Seminars archive
Upcoming Seminars   Seminar: Algebra Title to be announced Renee Bell, University of Pennsylvania   Seminar: Combinatorics On the ErdosGyarfas distinct distances problem with local constraints Cosmin Pohoata, The California Institute of Technology   Seminar: Algebra Title to be announced Eva Bayer Fluckinger, EPFL   Seminar: Algebra Joint AthensAtlanta Number Theory Seminar Larry Rolen and Bianca Viray, Vanderbilt and University of Washington   Seminar: Algebra Title to be announced Anne Qu\'eguinerMathieu, Paris   Seminar: Algebra Title to be announced Natalie Paquette, Caltech  Past Seminars   Defense: Masters A Decision Support System For Heparin Dosing Romgmei Lin, Emory University   Seminar: Algebra Patching for proper schemes Bastian Haase, Emory University   Seminar: Combinatorics The complexity of perfect matchings and packings in dense hypergraphs Jie Han, University of Sao Paulo   Defense: Dissertation Recommender System and Information Fusion in Spatial Crowdsourcing Daniel Garcia Ulloa, Emory University Venue: Mathematics and Science Center, Room W301 Show abstract Spatial Crowdsourcing (SC) refers to a series of data collection mechanisms where a set of users with a sensing or computing
device are asked to perform a set of tasks at different locations and times.\\
\\In this work, we explore some of the challenges that arise with SC and propose some solutions. These challenges concern
a proper recommendation of tasks to users in such a away that they maximize their expected utility while at the same time
maximizing the probability that all the tasks are performed. The utility for the users can be based on the tasks the expected
reward they are planning to obtain, and the distance to the assignments. These aspects can be predicted through tensorfactorization techniques. To set an example, a highpaying assignment might be far from a user, while a low paying assignment is nearby. Depending on the users’ preference, we seek to recommend a set of tasks that maximize the user’s utility. On the other hand, we also want to maximize the sum of probabilities that the tasks are performed, considering the interdependencies between users. We define the system utility as a convex linear combination of the user and the task utility and suggest approximation methods to recommend the tasks that yield the highest system utility.\\
\\We also deal with the problem of truth inference, which focuses on integrating the responses from a mobile crowdsouring
scenario and determining the true value. Many times, the answers from a mobile crowdsourcing scenario are noisy, contradicting
or have missing values. We developed a recursive Bayesian system that updates the reputation model of the users, the
probability that the users where in the correct time and location, and the probability that the reports are true or false. We
further enhanced this algorithm using a Kalman filter that predicts the true state of the event at each timestamp using a
hidden event model and which is updated with the reports from the users. Our method was compared against the naive
majority voting method as well as other stateoftheart truth inference algorithms and our method shows a considerable
improvement.   Seminar: Numerical Analysis and Scientific Computing Bridging the Gap: Math across Emory Samuel Sober, Roberto Franzosi, Gordon Berman, Emory University   Defense: Honors Thesis Application of Global Optimization to Image Registration Huiying Zhu, Emory University Venue: Mathematics and Science Center, Room E408 Show abstract Given two images, image registration aims to transform an image into a given reference image so that the two images look alike. This technique is vital in many applications, such as medical imaging and astronomy. Finding the best transform can be phrased as solving a mathematical optimization problem. Due to the nonconvexity of the objective function, commonly employed optimization techniques often generate local minimizers, limiting the accuracy of the registration. This thesis evaluates the applicability of a global optimization method, called as DDNCID, for image registration. Direct application of DDNCID in image registration could cause minimizers to be infeasible. Thus, a focus of this thesis is to add a bound constraint by imposing a barrier function into the objective function to extend DDNCID.   Seminar: Algebra On semisimplicity of tensor products in positive characteristics Vikraman Balaji, Chennai Mathematical Institute   Seminar: Algebra An arithmetic count of the lines on a cubic surface. Kirsten Wickelgren, Georgia Institute of Technology   Seminar: Combinatorics Ramsey Properties of Random Graphs and Hypergraphs Andrzej Dudek, Western Michigan University   Seminar: Numerical Analysis and Scientific Computing Accelerated Diffeomorphisms for Motion Estimation and Segmentation from Video Ganesh Sundaramoorthi, KAUST 
