Seminars archive
Upcoming Seminars   No upcoming seminars currently scheduled.  Past Seminars   Colloquium: Computational Mathematics Computational mathematics meets medicine: Formulations, numerics, and parallel computing Andreas Mang, University of Houston   Seminar: Algebra Irrational points on random hyperelliptic curves Jackson Morrow, Emory University   Seminar: Combinatorics On strong Sidon sets of integers Sang June Lee, Duksung Women's University   Seminar: Numerical Analysis and Scientific Computing New methods in EEG/MEG source analysis Johannes Vorwerk, Scientific Computing and Imaging (SCI) Institute, University of Utah   Seminar: Algebra Cohomology of hyperkahler manifolds Nikon Kurnosov, University of Georgia   Seminar: Numerical Analysis and Scientific Computing Building Energy  Modeling, Optimization and Optimal Control Raya Horesh, IBM Research AI, TJ Watson Research Center   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. 
