Mixed effects models are used routinely in the biological and social sciences to share information across groups and to account for data dependence. The statistical properties of such models are often quite good on average across groups, but may be poor for any specific group. For example, commonly-used confidence interval procedures may maintain a target coverage rate on average across groups, but have near zero coverage rate for a group that differs substantially from the others.
Today, we are collecting an immense amount of health data both inside and outside of the hospital. While clinicians are studying ever more data about their patients, they are still ignoring the vast majority of it. Transforming these observational data into actionable knowledge is challenging due to a number of reasons including the presence of confounders, missing context, and complex longitudinal relationships. At the same time, due to the high-stakes nature of healthcare, the field requires tools that are not only accurate, but also interpretable and robust.
Despite the impressive past and recent advances in medical sciences, there are still a host of chronic conditions which are not well understood and lack even consensus description of their signs and symptoms. Without such consensus, research for precise treatments and ultimately a cure is at a halt. Phenotyping these conditions, that is, systematically characterizing the signs, symptoms and other aspects of these conditions, is thus particularly needed.
Commodity operating system kernels are the foundation of our software systems, providing access control, I/O mechanisms, and memory management. However, operating system kernels are vulnerable to a variety of security attacks. Compromising the kernel allows an attacker to render any security protections, provided by the kernel or the applications running on the kernel, useless. Additionally, control of the kernel can be used to launch powerful side-channel attacks against protection systems like Intel SGX.
Over the last 20 years, technological advances have allowed for quantification of nearly all biological molecules, including DNAs, RNAs, proteins, and metabolites. These so-called ‘omic’ experiments enable a system-wide view of cellular dynamics in response to arbitrary perturbations. Due to the dimensionality of systems biology data, significant computation is required for analysis, which represents a vast space for computational research and development.
You are invited to attend a presentation and informal chat with Professors Theodoros Rekatsinas and Loris D'Antoni (Computer Sciences) on October 23rd from 12:10PM - 1:00PM in CS 1240. Join us for pizza as you hear about what they teach and the research they do with the Department of Computer Sciences. This will be followed by a question and answer session about the topics they teach/research, general questions about the CS major and job potential, what is hot/not in the area of Computer Sciences, or any other general advice they can give.
Please join us for an information session on American Family and specific openings we have within our Automation and Technology teams. We have openings that we are looking to fill with recent or December 2018 graduates.