Daniel C. Posmik

Brown University

Department of Biostatistics

121 S Main St, Providence, RI 02903

My name is Daniel Posmik (He/Him/His) and I am a doctoral student in the Biostatistics Department at Brown University.

My goal is to develop scalable and assumption-lean inference methods for high-dimensional networks. I am drawn to inference problems that involve geometry and shape. I find brain imaging applications, such as connectivity and disease detection, particularly interesting. I am very fortunate to be advised by Prof. Ani Eloyan.

Previously, I worked with Prof. Guanglei Hong at the University of Chicago.

In my free-time, I am an avid outdoorsman, cyclist, and lover of dogs. If you are interested in having a chat on any of the above, please do not hesitate to reach out to me at daniel_posmik[at]brown[dot]edu.

From left to right: (1) Hiking to Everest Basecamp in Nepal in 2022, (2) Climbing at the Red River Gorge, (3) Petting my friend's dog Choji on a hike in Wisconsin

news

Apr 1, 2024	I will start my Ph.D. in Biostatistics at Brown University in the fall!
Jan 15, 2024	I have started a Graduate Research Assistantship!
Nov 10, 2023	I was invited to participate in final interviews for the McCall MacBain Scholarship at McGill University in March ‘24.

selected publications

JSFA

Predicting International Student Enrollment by Institutional Aid: A Random and Fixed Effects Approach

D.C. Posmik

Journal of Student Financial Aid, Sep 2022

Abs HTML PDF

Since the fall semester of 2016, first-time international student enrollment (ISEft) has declined at U.S. colleges and universities. This trend disrupts a steady upwards trajectory of ISEft rates. Previous research has demonstrated that various political, social, and macroeconomic factors influence the number of international students studying in the U.S. Exploiting data from the Common Data Set (CDS), I focus on the role financial aid plays as an enrollment predictor for international undergraduate students. A fixed effects model reveals that financial aid is strongly and significantly predictive of ISEft, yielding a 1.8% enrollment increase per 10% aid increase, all else equal. Interestingly, financial aid is only predictive of ISEft if it is awarded in substantial amounts. Extending the work of Bicak and Taylor (2020), I also analyze how the effectiveness of financial aid awards varies within different institutional settings. Random effects regressions reveal that rural, low research, and private universities experience considerable marginal ISEft boosts when awarding aid to international students. The findings of this work are primarily directed at institutional leaders who seek to revitalize their institution’s ISEft policy. Moreover, these insights may inform local policymakers who seek to incent ISEft.