Research Methods on Kailas Venkitasubramanian

Deploying AI across the Research Life Cycle

Fri, 06 Feb 2026 00:00:00 +0000

Deploying AI across the research life cycle

Last summer, I gave a workshop to the staff on augmenting the research proposal process with AI. Thought I’ll sketch out what an AI-assisted research workflow might actually look like for both quantitative and qualitative work. One caveat before we get into it. This is a map of possibilities, not a prescription. What you can actually do depends on your data, your project, and what UNC Charlotte currently permits. Before trying any of this, check what the university’s Office of OneIT has published on approved tools and data classification. That guidance sets the real boundaries. This is more of a documentation of experiments that hopefully will be part of how our AI strategy takes shape.

Some Thoughts on AI-Augmented Community Research at the Charlotte Urban Institute

Tue, 11 Nov 2025 00:00:00 +0000

I thought of compiling a few thoughts on using AI at the institute or broadly in organizations similar to ours. I’ve been using AI tools in my research for over a year and I still can’t decide if I’m more excited or more unsettled but I feel it’s more of the former these days. But this ambivalence feels like the right starting point for a conversation about where we, as an institute built on community trust and research, actually want to go with this. When I say AI, I mean the generative AI kind.

Switching from ArcGIS to QGIS (and a bit of R too)

Tue, 18 Feb 2025 00:00:00 +0000

I have been using ArcGIS for longer than I care to admit. I started with it during my postgraduate years, and for a long time it was just the GIS software, the one everyone used, the one you learned if you wanted to do spatial analysis seriously. Our university has an enterprise license, so it has always been available, and old habits die hard.

But lately, I find myself opening it less and less. And when I do, there is usually a nagging feeling that I could be doing this in QGIS instead.

Bayesian Improved Surname Geocoding: How It Works and Where We Use It

Sun, 15 Sep 2024 00:00:00 +0000

If you work with administrative data long enough, you run into the same wall eventually: the dataset has everything you need except race and ethnicity. Hospital discharge records, voter files, tax records, benefits enrollment data — these are often rich with information about where people live, what services they use, and what outcomes they experience. But ask whether they capture race or ethnicity, and the answer is usually no, or inconsistently, or only in ways that aren’t usable.

Using tidycensus to Analyze ACS PUMS Data

Sun, 12 May 2024 00:00:00 +0000

If you’ve spent any time working with Census data, you know the drill: pull a pre-aggregated table, get median household income by county, move on. It works, and for a lot of questions, it’s exactly what you need. But sometimes the published tables just don’t cut it. What if you want to look at wage distributions for workers with specific educational credentials? Or model individual-level outcomes rather than tract-level averages? That’s where PUMS comes in — and once you start using it, it’s hard to go back.

RSCH 8140 - Multivariate Analytical Methods

Fri, 04 Mar 2022 00:00:00 +0000

Course Summary

This course examines statistical procedures that have multiple independent and/or dependent variables, all correlated with one another to some degree. Emphases are placed on practical issues such as selecting the appropriate statistical analyses, using SPSS and R to screen and analyze data, interpreting output, presenting results, and applying these analyses in research areas. Students are trained to be critical consumers and novice producers of multivariate research.

RSCH 8150 - Structural Equation Modeling

Thu, 03 Mar 2022 00:00:00 +0000

Course Summary

Structural Equation Modeling (SEM) is designed to apply general statistical modeling techniques to establish relationships among variables. SEM provides researchers with powerful techniques that takes into account the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, multiple latent independents each measured by multiple indicators, and one or more latent dependents also each with multiple indicators. Empirical research articles that use structural equation modeling as a major analytic tool appear regularly in leading academic journals. The purpose of this course is to train doctoral students in both the conceptual and applied uses of SEM.