• CS Department receives a Community Grant for $10,000 from the Best Buy Foundation to support TLI!

    The Computer Science Department is pleased to announce that it has received a community grant for $10,000 from the Best Buy Foundation to support the department's Technology Leadership Initiative (TLI)! The grant is awarded through the Community Grants program, in partnership with local Best Buy stores, to support programs that provide youth with access to new technologies and help them become interested and fluent in digital learning while developing skills to help better prepare them for future education and career success. The Best Buy Foundation's primary goal is to give teens access to opportunities through technology that help them excel in school and develop 21st century skills for their future careers. The CS Department's Technology Leadership Initiative aims to engage students across grade levels and strives to instill a passion for computing and technical thinking. TLI consists of two major components, Tech Divaz and High School Academy. The mission of TLI is to provide under-represented and under-served students in grades 6-12 with opportunities, tools, and motivation needed to pursue Computer Science related degrees and excel academically, socially and professionally. Thanks to the Best Buy Foundation the CS Department will be able to continue and expand its TLI program!

    The Computer Science Department is pleased to announce that it has received a community grant for $10,000 from the Best Buy Foundation to support the department's Technology Leadership Initiative (TLI)! The grant is awarded through the Community Grants program, in partnership with local Best Buy stores, to support programs that provide youth with access to new technologies and help them become interested and fluent in digital learning while developing skills to help better prepare them for future education and career success. The Best Buy Foundation's primary goal is to give teens access to opportunities through technology that help them excel in school and develop 21st century skills for their future careers. The CS Department's Technology Leadership Initiative aims to engage students across grade levels and strives to instill a passion for computing and technical thinking. TLI consists of two major components, Tech Divaz and High School Academy. The mission of TLI is to provide under-represented and under-served students in grades 6-12 with opportunities, tools, and motivation needed to pursue Computer Science related degrees and excel academically, socially and professionally. Thanks to the Best Buy Foundation the CS Department will be able to continue and expand its TLI program!

  • Congratulations to Prof. Chrysanthis and Prof. Labrinidis on receiving new NSF Award!

    Prof. Chrysanthis and Prof. Labrinidis, together with colleagues from the School of Engineering (Prof. Peyman Givi, PI) and the Math Department (Prof. William Layton) received new research funding from the National Science Foundation for their project entitled Appraisal of Subgrid Scale Closures in Reacting Turbulence via DNS Big Data. The project will employ a range of strategies and computational tools for utilizing DNS data to appraise the performance of large eddy simulation (LES) predictions in turbulent combustion. The study will pave the way for LES to become the primary means of predictions for future design and manufacturing of combustion systems, while building a data sharing infrastructure, and providing educational and outreach programs to students at all levels. http://www.nsf.gov/awardsearch/showAward?AWD_ID=1609120

    Prof. Chrysanthis and Prof. Labrinidis, together with colleagues from the School of Engineering (Prof. Peyman Givi, PI) and the Math Department (Prof. William Layton) received new research funding from the National Science Foundation for their project entitled Appraisal of Subgrid Scale Closures in Reacting Turbulence via DNS Big Data. The project will employ a range of strategies and computational tools for utilizing DNS data to appraise the performance of large eddy simulation (LES) predictions in turbulent combustion. The study will pave the way for LES to become the primary means of predictions for future design and manufacturing of combustion systems, while building a data sharing infrastructure, and providing educational and outreach programs to students at all levels. http://www.nsf.gov/awardsearch/showAward?AWD_ID=1609120

  • Congratulations to Professor Adriana Kovashka on receiving a NSF CRII CISE Research Initiation Grant!

    This project develops technologies to interpret the visual rhetoric of images. The project advances computer vision through novel solutions to the novel problem of decoding the visual messages in advertisements and artistic photographs, and thus brings computer vision closer to its goal of being able to automatically understand visual content. From a practical standpoint, understanding visual rhetoric can be used to produce image descriptions for the visually impaired that align with how a human would label these images, and thus give them access to the rich content shown in newspapers or on TV. This project is tightly integrated with education. The work is interdisciplinary and can attract undergraduate students to the research from different fields. This research focuses on three media understanding tasks: (1) understanding the persuasive messages conveyed by artistic images and the strategies that those images use to convey their message; (2) exposing a photographer's bias towards their subject, e.g., determining whether a photograph portrays its subject in a positive or negative light; and (3) predicting what part of an artistic photograph a viewer might find most captivating or poignant. To enable decoding of artistic images, a large dataset is collected and annotated with a number of artistic properties and persuasion techniques that are intended for human understanding, then methods are developed to model visual symbolism in artistic images, as well as adapt positive/negative effect methods from sentiment analysis. To predict the photographer's bias towards a subject, a dataset of historical and modern portrayals of minorities and foreigners is collected, then an algorithm is created that reasons about body language and 3D layout and composition of the photo. To predict poignance, eyetracking data on a set of artistic images from famous photographers is collected, then semantic and connotation conflicts between the objects in the photographs are analyzed.

    This project develops technologies to interpret the visual rhetoric of images. The project advances computer vision through novel solutions to the novel problem of decoding the visual messages in advertisements and artistic photographs, and thus brings computer vision closer to its goal of being able to automatically understand visual content. From a practical standpoint, understanding visual rhetoric can be used to produce image descriptions for the visually impaired that align with how a human would label these images, and thus give them access to the rich content shown in newspapers or on TV. This project is tightly integrated with education. The work is interdisciplinary and can attract undergraduate students to the research from different fields.

    This research focuses on three media understanding tasks: (1) understanding the persuasive messages conveyed by artistic images and the strategies that those images use to convey their message; (2) exposing a photographer's bias towards their subject, e.g., determining whether a photograph portrays its subject in a positive or negative light; and (3) predicting what part of an artistic photograph a viewer might find most captivating or poignant. To enable decoding of artistic images, a large dataset is collected and annotated with a number of artistic properties and persuasion techniques that are intended for human understanding, then methods are developed to model visual symbolism in artistic images, as well as adapt positive/negative effect methods from sentiment analysis. To predict the photographer's bias towards a subject, a dataset of historical and modern portrayals of minorities and foreigners is collected, then an algorithm is created that reasons about body language and 3D layout and composition of the photo. To predict poignance, eyetracking data on a set of artistic images from famous photographers is collected, then semantic and connotation conflicts between the objects in the photographs are analyzed.

  • Valued Staff Member Russell A. Howard II Passes Away

    It is with a heavy heart that we share the news of long time staff member Russell "Russ" Howard's passing. He received his BS in Computer Science and MS in Information Science from the University of Pittsburgh. He worked for the Department of Computer Science for over 20 years as a valued and beloved member of the technical staff. He contributed to the CS Department in countless ways, and was an important part of the CS Department staff. He was well liked by all and he will be deeply missed. We will remember him always for his devotion to the Department and his sincere kindness to everyone he met. http://www.cicholski-zidekfuneralhome.com/notices/Russell-HowardII

    It is with a heavy heart that we share the news of long time staff member Russell "Russ" Howard's passing. He received his BS in Computer Science and MS in Information Science from the University of Pittsburgh. He worked for the Department of Computer Science for over 20 years as a valued and beloved member of the technical staff. He contributed to the CS Department in countless ways, and was an important part of the CS Department staff. He was well liked by all and he will be deeply missed. We will remember him always for his devotion to the Department and his sincere kindness to everyone he met. http://www.cicholski-zidekfuneralhome.com/notices/Russell-HowardII