TechConnect brings students and employers together for networking, education and connections. Twice each year, employers connect with Engineering and Computer Science students in an open, dynamic networking environment. Students come prepared with resumes to meet industry and tech representatives and learn about employment opportunities available, the characteristics employers seek, and also a realistic and insightful view of the job market and career paths for students interested in engineering and technical careers.
In mobile health (mHealth), collecting momentary activity or behavioral state labels often comes at a significantly higher cost or level of user burden than collecting unlabeled data using passive sensing. This observation motivates the idea of attempting to optimize the collection of labeled data to minimize cost or burden when developing personalized models. In this talk, I will present on-going research on the problem of developing active learning methods for use in mobile health that leverage the affordances of this domain while respecting its unique constraints.
Machine learning has been successful and is prevalent in everyday life, shaping many aspects of modern society. Nevertheless, many fundamental questions remain, and it is important to develop a proper theoretical understanding of machine learning to guide its future development. In this talk I will discuss the fundamental properties of optimization, sampling, and game dynamics for machine learning.
On the Recent Progress on the Inapproximability of High Dimensional Clustering and the Johnson-Coverage Hypothesis
Concurrent systems may execute one of an exponential number of paths resulting in a large number of potential states. This often makes it infeasible to consider all possible behaviors of a system. Scalable formal methods, while not guaranteeing correctness, can provide greater confidence about properties of a system. The talk will describe three such methods: Statistical Model Checking (SMC), Euclidean Model Checking (EMC), and Predictive Runtime Verification (PRV).
Please join us for the Computer Science Department's annual holiday celebration. Families are welcome. This is a popular event, so please register today!
Date: Friday, December 6, 2019
Place: JB Duke Hotel
Time: 5:30 pm - 8:30 pm
I will survey a variety of algorithmic settings in which one would like to compute (or at least use) distances between points that are induced by local scaling of space. I will track this idea through topics in mesh generation, surface reconstruction, and robotics. Then, I will discuss some recent results on exact computation of such distances for the special case where the local scaling of space is proportional to the distance to the input. This gives the first example of an exact computation for a so-called density-based distance.
If you want to build a high-quality machine learning product, build a large, high-quality training set. At first glance, this seems as useful as the statement “if you want to be rich, get a lot of money.” However, a key idea driving our work is that new theoretical and systems concepts including weak supervision, automatic data augmentation policies, and more, can enable engineers to build training sets more quickly and cost effectively.
We consider the (exact, minimum) k-cut problem: given a graph and an integer k, delete a minimum-weight set of edges so that the remaining graph has at least k connected components. This problem is a natural generalization of the global minimum cut problem, where the goal is to break the graph into k=2 pieces.
Anxiety is one of the main symptoms of some major disorders, such as general anxiety disorder (GAD) and attention-deficit/hyperactivity disorder (ADHD). Several biomarkers including heart rate (HR), heart rate vitality (HRV), excessive motions, etc. can be good metrics of anxiety levels, thus good metrics of GAD or ADHD. On one hand, with the development of mobile and wearable sensors, recent researches rise in the measurement of HR with wearable devices or self-reported results on mobile devices .
Computed tomography (CT) is a medical imaging technique used for the diagnosis and management of numerous conditions, including cancer, fractures, and infections. Automated interpretation of CT scans using machine learning holds immense promise: it may accelerate the radiology workflow, bring radiology expertise to underserved areas, and reduce missed diagnoses caused by human error.
The regional competition is the first tier of the ICPC International Collegiate Programming Contest (formerly known as the ACM International Collegiate Programming Contest). The Mid-Atlantic Region covers southern New Jersey, eastern Pennsylvania, Delaware, Maryland, the District of Columbia, Virginia, and North Carolina. For more information, see the Mid-Atlantic Regional Programming Contest or The ICPC International Collegiate Programming Contest.
We present a framework used to construct and analyze algorithms for online optimization problems with deadlines or with delay over a metric space. Using this framework, we present algorithms for several different problems. We present an O(D^2)-competitive deterministic algorithm for online multilevel aggregation with delay on a tree of depth D, an exponential improvement over the O(D^4 * 2^D)-competitive algorithm of Bienkowski et al. (ESA '16), where the only previously-known improvement was for the special case of deadlines by Buchbinder et al. (SODA '17).
Born a half-generation after the computer pioneers, I knew most of them. This talk will sketch an early history of computers, emphasizing the personalities rather than the technology, and the parts I know from personal experience rather than uniform coverage.