Past Events

This talk will deal with the notions of adaptive and non-adaptive information, in the context of statistical learning and inference.  Suppose that we have a collection of models (e.g., signals, systems, representations, etc.) denoted by X and a collection of measurement actions (e.g., samples, probes, queries, experiments, etc.) denoted by Y. A particular model x in X best describes the problem at hand and is measured as follows.  Each measurement action, y in Y, generates an observation y(x) that is a function of the unknown model.

Climate data presents unique challenges for machine learning due to its spatiotemporal nature and high-dimensionality. In this talk, I will discuss two applications of high-dimensional modeling for climate data analysis. The first application is on abrupt change detection, with emphasis on detecting significant droughts in the past century. The problem is formalized as a graph-structured linear program (GSLP), and solved using KL-ADM, a novel parallel inexact alternating directions method with Bethe entropy based augmentation. KL-ADM is provably guaranteed to solve GSLPs, and is

This talk will deal with the notions of adaptive and non-adaptive information, in the context of statistical learning and inference.  Suppose that we have a collection of models (e.g., signals, systems, representations, etc.) denoted by X and a collection of measurement actions (e.g., samples, probes, queries, experiments, etc.) denoted by Y. A particular model x in X best describes the problem at hand and is measured as follows.  Each measurement action, y in Y, generates an observation y(x) that is a function of the unknown model.

Distributed storage systems (DSS) are instrumental for providing reliable storage solutions to satisfy ever growing data demands. DSS stores the content over a network of nodes, and achieves resilience against node failures by maintaining  data redundancy via various coding techniques. When some DSS nodes fail, this coding allows for retrieving the original data from the remaining nodes, as long as there is sufficient number of nodes left.

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Wireless ad hoc networks (WANETs) allow users to communicate, sharing the same wireless channel, without the need of any infrastructure. The performance of wireless ad-hoc networks (WANET) is mainly limited by its self-interference. The talk will focus on the performance of WANETs applying slotted carrier sense multiple access (CSMA) mechanism and possibly utilizing also directional antennas.

Traditionally, channel estimation has been undertaken only in service of better data communication. However, a number of problem frameworks (sonar, cognitive radio, digital watermarking) require the reconstruction of a transmitted message as well as estimating properties of the channel over which the message was transmitted. We abstract these scenarios to one wherein a source sends a message to the destination and the destination endeavors to both decode the message and estimate the channel to some fidelity.

Rank aggregation is the problem of combining multiple candidate rankings into one list that best reflects the candidates' standing as a whole. Rank aggregation has many applications,in fields as diverse as bioinformatics, coding theory and social sciences.Mathematically, the rank aggregation problem can be formulated as finding a permutation thatrepresents the ``centroid'' of a set of permutations - i.e., a permutation that minimizes the averagedistance from the given set of permutations.

Modern systems must trade off traditional performance goals with energy concerns, e.g., running faster lowers delays but increasespower usage. However, while there are well-developed theories and models for discussing computation, communication, and memory demandsof algorithms/systems, a theory for discussing the energy efficiency of an algorithm/system is only developing. In this talk, I willdescribe some recent work toward this goal, which focuses on dynamic capacity provisioning in data centers.