Past Events

Everyone has some experience of solving jigsaw puzzles. When facing ambiguities of assembling a pair of pieces, a common strategy we use is to look at clues from additional pieces and make decisions among all relevant pieces together. In this talk, I will show how to apply this common practice to develop data-driven algorithms that significantly outperform pair-wise algorithms. I will start with describing a computation framework for the joint inference of correspondences among shape/image collections.

This talk will present recent work from the Socially Intelligent Machines Lab.

Abstract: This talk will present a number of candidate technologies for vehicle to vehicle and vehicle to infrastructure communications. In more detail it will describe 802.11p and LTE applied for vehicular applications. The effect of vehicle speed, packet size and antenna correlation will be described. Simulation and emulation results will be presented. Then we will consider the challenges and opportunities of mmWave for vehicular communications.

This talk surveys the state-of-the art in RFID, energy-harvesting sensors, and devices for the Internet of Things.  Everything you know about wireless communications will be challenged, as we discuss ultra-low energy RF devices, bizarre forms of modulation, ``smart’’ antennas that do not require power, and undulating waveforms that extend the physical limits of RF energy-harvesting.  We present the engineering breakthroughs of today that will lead to real Sci-Fi applications of tomorrow:  peel-and-stick radio sensors that last forever, mm-scale wireless location capability, and

Abstract: How much of space can be filled with pairwise non-overlapping copies of a given solid? This is one of the oldest problems in mathematics, intriguing since the times of Aristotle, and remaining remarkably elusive until present times.

The need to improve capacity and energy consumption in existing and future mobile communication systems is driving intense research on how to improve the bandwidth and efficiency of mobile and base station transmitters. This presentation will give an overview of the recent research performed at Chalmers University in this area, ranging from new power amplifier circuits to advanced signal processing and system level analysis techniques.

Abstract: A fundamental problem to all data-parallel applications is data locality. There are multiple levels of data locality, as a data block can be accessed in local memory or disk, within a rack or a data center, or across data centers. Scheduling with data locality is an affinity scheduling problem with an explosive number of task types and unknown task arrival rates. As a result, existing algorithms do not apply and the recently proposed JSQ-MaxWeight (Wang et. al. 2014) algorithm for two-level locality is delay-optimal only for a special heavy traffic scenario.  

Abstract: The fast Johnson-Lindenstrauss transform has triggered a large amount of research into fast randomized transforms that reduce data dimensionality while approximately preserving geometry. We discuss uses of these fast transforms in three situations. In the first, we use the transform to precondition a data matrix before subsampling, and show how for huge data sets, this leads to great acceleration in algorithms such as PCA and K-means clustering. The second situation reconsiders the common problem of sketching for regression.

Abstract: The Intensive Care Unit (ICU) is playing an expanding role in acute hospital care, but the value of many treatments and interventions in the ICU is unproven, and high-quality data supporting or discouraging specific practices are sparse. Much prior work in clinical modeling has focused on building discriminating models to detect specific coded outcomes (e.g., hospital mortality) under specific settings, or understanding the predictive value of various types of clinical information without taking interventions into account.

Abstract: Ubiquitous sensors generate prohibitively large data sets. Large volumes of such data are nowadays generated by a variety of applications such as imaging platforms and mobile devices, surveillance cameras, social networks, power networks, to list a few. In this era of data deluge, it is of paramount importance to gather only the data that is informative for a specific task in order to limit the required sensing cost, as well as the related costs of storing, processing, or communicating the data.