Jonathan J. Park

A holiday gift from @suryadyutibaral.bsky.social for modeling longitudinal data in a non-parametric framework using continuous-time functional data analysis.

My work focuses on how we can identify and address undiagnosed heterogeneity in samples of heterogeneous time-series by drawing on techniques from graph theory and network analysis.

I also have a line of work directly in network analysis using cascading failure models and fuzzy clustering methods.

Wanted to announce that I will be recruiting graduate students this year in Quantitative Psychology here at UC Davis. If you are or know of any undergrads who are interested in dynamical systems and network analytic methods, please send them my way or get in touch!

whether a failure of a specific vertex results in overwhelming failure throughout a graph or whether the pattern of failure is similar to any other randomly selected vertex.

Adding to (1.), we conduct simulations where peripheral vertices are more influential to one another and ones where hubs are highly connected *and* influential. In the former, we can recreate the results from this paper and in the latter we find that the topology of the graph is a large player in

2. We don't allow flipped vertices to come back into the system; so, failure in our simulations is permanent.

This could be a time-scale difference in the mode of collapse. Some systems fail so quickly that other variables cannot react while in slower time-scales, this is more plausible

Thanks for the shout out, @omidvebrahimi.bsky.social. This is an interesting paper!

Our simulations are a bit different. So, the type of world our models describe are a bit different and I can describe below:

1. We assumed that the connections between vertices are weighted and heterogeneous.

Thank you, Cam! 🥹
Helps to be a part of a stellar department too 😉

“Among several undergraduates I've worked with over the years, he is definitely in the top 20 (N = 20)."

Thanks, Siwei!

I wouldn't have been able to complete this work without mentors and collaborators:
- Sy-Miin Chow
- Peter Molenaar
- @fishingwithzack.bsky.social
- Michael Hunter
- @chadshenkphd.bsky.social
- Michael Russell

In the paper, we highlight the strengths of modeling in continuous-time and contrast it with modeling in discrete-time dynamic networks.

We also highlight some key weaknesses in implementing an automated search of continuous-time dynamic networks RE: initial conditions and determining them sensibly

Unsupervised Model Construction in Continuous-Time Many of the advancements reconciling individual- and group-level results have occurred in the context of a discrete-time modeling framework. Discrete-time models are intuitive and offer relatively ...

My first paper as an Assistant Professor at UC Davis is out in the journal SEM!

In the paper, we introduce a continuous-time extension to the GIMME model implemented in OpenMx. It uses iterative tests of modification indices to construct group- and person-specific dynamic networks!

Where did all the premies go during COVID👶? In this preprint, we (me, @jonathanpark.bsky.social, & M. Cardwell) discuss how lockdown measures may have (unintentionally) changed the way that genetic conflict 🧬manifested & the frequency of some pregnancy 🤰complications osf.io/preprints/ps...

I think you’re in charge of adding people if you made the thread! I am also new and don’t know anything hahah

Nvm RE: my earlier thread. It’s here!
🙌

Thanks, Björn!
Looks like I’m too late to jump on that starter pack haha!

Have to ask while Starter Pack Mania is at its apex:

Have we made a Quant Psych starter pack? If we have, I’d love to be added but if not perhaps we can get one going.

Hello! I’m primarily doing work in dynamic network modeling and community detection. Could I be added to this? Thanks for putting this together!

Lots of new followers today thanks to @omidvebrahimi.bsky.social!

Hello everyone! I'm an Assistant Professor at @ucdavispsych.bsky.social.

I'm a quantitative psychologist studying dynamic network models in discrete- and continuous-time and how we can find commonalities in person-specific dynamics.

Penalized Subgrouping of Heterogeneous Time Series Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal...

We have a new preprint!

I'm excited to share recent work related to modeling multiple-subject, multivariate time series.

We extend the multi-VAR framework to allow for data-driven identification and penalized estimation of subgroup-specific dynamics.

arxiv.org/abs/2409.03085

Thanks, Björn! Glad you liked it; hope the reviewers do too haha

We really wanted to be clear during the empirical application that we didn't magically solve issues with starting values in continuous-time. These systems are just so much more sensitive than discrete-time ones.

Special thanks to my dissertation committee members:
@fishingwithzack.bsky.social, Mike Hunter, Chad Shenk, Mike Russell, and my advisors Sy-Miin Chow and Peter Molenaar for their help and guidance throughout my PhD.

I could not have done this without all of you!

We also tested ct-gimme on real-world data and comment on some issues that researchers fitting continuous-time models are still likely to face even with the automated behavior of ct-gimme.

We evaluated the performance of what we're calling ct-gimme in simulations and found that it outperforms N = 1 fitting in continuous-time by leveraging information across the sample prior to individual model fitting as per traditional GIMME

Hi folks!

Sharing a pre-print that we just submitted from my dissertation work.

We adapted and extended the GIMME framework for identifying group-level structure in person-specific dynamics to the continuous-time framework via modification indices

osf.io/preprints/ps...

Thanks, Aaron!

Successfully defended my dissertation.
I’m a doctor now!

Got knighted by my advisors, Sy-Miin Chow and Peter Molenaar with an engraved sword gifted to my by Alexis Santos.

Lots of folks to thank but never enough space in these character limits.

Subgrouping with Chain Graphical VAR Models Recent years have seen the emergence of an “idio-thetic” class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic pro...

Lots of exciting news in the coming months. Stay tuned!

First up, our new paper just came out with Sy-Miin Chow,
@sachaepskamp.bsky.social, and Peter Molenaar for fitting N = 1 graphical VAR models and using cluster-based methods to identify subgroups of people who share similar dynamic patterns.