Calendar

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Eduardo Lopez, Assistant Professor
Department of Computational and Data Sciences
George Mason University

A Network Theory of Inter-Firm Labor Flows

Monday, March 5, 4:30-5:45
Exploratory Hall, Room 3301

Abstract: Using detailed administrative microdata for two countries, we build a modeling framework that yields new explanations for the origin of firm sizes, the firm contributions to unemployment, and the job-to-job mobility of workers between firms. Firms are organized as nodes in networks where connections represent low mobility barriers for workers. These labor flow networks are determined empirically, and serve as the substrate in which workers transition between jobs. We show that highly skewed firm size distributions are a direct consequence of the connectivity of firms. Further, our model permits the reconceptualization of unemployment as a local phenomenon, induced by individual firms, leading to the notion of firm-specific unemployment, which is also highly skewed. In coupling the study of job mobility and firm dynamics the model provides a new analytical tool for industrial organization and may make it possible to synthesize more targeted policies managing job mobility.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

James Glasbrenner, Assistant Professor
Department of Computational and Data Sciences
George Mason University

Using data science and materials simulations to control the corkscrew magnetism of MnAu₂

Monday, March 19, 4:30-5:45
Exploratory Hall, Room 3301

Materials occupy a foundational role in our society, from the silicon-based chips in our smartphones to the metals used to manufacture automobiles and construct buildings. The sheer variety in materials properties enables this wide range of use, and studying the atoms that bond together to form solids reveals the microscopic origin behind these properties. Remarkably, many properties can be traced to the behavior of and interaction between electrons, and computational simulations such as density functional theory calculations are used to study the features and macroscopic effects of this electronic structure. This computational approach can be further enhanced through recent advances in data science, which provide powerful tools and methods for analyzing and modeling data and for handling and storing large datasets.

In this talk, I will: 1) introduce the basic concepts of computational materials science and density functional theory in an accessible manner, and 2) present calculations on the material MnAu₂ where I use density functional theory and modeling to analyze its magnetic properties. The MnAu₂ structure is layered and its magnetic ground state forms a noncollinear corkscrew that rotates approximately 50° between neighboring manganese layers. Using the results of my calculations, I will explain the electronic origin of this corkscrew state and how to control its angle using external pressure and chemical substitution. In addition to discussing the electron physics, I will place a particular emphasis on the connection between data science and how modeling was used to analyze and interpret the density functional theory calculations. This will include a new, critical reexamination of my model fitting procedure using cross-validation and feature selection techniques, which will formally test the underlying assumptions I made in the original study.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Dr. Peer Kröger, Professor
Chair of Database Systems and Data Mining
Ludwig-Maximilians-University Munich

TBA

Monday, March 26, 4:30-5:45
Exploratory Hall, Room 3301

Details coming soon….

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS 

Olga Papaemmanouil, Associate Professor
Department of Computer Science at Brandeis University

Data Management Expert Discussion Seminar:
Learning-based Cost Management for Cloud Databases

Monday, April 16, 4:30-5:45
Exploratory Hall, Room 3301

Cloud computing has become one of the most active areas of computer science research, in large part because it allows computing to behave like a general utility that is always available on demand. While existing cloud infrastructures and services reduce significantly the application development time, significant effort is still required by cloud data management applications to manage their monetary cost, for often this cost depends on  a number of decisions including but not limited to performance goals, resource provisioning and workload allocation. These tasks depend on the application-specific workload characteristics and performance objectives and today their implementation burden is left on application developers.  

We argue for a substantial shift away from human-crafted solutions and towards leveraging machine learning algorithms to address the above challenges.   These algorithms can be trained on application-specific properties and customized performance goals to automatically learn how to provision resources as well as schedule the execution of incoming query workloads with low cost. Towards this vision, we have developed WiSeDB, a  learning-based cost  management service for cloud-deployed data management applications. In this talk, I will discuss how WiSeDB leverages  (a) supervised learning to automatically learn cost-effective models for guiding query placement, scheduling, and resource provisioning decisions for batch processing, and (b) reinforcement learning  to offer low cost online processing solutions, while being adaptive to resource availability and decoupled from notoriously inaccurate performance prediction models.

Speaker Bio:  Dr. Papaemmanouil is an Associate Professor in the Department of Computer Science at Brandeis University. Her research interest lies in the area of data management with a recent focus on cloud databases, data exploration, query optimization and query performance prediction. She received her undergraduate degree in Computer Science and Informatics at the University of Patras, Greece in 1999. In 2001, she received her Sc.M. in Information Systems at the University of Economics and Business, Athens, Greece. She then joined the Computer Science Department at Brown University, where she completed her Ph.D in Computer Science at Brown University in 2008. She is the recipient of an NSF Career Award (2013) and a Paris Kanellakis Fellowship from Brown University (2002)