Siwei Liu

@siweiliu.bsky.social

Professor & Director of the Intensive Longitudinal Methods Lab at UC Davis. https://siweiliu.weebly.com/

We just got approved to hire a teaching professor in Quantitative Methods! This is the *very* rare tenure track teaching position at Berkeley. I'll be chairing the search committee. Official job ad coming later this summer!

Two new facts stand out: 1. 85% of hallucinated citations in preprints are also in the subsequent journal version (thanks, peer review!) 2. Fake cites more likely to use the names of (male) scholars who are already highly cited, creating a fake-citation Matthew effect. arxiv.org/abs/2605.07723

LLM hallucinations in the wild: Large-scale evidence from non-existent citations

Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain p...

arxiv.org

Hello all! I’m recruiting a postdoc to work with my lab and I on methods for analyzing intensive longitudinal timeseries of psychological phenomena, with particular focus on measurement and optimization of interventions! Start would be August 2026

Research Associate in Psychology in Charlottesville, Virginia, United States of America | Research at University of Virginia

Apply for Research Associate in Psychology job with University of Virginia in Charlottesville, Virginia, United States of America. Research at University of Virginia

jobs.virginia.edu

After 5 years of data collection, our WARN-D machine learning competition to forecast depression onset is now LIVE! We hope many of you will participate—we have incredibly rich data. If you share a single thing of my lab this year, please make it this competition. eiko-fried.com/warn-d-machi...

WARN-D machine learning competition is live » Eiko Fried

If you share one single thing of our team in 2026—on social media or per email with your colleagues—please let it be this machine learning competition. It was half a decade of work to get here, especi...

eiko-fried.com

Happy to share that our large-scale network analysis is now out in @nathumbehav.nature.com We show that networks are often supported by too little evidence from the data for results to be reported with confidence, not meaning that results are flawed but rather suggests caution in interpretation.

Nature Human Behaviour@nathumbehav.nature.com · 10mo ago

Psychometric network models have become increasingly popular in psychology and the social sciences. Huth et al. show that many reported network findings are based on weak or inconclusive evidence inviting caution when interpreting results.

🧵 US states that implemented abortion bans saw higher than expected infant mortality rates, with larger increases among Black infants and those in southern states, according to this analysis of US national vital statistics data from 2012–2023. ja.ma/4aVchPn #MedSky

Figure 1.  Trends in Biannual US Infant Mortality Rates, 2012-2023

Are psychometric networks sufficiently supported by data such that one can be confident when interpreting its results? We analysed 294 psychometric networks from 126 papers with the Bayesian approach to address this question @jmbh.bsky.social Sara Ruth van Holst @maartenmarsman.bsky.social 🧵

PsyArXivBot@psyarxivbot.bsky.social · 2y ago

Statistical Evidence in Psychological Networks: A Bayesian Analysis of 294 Networks from 126 Studies: http://osf.io/62ydg/