I'm excited to share that I'm officially a Ph.D. candidate 🎉 Meaning I'll be on the job market this fall (gulp). My research explores human-algorithm interaction and the social impact of emerging tech at the intersection of mass communication & decision-making.

https://t.co/BgNDvgaJCj

I've noticed w/research it's often hard to find validated measures of constructs without hobbling together scales from multiple papers; now when possible I try to contribute by validating scales. Below is a scale that I hope is helpful measuresing the perceptual attributes of DOI

New paper (2025) by Sovannie Len proposes the PMSIS model: parents can use racially diverse entertainment media + "foreground co-viewing" + active mediation to improve children's intergroup socialization https://t.co/VYYkb7AgG6 Great work Sovannie 👏👏👏

https://t.co/YWnLK9M0RQ Fabulous work 👏👏👏

New study by @JanaDreston @anneohirsch & @g_neubaum reveals how users understand algorithms. Key findings: 71% have a basic understanding of algorithms but only 33% can explain how they work; users see themselves as passive actors when interacting with algorithms

Personally, I had a lot of fun on this project. It was my first time leading a mixed-methods study and an all student team. I hope this research is useful for informing design, policy, and education efforts that help people feel more empowered in the algorithmic age.

Demographics mattered too: 👩‍🦱 Women & people of color often described avoidant attitudes—seeing risks but feeling powerless. Which makes sense as they are often the target of algorithmic bias 👨 White men sometimes saw systemic risks but reported higher efficacy.

Qual findings: ⚠️ Risks clustered around mental health, privacy, fairness, and polarization. 💡 Efficacy beliefs were split into: Powerlessness, Strategic consumption (user tactics) & Collective responsibility (policy, regulation, audits)

Quant findings: 📊 People saw organizational algorithms as riskier than personal ones. 📊 But they also felt less able to mitigate bias in those systems. In other words, the higher the stakes, the less control people feel.

Drawing from the Risk Perception Attitude framework, we studied how people think about algorithmic bias in both: - Organizational algorithms (e.g., hiring, healthcare, policing) - Individual-use algorithms (e.g., search engines, facial filters)

Excited to share my new paper with with Erick Garcia, Xinyi Zhang & @LaurentH_Wang We ask: Do people see algorithmic bias as a risk—and do they feel capable of addressing it? Answer... It depends! More below 👇 https://t.co/i2JesUSvkE

https://t.co/GfxbzlpHgu

New study by @yvessj_aquino et al. provides a fabulous look at differing opinions about algorithmic bias held by healthcare professionals. 72 experts had 3 key disagreements: whether bias exists, who's responsible for fixing it & whether to include race data in AI systems

https://t.co/ffxNScviFM

New study by @jasontsukahara examines if attention control explains the 🔗 between inspection time tasks & intelligence. Key finding: attention control mediated the inspection time-intelligence relationship + people with better sustained attention showed less performance decline

We are hiring! The Stanford Psychology Department is seeking applicants for a tenure-track Assistant Professor position, with a research focus in affective science. Our ad, including application link, can be found here: https://t.co/LOrKyeOk33

https://t.co/kqySxzb24W

New from @KylieWoodman1 "baseline levels of psychopathology were significantly associated with an increased risk of developing gaming disorder 1 year later. However, there was no significant association between gaming disorder and the development or worsening of psychopathology."

https://t.co/kTGx2v0qYd

New study (2025) examines why people expect news to find them on social media (vs seeking it out). Key finding: when people habitually scroll social media w/out thinking + believe algorithms/lack of control drive SM usage, they're more likely to rely on incidental news exposure