All Stories

Disentangling Effect Size Heterogeneity in Meta-analysis - A Latent Mixture Approach

An article co-authored by Nan Zhang, Mo Wang, and Heng Xu was recently accepted for publication at Psychological Methods. In the article, we leveraged the recent advances in theoretical machine...

Working Paper - Implications of Data Anonymization on the Statistical Evidence of Disparity

We posted on SSRN a working paper by Heng Xu and Nan Zhang, addressing the implications of data anonymization on the statistical evidence of disparity.

Facebook Live: COVID-19's Implications for Cybersecurity

Heng Xu will be discussing COVID-19’s Implications for Cybersecurity at a webinar hosted by Kogod.

Upcoming Talk at Harvard Privacy Tools Working Group

Heng Xu and Nan Zhang, co-directors of the Robust Analytics Lab, will give a (virtual) presentation to the Harvard Privacy Tools project working group, Berkman Klein Center for Internet &...

Working Paper - From Contextualizing to Context-Theorizing in Privacy Research

We posted on SSRN a working paper co-authored by Heng Xu and Nan Zhang, “From Contextualizing to Context-Theorizing: Assessing Context Effects in Privacy Research”.

KCGC Annual Report

For the Kogod Cybersecurity Governance Center (KCGC), 2019 was the year fueled with new beginnings. Our new research projects were sponsored and recognized by federal government agencies and world leading organizations. Our new distinguished lecture series convened thought leaders from...

Robustness in Social Media Studies

On Tuesday, February 4, Nan Zhang gave a talk on robustness in social media studies at DAISY 2020, a Workshop on Trans-disciplinary Data Science in the University of Florida. The...

Validity Concerns in Using Organic Data

An article co-authored by Heng Xu, Nan Zhang, and Le (Betty) Zhou was recently accepted for publication at the Journal of Management. In the article, we provide an overview of...

AMA Best Paper Award

Heng Xu and Nan Zhang recently received a Best Paper Award from the Consumer 360° Track of the 2019 American Marketing Association (AMA) Summer Academic Conference for paper “How Much Choice is Too Much? A Machine Learning Based Meta-Analysis of...