All Seminars
| Title: Two-Hilbert spaces Mourre theory for the Laplace-Beltrami operator on manifolds with asymptotically cylindrical ends |
|---|
| Seminar: Analysis and Differential Geometry |
| Speaker: Rafael Tiedra de Aldecoa of Catholic University of Chile |
| Contact: David Borthwick, davidb@mathcs.emory.edu |
| Date: 2014-03-04 at 4:00PM |
| Venue: MSC W301 |
| Download Flyer |
| Abstract: We review some aspects of Mourre theory in a two-Hilbert spaces setting. Then we apply this theory to the spectral analysis for the Laplace-Beltrami operator on manifolds with asymptotically cylindrical ends. This is a joint work with Serge Richard (University of Nagoya). |
| Title: When Big Data Meets BRAIN Initiative: Large-Scale Structured Sparse Learning with Applications in Imaging Genomics |
|---|
| Colloquium: N/A |
| Speaker: Heng Huang of University of Texas at Arlington |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-03-03 at 4:00PM |
| Venue: MSC W303 |
| Download Flyer |
| Abstract: Sparsity is one of the intrinsic properties of real-world data, thus sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability at low computational cost, and provide great opportunities to analyze the big, complex, and diverse datasets. By enforcing properly designed structured sparsity, we can integrate the specific data structures and domain knowledge into the machine learning models to simplify data models and discover predictive patterns in big data analytics. Big Data research is accelerating the translation of biological and biomedical data to advance the detection, diagnosis, treatment and prevention of diseases, including the recently announced BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative. To address the challenging problems in current big data mining, we proposed several novel large-scale structured sparse learning models for multi-dimensional data integration, heterogeneous multi-task learning, group/graph structured data analysis, and longitudinal feature learning. We applied our new structured sparse learning models to analyze the multi-modal neuroimaging and genome-wide array data in Imaging Genomics and discover the phenotypic and genotypic biomarkers to characterize the neurodegenerative process in the progression of Alzheimer’s disease and other complex brain disorders. We also utilized our new machine learning models to analyze the Electronic Medical Records for predicting the heart failure patients’ readmission and drug side effects, detect the multi-dimensional biomarkers in The Cancer Genome Atlas (TCGA) research, and identify the brain circuitry patterns in Human Connectome. |
| Title: An Integrated Human Decision Making Model under Extended Belief-Desire-Intention Framework: Emergency Evacuation Applications |
|---|
| Seminar: Computer Science |
| Speaker: Young-Jun Son of The University of Arizona |
| Contact: TBA |
| Date: 2014-02-28 at 11:00AM |
| Venue: MSC W303 |
| Download Flyer |
| Abstract: In this talk, we discuss an integrated Belief-Desire-Intention (BDI) modeling framework for human decision making, whose sub-modules are based on Bayesian belief network, Decision-Field-Theory, and probabilistic depth first search technique. A key novelty of the proposed model is its ability to represent both the human decision-making and decision-planning functions in a unified framework. In this talk, the proposed modeling framework is demonstrated for human’s evacuation behaviors under a terrorist bomb attack situation. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from the human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE) available at The University of Arizona. A crowd simulation is then constructed, where individual human behaviors are based on what was learned from the CAVE experiments. In this work, the simulated environment and humans conforming to the proposed BDI framework are implemented in AnyLogic® agent-based simulation software, where each human entity calls external Netica BBN software to perform its perceptual processing function and Soar software to perform its real-time planning and decision-execution functions. The constructed crowd simulation is then used to test impact of several factors (e.g. demographics of people, number of policemen, information sharing via speakers) on evacuation performance (e.g. average evacuation time, percentage of casualties). Finally, we discuss other emergency evacuation applications (e.g. evacuation behaviors under fire in a factory) and research extensions for the proposed BDI framework. |
| Title: Assured Information Distillation in Social Sensing |
|---|
| Colloquium: N/A |
| Speaker: Dong Wang of University of Illinois at Urbana-Champaign |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-02-27 at 4:00PM |
| Venue: W306 |
| Download Flyer |
| Abstract: The advent of sensors and online social broadcast media (e.g., Twitter and Flickr) create a deluge of unfiltered, unstructured, and unvetted data about the physical environment. This opens up unprecedented challenges and opportunities in social sensing, where the goal is to distill assured information from social sources and devices in their possession. This talk will present a new analytical framework and theories to obtain reliable information with quality guarantees from large amounts of unreliable social sensing data. Noticeably, our analytical framework is the first to jointly model the complex interactions among three deeply coupled networks underlying the data; namely, the information, social and physical networks. The talk will also introduce a new information distillation system we built, called Apollo, which has been applied in a wide range of social sensing scenarios such as real event/disaster tracking, geo-tagging, smart road applications, and language/dialect classification. Apollo is now used by different branches at Army Research Lab (ARL). |
| Title: Solved and unsolved problems in elementary number theory |
|---|
| Seminar: Joint Athens-Atlanta Number Theory Seminar |
| Speaker: Paul Pollack of UGA |
| Contact: David Zureick-Brown, dzb@mathcs.emory.edu |
| Date: 2014-02-25 at 4:00PM |
| Venue: W302 |
| Download Flyer |
| Abstract: This will be a survey of certain easy-to-understand problems in elementary number theory about which "not enough" is known. We will start with a discussion of the infinitude of primes, then discuss the ancient concept of perfect numbers (and related notions), and then branch off into other realms as the spirit of Paul Erd\H{o}s leads us. |
| Title: Discourse-Guided and Multi-faceted Event Recognition from Text |
|---|
| Colloquium: N/A |
| Speaker: Ruihong Huang of University of Utah |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-02-25 at 4:00PM |
| Venue: MSC W303 |
| Download Flyer |
| Abstract: Events are one important type of information throughout the text. Accurately extracting significant events from large volumes of text informs the government, companies and the public regarding possible changing circumstances caused or implied by events. \\ \\ Extracting event information completely and accurately is challenging mainly due to the high complexity of discourse phenomena. In this talk, I will present two discourse-guided event extraction architectures that explore evidence and clues from wider discourse to seek out or validate pieces of event descriptions. TIER is a multilayered event extraction architecture that performs text analysis at multiple granularities to progressively "zoom in" on relevant event information. LINKER is a more principled discourse-guided approach that models textual cohesion properties in a single structured sentence classifier.\\ \\ Finding documents that describe a specific type of event is also challenging because of the wide variety and ambiguity of event expressions. I will focus on the recent multi-faceted event recognition approach that uses event defining characteristics (facets), in addition to event expressions, to effectively resolve the complexity of event descriptions. I will present a novel bootstrapping algorithm that can automatically learn both event expressions and facets from unannotated texts, which will enable fast configurations of domain-specific event detection systems. |
| Title: Bounded gaps between primes |
|---|
| Seminar: Joint Athens-Atlanta Number Theory Seminar |
| Speaker: James Maynard of Universite de Montreal |
| Contact: David Zureick-Brown, dzb@mathcs.emory.edu |
| Date: 2014-02-25 at 5:15PM |
| Venue: W302 |
| Download Flyer |
| Abstract: It is believed that there should be infinitely many pairs of primes which differ by 2; this is the famous twin prime conjecture. More generally, it is believed that for every positive integer $m$ there should be infinitely many sets of $m$ primes, with each set contained in an interval of size roughly $m\log{m}$. We will introduce a refinement of the `GPY sieve method' for studying these problems. This refinement will allow us to show (amongst other things) that $\liminf_n(p_{n+m}-p_n)<\infty$ for any integer $m$, and so there are infinitely many bounded length intervals containing $m$ primes. |
| Title: Dynamic Performance Profiling of Data Caches |
|---|
| Colloquium: N/A |
| Speaker: Ymir Vigfusson of Reykjavik University |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-02-24 at 4:00PM |
| Venue: MSC W303 |
| Download Flyer |
| Abstract: Scalable data replication protocols and layers, such as streaming, multicast and caching, enable large data-driven distributed systems to be practical. As a concrete example, large-scale in-memory object caches like memcached are now widely used to accelerate popular web sites and to reduce burden on backend databases. Yet operators still have limited visibility into how these caches should be set up to optimally accommodate the workloads they see. How much would the cache performance improve from additional cache space, or by adding more cache servers to the pool? Since resources come at a cost, to what extent would request latencies deteriorate if cache memory were repurposed for a different service?\\ \\ In this talk, I'll focus on some of the latest research questions pertaining to scalable data replication and large-scale distributed caches. In particular, I'll home in on the challenge of providing online monitoring of the cost and benefits of memory space in a large-scale cache, enabling cache operators to answer the questions above without requiring extraneous trace collection and manual offline tuning. I will introduce general and efficient algorithms for dynamically estimating hit rate curves -- histograms of cache hit rate as a function of memory size -- which can be plugged into cache replacement policies such as LRU.\\ \\ Extensive simulations on cache benchmarks indicate that these methods provide accurate estimates of hit rate at different cache sizes. Experiments on an implementation of these methods in memcached showed that hit rate curves were dynamically estimated at over 98% accuracy with only a small drop in throughput. The results are encouraging and suggest that exposing hit rate curves can be a practical method for improving provisioning and metering of large-scale data caches. |
| Title: Decision Making and Inference under Limited Information and Large Dimensionality |
|---|
| Colloquium: N/A |
| Speaker: Stefano Ermon of Cornell University |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-02-21 at 3:00PM |
| Venue: MSC W201 |
| Download Flyer |
| Abstract: Statistical inference in high-dimensional probabilistic models (i.e., with many variables) is one of the central problems of statistical machine learning and stochastic decision making. To date, only a handful of distinct methods have been developed, most notably (MCMC) sampling, decomposition, and variational methods. In this talk, I will introduce a fundamentally new approach based on random projections and combinatorial optimization. Our approach provides provable guarantees on accuracy, and outperforms traditional methods in a range of domains, in particular those involving combinations of probabilistic and causal dependencies (such as those coming from physical laws) among the variables. This allows for a tighter integration between inductive and deductive reasoning, and offers a range of new modeling opportunities. As an example, I will discuss an application in the emerging field of Computational Sustainability aimed at discovering new fuel-cell materials where we greatly improved the quality of the results by incorporating prior background knowledge of the physics of the system into the model. |
| Title: Harnessing the Power of Crowd for On-Demand Geographical Data Collection |
|---|
| Colloquium: Computer Science |
| Speaker: Cyrus Shahabi of University of Southern California |
| Contact: Li Xiong, lxiong@emory.edu |
| Date: 2014-02-20 at 12:00PM |
| Venue: MSC W303 |
| Download Flyer |
| Abstract: GeoCrowd is an online spatial crowdsourcing market (similar to Amazon’s Mechanical Turk) that matches geo tasks (i.e., tasks associated with a specific location and time such as “Take pictures of Tommy Trojan during 2012 USC-UCLA game”) to human workers. Every person with mobile devices can now act as a multi-modal sensor collecting various types of data instantaneously (e.g., picture, video). With GeoCrowd, subscribers can publish tasks with specific space and time attributes. Subsequently, the workers (with GeoCrowd mobile app) can perform the tasks if they are at the right time and at the right place and upload the results to the GeoCrowd server(s). In this talk, I first introduce our generic framework for GeoCrowd and discuss various techniques for optimal assignment of spatiotemporal tasks to human workers. Next, I show how we can extend this framework to incorporate trust in GeoCrowd in order to ensure workers satisfy a confidence value given by the task requester. Finally, I will show an application of the GeoCrowd framework in a commercial domain. |