Eventshttp://www.cam.cornell.eduEventsFri, 22 Sep 2017 07:49:08 -0400CAM Colloquium: Ohad Perry (Northwestern University) - Service Systems in which the Service Times Depend on the Delay in QueueAbstract In most queueing models it is assumed that the primitive processes (arrival, service, abandonment, etc.) are independent. However, data shows that the service time of a customer may depend on that customer’s patience, or on her delay in queue. In this talk I will discuss the impacts that such a dependency has on key performance measures (waiting times, queue length, proportion of abandonment and throughput), and on optimal capacity decisions. In particular, I will first consider a system with a single pool of many statistically-homogeneous agents serving one class of statistically-identical customers whose service requirements and patience times are dependent random variables. Since the assumed dependence renders exact analysis intractable, we develop a deterministic (fluid) approximation which is characterized via the entire joint distribution of the service and patience times. To evaluate the impacts of the dependence, we employ bivariate dependence orders, and provide structural results which facilitate revenue optimization when a staffing cost is incurred. Time permitting; I will also discuss an alternative model, in which the service times depend on the delay in queue, and how the two different models can be related and approximated via a unified fluid model. (Joint work with Allen Wu and Achal Bassamboo, Northwestern University) Bio Ohad Perry is an assistant professor in the Industrial Engineering and Management Sciences (IEMS) Department at Northwestern University, which he joined in August 2011. His undergraduate degree is in Mathematics and Statistics from Haifa University, and his M.S. and PhD degrees are from the Industrial Engineering and Operations Research (IEOR) Department at Columbia University. After completing his PhD in 2010, he spent a year and a half as a post-doctoral fellow in Centrum Wiskunde & Informatica (CWI) in Amsterdam, the Netherlands, working with Bert Zwart. His research interests center around queueing theory and applied probability. More specifically, his work focuses on developing analytical techniques, involving stochastic analysis, stability theory and dynamical-systems’ control, to design and optimize service systems, such as call centers, hospitals and inventory systems.http://www.cam.cornell.edu/news/colloquium.cfm?event=18160
http://www.cam.cornell.edu/news/colloquium.cfm?event=18160
Fri, 22 Sep 2017 15:30:00 -0400CAM Colloquium: Samory Kpotufe (Princeton University) - Efficient and Optimal Modal-Set Estimation Using k-NN GraphsAbstract: Estimating the mode or modal-sets (i.e. extrema points or surfaces) of an unknown density from a sample is a basic problem in data analysis. Such estimation is relevant to other problems such as clustering, outlier detection, or can simply serve to identify low-dimensional structures in high dimensional-data (e.g. point-cloud data from medical-imaging, astronomy, etc). Theoretical work on mode-estimation has largely concentrated on understanding its statistical difficulty, while less attention has been given to implementable procedures. Thus, theoretical estimators, which are often statistically optimal, are for the most part hard to implement. Furthermore, for more general modal-sets (general extrema of any dimension and shape), much less is known, although various existing procedures (e.g. for manifold-denoising or density-ridge estimation) have similar practical aim. I’ll present two related contributions of independent interest: (1) practical estimators of modal-sets based on particular subgraphs of a k-NN graph, which attain minimax-optimal rates under surprisingly general distributional conditions; (2) high-probability finite sample rates for k-NN density estimation, which is at the heart of our analysis. Finally, I’ll discuss recent work towards the deployment of these modal-sets estimators for clustering and medical-imaging applications. Much of the talk is based on a series of work with collaborators S. Dasgupta, K. Chaudhuri, U. von Luxburg, and Heinrich Jiang. Bio: Samory Kpotufe is an Assistant Professor at ORFE, Princeton University, and obtained his PhD in 2010 in CS at UC San Diego. He works in Statistical Machine Learning Theory, with an emphasis on exploiting low-dimensional structure in high-dimensional data. This work has won honors at major Machine Learning venues (plenary presentations at NIPS, AISTATS, and Best Student Paper at COLT).http://www.cam.cornell.edu/news/colloquium.cfm?event=18164
http://www.cam.cornell.edu/news/colloquium.cfm?event=18164
Fri, 29 Sep 2017 15:30:00 -0400CAM Colloquium: No Colloquium - Fall BreakCAM Colloquium: No Colloquium - Fall Breakhttp://www.cam.cornell.edu/news/colloquium.cfm?event=17984
http://www.cam.cornell.edu/news/colloquium.cfm?event=17984
Fri, 06 Oct 2017 15:30:00 -0400CAM Colloquium: Qiqi Wang (MIT)Title and Abstract TBAhttp://www.cam.cornell.edu/news/colloquium.cfm?event=17985
http://www.cam.cornell.edu/news/colloquium.cfm?event=17985
Fri, 13 Oct 2017 15:30:00 -0400CAM Colloquium: Chad Topaz (Williams College) - Topological Data Analysis of Biological Aggregation ModelsBiological aggregations such as bird flocks, fish schools, and insect swarms are striking examples of self-organization, and serve as the inspiration for algorithms in robotics, computer science, applied mathematics, and other fields. Aggregations give rise to massive amounts of data, for instance, the position and velocity of each group member at each moment in time during a field observation or numerical simulation. Interpreting this data to characterize the group's dynamics can be a challenge. To this end, we apply computational persistent homology — the workhorse of the field of topological data analysis — to the aggregation models of Vicsek et al (1995) and D’Orsogna et al. (2006). We assign a topological signature to each set of simulation data. This signature identifies dynamical events that traditional methods do not. Finally, we pose open questions related to topological signatures averaged over many simulations of stochastic models. This talk assumes no prior knowledge of topology. BIO Professor of Mathematics Chad Topaz (A.B. Harvard, Ph.D. Northwestern) is an applied mathematician at Williams College. Chad examines problems in biology, chemistry, physics, and the social sciences through several lenses, including data science, modeling, analysis, topology, geometric dynamical systems, numerical simulation, and experiment... all with an eye towards understanding and predicting complex behavior. Passionate about scientific communication and discourse, Chad has delivered over 100 talks at colleges, universities, and scientific meetings, and has co-organized numerous interdisciplinary minisymposia and workshops on chemical reaction diffusion systems, biological swarming, agent-based models, and related topics. His honors include a New Directions Research Professorship at the Institute for Mathematics and its Applications (the first given to a liberal arts college faculty member), a Kavli Frontiers Fellowship from the National Academy of Sciences, a Board of Trustees Award from Macalester College, and the 2013 Outstanding Paper Award of the Society for Industrial and Applied Mathematics.http://www.cam.cornell.edu/news/colloquium.cfm?event=18032
http://www.cam.cornell.edu/news/colloquium.cfm?event=18032
Fri, 20 Oct 2017 15:30:00 -0400CAM Colloquium: Austin Benson (CS, Cornell University)Abstract: Random walks are a fundamental model in applied mathematics and are a common example of a Markov chain. A standard way to compute the stationary distribution for a random walk on a finite set of states is to compute the Perron vector of the associated transition probability matrix. There are algebraic analogues of the Perron vector in terms of z-eigenvectors of transition probability tensors whose entries come from higher-order Markov chains. These vectors look stochastic, but they are derived from an algebraic substitution in the stationary distribution equation of higher-order Markov chains and do not carry a probabilistic interpretation. In this talk, I will present the spacey random walk, a non-Markovian stochastic process whose stationary distribution is given by a dominant z eigenvector of the transition probability tensor. The process itself is a vertex-reinforced random walk, and its discrete dynamics are related to a continuous dynamical system. We analyze the convergence properties of these dynamics and discuss numerical methods for computing the stationary distribution. We also provide several applications of the spacey random walk model in population genetics, ranking, and clustering data, and we use the process to analyze taxi trajectory data in New York. Bio: Austin Benson is currently a postdoctoral associate in the department of Computer Science at Cornell. He will join the faculty of Computer Science at Cornell in July 2018. Before coming to Cornell, he received his PhD in 2017 from the Institute for Computational and Mathematical Engineering at Stanford University. Before that, he received undergraduate degrees in computer science and applied mathematics from UC-Berkeley. Outside of the university, he has also spent time as an intern at Google Research, Sandia National Laboratories, and HP Labs.http://www.cam.cornell.edu/news/colloquium.cfm?event=18165
http://www.cam.cornell.edu/news/colloquium.cfm?event=18165
Fri, 27 Oct 2017 15:30:00 -0400CAM Colloquium: Rahul Roy (ISI Delhi)Title and Abstract TBAhttp://www.cam.cornell.edu/news/colloquium.cfm?event=17988
http://www.cam.cornell.edu/news/colloquium.cfm?event=17988
Fri, 03 Nov 2017 15:30:00 -0400CAM Colloquium: OpenSpeaker TBAhttp://www.cam.cornell.edu/news/colloquium.cfm?event=17989
http://www.cam.cornell.edu/news/colloquium.cfm?event=17989
Fri, 10 Nov 2017 15:30:00 -0400CAM Colloquium: OpenSpeaker TBAhttp://www.cam.cornell.edu/news/colloquium.cfm?event=17990
http://www.cam.cornell.edu/news/colloquium.cfm?event=17990
Fri, 17 Nov 2017 15:30:00 -0400CAM Colloquium: No Colloquium - Thanksgiving BreakCAM Colloquium: No Colloquium - Thanksgiving Breakhttp://www.cam.cornell.edu/news/colloquium.cfm?event=17991
http://www.cam.cornell.edu/news/colloquium.cfm?event=17991
Fri, 24 Nov 2017 15:30:00 -0400CAM Colloquium: OpenSpeaker TBAhttp://www.cam.cornell.edu/news/colloquium.cfm?event=17992
http://www.cam.cornell.edu/news/colloquium.cfm?event=17992
Fri, 01 Dec 2017 15:30:00 -0400