Conference: Collaborative Research at the Intersection of Statistics and Engineering: High-Dimensional Data Analysis

The three-day workshop, ‘Collaborative Research at the Intersection of Statistics and Engineering: High-Dimensional Data Analysis,’ will be held at the Wharton School of the University of Pennsylvania in the summer of 2026 to bring together Ph.D. students in engineering and statistics, along with the faculty from both fields. Data in engineering has grown exponentially in both the volume of data collected and the set of quantities measured. In order to make optimal use of these data sources, engineering researchers increasingly require data science expertise and knowledge of the most current statistical methods. Engineering also provides a rich source of new problems for statisticians, ranging from the types of data being collected to the volume and frequency at which those data are available and the applications being made of it, and communication across these disciplines will help to ensure that new statistical methods are targeted and relevant to engineers. The workshop will feature both expert lectures on current statistical techniques and research panels discussing the projects of participating students. The workshop is expected to have 30 participants, including the expert lecturers and panelists. The three-day summer 2026 workshop aims to mentor Ph.D. students in statistics and engineering engaged in multidisciplinary research on high-dimensional sequential data modelling. The workshop will offer students supplementary mentorship on topics beyond the expertise of their Ph.D. advisors. This in turn will build collaborations between statisticians and engineers through a structured workshop consisting of expert lectures tailored to the research projects of participating Ph.D. students and research sessions focused on discussing those projects and proposing appropriate statistical modelling approaches. While remaining open to diverse engineering topics, the workshop will emphasize statistical areas that are related to current challenges in high-dimensional data analysis. These areas are unified by the difficulty of working with large, complex datasets for which selecting or developing appropriate methods is often a major challenge. Details are provided on the workshop’s webpage at https://sites.google.com/view/hdconf/home. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.