Event Archive

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 [23].

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.

What kind of architecture and systems work is there to do at a software company?  It turns out that if you’re at Microsoft, a lot. We are pushing the limits of computing by considering it on a planetary scale. Quantum computing, datacenter architecture, DNA storage, cryogenic computing, and more. Come learn about some of the big problems being tackled at Microsoft.

In AI, the ability to model and reason with preferences allows for more personalized services. However, ethical priorities are also essential, if we want AI systems to make decisions that are ethically acceptable for us. Both data-driven and symbolic methods can be used to model preferences and ethical priorities, and to combine them in the same system, as two agents that need to cooperate. We describe two approaches to design AI systems that can reason with both preferences and ethical priorities.

A three-day conference from Friday, Oct. 25 – Sunday, Oct. 27, the ADT 2019 focus is on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of Computer Science, Economics and Operations Research in order to improve the theory and practice of modern decision support.

A classical problem in causal inference is that of matching treatment units to control units in an observational dataset. This problem is distinct from simple estimation of treatment effects as it provides additional practical interpretability of the underlying causal mechanisms that is not available without matching.

Hosted at Duke University and bringing together researchers, students and industry labs for a day of technical talks and posters in the broad areas of privacy, security, cryptography, and blockchains, the first-ever Triangle Area Privacy and Security Day (TAPS) is modeled after similar events occurring in Boston, DC and New York. The program will feature technical talks and a poster session.