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Modern demands of the statistics profession call for reimagining statistics training. The discipline needs to attract and develop students who are effective as real-world problem solvers, interdisciplinary collaborators, communicators,…
Keeping up with the research literature plays an important role in the workflow of scientists - allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape…
Methods of high-dimensional probability play a central role in applications for statistics, signal processing theoretical computer science and related fields. These lectures present a sample of particularly useful tools of high-dimensional…
While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have…
Pair programming is widely recognized as an effective educational tool in computer science that promotes collaborative learning and mirrors real-world work dynamics. However, communication breakdowns within pairs significantly challenge…
Concept inventories are standardized assessments that evaluate student understanding of key concepts within academic disciplines. While prevalent across STEM fields, their development lags for advanced computer science topics like dynamic…
Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in…
Adaptive remote instruction has led to important lessons for the future, including rediscovery of known pedagogical principles in new contexts and new insights for supporting remote learning. Studying one computer science department that…
Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it,…
Course-based undergraduate research experiences (CUREs) increase students' access to research. This lesson plan describes an interdisciplinary CURE developed to be able to involve over 60 students per semester in original research using…
From a department being resistant to change to students not buying into the new computational activities, the challenges that are faced with integrating computation into the physics undergraduate curriculum are varied. The Partnership for…
A national survey of physics faculty was conducted to investigate the prevalence and nature of computational instruction in physics courses across the United States. 1246 faculty from 357 unique institutions responded to the survey. The…
We introduce a methodology to analyze citation metrics across fields of Mathematics. We use this methodology to collect and analyze the MathSciNet profiles of Full Professors of Mathematics at all 131 R1, research oriented US universities.…
Students from statistically underrepresented minority (URM) groups and women earn a smaller fraction of undergraduate and graduate degrees in most physical sciences, particularly physics. This underrepresentation is also prevalent at the…
Quantum information science is a burgeoning research field attracting vast public and private investment in the last decade. This quick rise has led to a talent gap, where there are more open positions than new graduates who can fill these…
This paper investigates how high school students approach computing through an introductory computer science course situated in the Logic Programming (LP) paradigm. This study shows how novice students operate within the LP paradigm while…
The Robotics Major at the University of Michigan was successfully launched in the 2022-23 academic year as an innovative step forward to better serve students, our communities, and our society. Building on our guiding principle of "Robotics…
Semidefinite programs (SDPs) are a class of optimisation problems that find application in numerous areas of physics, engineering and mathematics. Semidefinite programming is particularly suited to problems in quantum physics and quantum…
The Explorations in Statistics Research workshop is a one-week NSF-funded summer program that introduces undergraduate students to current research problems in applied statistics. The goal of the workshop is to expose students to exciting,…
Data Science is a multidisciplinary field that plays a crucial role in extracting valuable insights and knowledge from large and intricate datasets. Within the realm of Data Science, two fundamental components are Information Theory (IT)…