University of California-Berkeley · Mathematics & StatisticsThe HIV Analytics Lab
Laura B Balzer · Berkeley, California
Research Focus
Dr. Laura B. Balzer's lab focuses on using advanced statistical methods, particularly machine learning, to improve strategies for preventing HIV at a population level. The research aims to address the challenges posed by cluster randomized trials, which are critical for understanding how well these prevention strategies work in different community settings. Through their work, the lab seeks to enhance the effectiveness of HIV prevention efforts and optimize how these strategies are applied across varying contexts.
HIV preventionmachine learningcluster randomized trialsimplementation strategiesbiostatistics
Research Aims
5AIM 01Develop new machine learning methods to minimize bias in cluster randomized trials.
AIM 02Evaluate the effectiveness of HIV prevention strategies at the group level.
AIM 03Identify and analyze factors that influence health outcomes in diverse populations.
AIM 04Disseminate new methods to the research community to improve reproducibility.
AIM 05Utilize secondary data analyses to generate insights on prevention strategy implementation.
Skills the Lab Values
data analysismachine learningstatistical modelingmultilevel data analysiscausal inferenceR programmingproject managementdata visualizationreproducible research
Output against the department
Publications181bottom 37% of dept.82nd of 131
this lab
most publishedleast published
Citations3.4kbottom 24% of dept.99th of 131
this lab
most citedleast cited
Career totals from OpenAlex, compared against labs that share a department. A department spans everything from one paper to tens of thousands, so bar heights are on a log scale — otherwise the largest lab would flatten every other bar to a line.