Some project opportunities for Ph.D. students!

Hey! My collaborators and I are working on a bunch of interesting, important projects where we could use some help. If you’d like to work with us, send me an email with your CV and any relevant information and we can see if there’s a way to fit you into the project. Any of these could be part of a Ph.D. thesis. And with remote collaboration, this is open to anyone—you don’t have to be at Columbia University. It would help if you’re a Ph.D. student in statistics or related field with some background in Bayesian inference.

There are other projects going on, but here are a few where we could use some collaboration right away. Please specify in your email which project you’d like to work on.

1. Survey on social conditions and health:

We’re looking for help adjusting to the methodological hit that a study suffered due to COVID shut down in the middle of implementing sampling design to refresh the cohort, as well as completing interviews for the ongoing cohort members. Will need consider sampling adjustments. Also they experimented with conducting some interviews via phone or zoom during the pandemic, as they were shorter than their regular 2 hr in-person interview, so it would be good to examine item missing and imputation strategy for key variables important for the analyses that are planned.

2. Measurement-error models:

This is something that I’m interested in for causal inference in general, also a recent example came up in climate science, where an existing Bayesian approach has problems, and I think we could do something good here by thinking more carefully about priors. In addition to the technical challenges, climate change is a very important problem to be working on.

3. Bayesian curve fitting for drug development:

This is a project with a pharmaceutical company to use hierarchical Bayesian methods to fit concentration response curves in drug discovery. The cool thing here is that have a pipeline with thousands of experiments and so we want an automated approach. This relates to our work on scalable computing, diagnostics, and model understanding, as well as specific issues of nonlinear hierarchical models.

4. Causal inference with latent data:

There are a few examples here of survey data where we’d like to adjust for pre-treatment variables which are either unmeasured or are measured with error. This is of interest for Bayesian modeling and causal inference, in particular the idea that we can improve upon the existing literature by using stronger priors, and also for the particular public health applications.

5. Inference for models identifying spatial locations:

This is for a political science project where we will be conducting a survey and asking people questions about nationalities and ethnic groups and using this to estimate latent positions of groups. Beyond the political science interest (for example, comparing mental maps of Democrats and Republicans), this relates to some research in computational neuroscience. It would be helpful to have a statistics student on the project because there are some challenging modeling and computational issues and it would be good for the political science student to be able to focus on the political science aspects of the project.

4 thoughts on “Some project opportunities for Ph.D. students!

  1. Hi Andrew,

    This is something I would be super interested in. Is there also a chance you assign people to projects depending how their abilities match to them, instead of specifying the project in the email?

    Thanks!

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