPEDS total headcount — combining undergraduate, graduate, certificate, and diploma students. This ensures pricing is fair, transparent, and directly proportional to institutional size.
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📋 Service Agreement: A professional service contract will be provided upon engagement to clearly define scope, deliverables, payment terms, confidentiality, and dispute resolution — protecting both parties.
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The Future of IR & IE Is AI-Driven — Attend the Most Uniquely Personalized Short Course and Stay Ahead of Your Profession.
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A scholar-practitioner at the intersection of econometrics, institutional research, and artificial intelligence in higher education.
Please contact admin@institutionalresearch.org before making any quotes from research publications.
Logistic models for predicting prospect students' probability to enroll, using longitudinal HS pipeline data.
Comparative enrollment and admissions trend analysis across AAU member institutions (Duke, U of Chicago, Vanderbilt).
Retention, persistence, and graduation analytics using multi-year cohort panel data from community colleges.
Research and frameworks for responsible, effective AI adoption across IR, IE, advising, and enrollment management.
ARIMA, structural break, and trend decomposition models applied to enrollment and demographic change over time.
Impact analysis of Pell grants, merit scholarships, and institutional aid on enrollment decision-making.
Race, Merit, Legacy, and Socioeconomic Status at America's Five Most Selective Universities, 1994–2024
"This study presents the most comprehensive multivariate analysis to date of admissions practices at America's five most selective private universities, examining race, merit, legacy, and socioeconomic background over three decades using PCA eigenvalue decomposition and logistic regression."
— GHTH.Org Research Division
Asian American enrollment at Caltech rose to 43% under race-blind admissions vs. 17% at Harvard — a 26-point gap attributable entirely to policy, not applicant quality.
43% of white Harvard admits were ALDCs vs. <16% of minority admits — making legacy admissions a powerful racial sorting mechanism independent of merit.
MIT's Black enrollment collapsed from 15% to 5% in one year post-ruling, while Harvard and Yale maintained relative stability — raising unresolved questions about race-neutral mechanisms.
PC1 (Academic Merit) explains 64.8% of total admissions variance. At Caltech, academic scores carry an OR of 2.12 — the highest of any school. At Harvard, race carries OR = 1.55.
Future Strategies Unveiled
"A data-driven comparative analysis of admissions selectivity, test score trends, financial aid generosity, long-term enrollment patterns, and yield rates across three elite research universities — Duke, University of Chicago, and Vanderbilt — spanning nearly four decades of institutional data."
— GHTH.Org Research Division
Harvard University
"A comprehensive 24-page crisis intelligence report analyzing Harvard University's five critical institutional crises — including a $686.5M federal funding impact, DOJ admissions investigation, legacy admissions crisis, NVivo CAQDAS analysis of 2,300+ survey responses, and a $58.85B Harvard Brand Value framework with five-year cash flow forecasts across three scenarios."
— GHTH.Org · April 2026
Competitive Analysis, Enrollment & Cash Flows Forecasting
"A 14-page longitudinal competitive analysis and enrollment forecasting study of North Carolina's two largest community colleges — serving the state's fastest-growing counties — using 38 years of data, structural econometric modeling with Bai-Perron breaks, and Fourier seasonal terms to project enrollment and cash flow implications through 2030–31."
— GHTH.Org · InstitutionalResearch.org · April 2026
A premium boutique hands-on training experience — bring your own data, leave with real solutions and a month of expert support.
IR directors, IE officers, data analysts, enrollment managers, and university administrators seeking hands-on AI and analytics skills.
Any institutional dataset — enrollment files, survey data, financial reports, accreditation data. Your data stays on your laptop at all times.
No refunds issued. Your registration is fully transferable to any future session at no additional cost.
After the course, you receive one month of follow-up via Zoom — scheduled at a mutually agreeable time to continue working on your institutional challenges.
* All fields required. Online registration only. Spot confirmed upon Stripe payment. Pre-course questionnaire sent via email after payment.
Securely upload your institutional data files for analysis. Each client receives a unique access code upon engagement. All data is handled with strict confidentiality and professional integrity.
Whether you're seeking a research collaboration, consulting engagement, access to a working paper, or simply want to learn more about our AI analytics work — we'd love to hear from you.
* Required fields. Our team typically responds within 2–3 business days.