Gorkem Turgut (G.T.) Ozer, MBA, MSc, PhD

Picture recreated by Nano Banana – Click for the original

In the last 15 years, I have used statistical modeling and machine learning to solve causal and predictive problems, managed data science teams, advised at the executive level on business analytics and data centricity, and mentored tech startups on digital platform strategy. My "data science" expertise is centered on causal inference using statistical, machine learning, and Bayesian methods, and in exploring what-if scenarios or counterfactuals via agent-based models and ongoing work involves quasi-experimental problem solving.

Previously founded two companies, and held corporate roles driving IT strategy, digital transformation, and data science initiatives.

For updates and posts on AI, data science, and causal models, visit ozer.gt/log (generally cross-posted at linkedin.com/in/gtozer).

Research

Areas: Strategic and societal implications of digital platforms and technology; Digitization and the role of algorithms in creative industries; Methodological advancements

Published:

"On Influence in Online Creative Craft Communities and the Relevance of Virtuosity and Community Engagement." Journal of the Association for Information Systems, 0(0), 2026 (Forthcoming).

"Noisebnb: An Empirical Analysis of Home-Sharing Platforms and Residential Noise Complaints." Information Systems Research, 35(4): 1824-1847, 2024. pubsonline.informs.org/doi/10.1287/isre.2022.0070

"Digital Multisided Platforms and Women's Health: An Empirical Analysis of Peer-to-Peer Lending and Abortion Rates." Information Systems Research, 34(1):223-252, 2022. pubsonline.informs.org/doi/10.1287/isre.2022.1126
→ INFORMS ISS Bapna-Ghose Social Justice Best Paper Award

"Time Series Anomaly Detection in the Age of Big Data: Matching Data Generation Processes with Algorithms." JSM and SDSS Proceedings, American Statistical Association, 2022. ww2.amstat.org/meetings/SDSS/2022

"Combining Stock-and-flow, Agent-based, and Social Network Methods to Model Team Performance." System Dynamics Review, 34: 527-574, 2020. onlinelibrary.wiley.com/doi/abs/10.1002/sdr.1613

Ongoing:

"Let it Ride! An Empirical Investigation of Problem Gambling and the Implications of Legalized Online Sports Betting"

"Can Algorithms Represent What They Did Not Create? An Empirical Study of Human - Algorithm Complementarity in the Context of Creative Craft"

"To Ensemble or Not: Empirical Analysis of Incremental Performance Gains in Predictive Hazards Models"

"The Sources of Researcher Variation in Economics"
→ Collective effort involving a number of other researchers

"Does Black Music also matter? The Effect of the George Floyd's Death on Hip-hop Music Streaming in the United States"

"Organizational Learning in the Context of Multisided Digital Platforms: A Multi-method Simulation Study"

Teaching

Courses I've developed and taught at the University of Texas at Austin, University of Maryland, College Park, and University of New Hampshire:

Big Data and Artificial Intelligence: Strategy & Analytics ozer.gt/bigdata — Analysis of big data using modern data science tools: Data Centricity and Business Value of AI, MLOps, Causal AI and Inference in Big Data, Reinforcement Learning, and Generative Models. Uses both R & Python with VS Code and Cursor.

AI Tools and Applications ozer.gt/aitools — Docker; Git, GitHub, and Hugging Face; LLMs, AI Agents, and Agentic Workflows for Data Science (using VS Code & Cursor); AI automation tools.

Predictive Analytics and Modeling ozer.gt/predict — Covers modern predictive analytics methods: Logistic, Probit, and Poisson Regression; Decision Trees and Ensemble Methods (Random Forests, XGBoost, LightGBM, CatBoost); Lasso and Ridge Regression; Deep Learning and Neural Networks. Uses R with VS Code and Cursor.

Predictive Analytics for Business ozer.gt/data — Selected statistical and machine learning methods along with strategy and business implications. Uses R as the primary analytical tool, a first for the MBA program, and encourages the use of AI coding assistants.

Managing Information Systems: Strategy, Software, and Data ozer.gt/301 — Three modules: Strategy in technology ecosystems; Software is eating the world; Data centricity: When strategy meets software. Addresses tech-enabled business models, IS/IT fundamentals (databases, agile IT project management, cybersecurity), and the impact of information technology on today's businesses and management.

Introduction to IT Management ozer.gt/introtoit — Robust adaptive strategies, competitive advantage in the digital age, multisided platforms and network effects, digital transformation, database management, cloud computing, cybersecurity, software: DevOps and agile.

Applied Cloud Computing using AWS ozer.gt/aws (Student-initiated course) — Preparation for Amazon Web Services Solutions Architect certification by covering tools and technologies needed to architect and deploy robust and secure applications, core architectural design principles, and their applications.

Picture recreated by Nano Banana – Click for the original

Project Highlights

Data Centricity Lab — datacentricity.org

This project offers a modeling concept and provides algorithmic solutions grounded in data. Our first proof-of-concept app uses purpose built LLM agents (via system prompts and RAGs) to provide answers grounded in medical research (live at findcredible.com - temporarily closed). In another app, we are developing a meta-benchmark (CoBench) to evaluate how large language models use data in augmenting coding tasks. We also discuss business cases of data centricity in a collaborative outlet, dataduets.com.

Causal Book — causalbook.com

This is an accessible, interactive resource for the data science and causal inference audience. This book aims to complement the existing excellent texts by focusing on the idea of solution patterns, with code in R and Python, exploring different approaches (Frequentist Statistics, Machine Learning, and Bayesian), and clarifying some of the counterintuitive (or seemingly surprising) challenges faced in practice. Work-in-progress.

Teaching Innovations

In-Class Hackathons: Predictive Analytics (modeling hackathon) and Reinforcement Learning (multi-armed bandits), both with prizes.

Kaggle Competitions: Causal inference and predictive modeling using rich panel data: 10 years of data with 15 million observations on 115 variables.

AI-assisted Production: Students develop a machine learning model and deploy it to Hugging Face for live access as a tangible portfolio piece.

DataScience.day: Speaker series focused on the application of course concepts to business problems and providing networking opportunities.