School of Information Sciences

SIS Colloquium Series

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Thursday, July 7, 2016

1:00 p.m. - 2 p.m.
IS Building, Room 501

Sharon Hsiao, Assistant Professor, Arizona State University

"Closing the Loop on Personalized Programming Learning in Blended Instruction Classrooms"

Abstract: This talk will address and showcase a series of education technologies that support programming learning. My team (CSI: Computing Systems & Informatics) works on designing technology to support personalized learning in bridging the gap between cyber and physical computing classes. We have been collecting and integrating formal and informal learning data to capture realistic programming learning activities, such as infomation seeking, problem solving, note taking, formal assessments, etc.

Our goal is to provide practical soluations to assist the majority of classes, which are blended instruction classes (face-to-face instruction in the classrooms supported by online tools--i.e. ITS, self-assessment quizzes, course management systems, etc. In this talk, I'll specifically focus on presenting the most recent design and findings of Programming Grading Assistant (PGA) & Web-based Programming Grading Assistant (WPGA) that support grading paper-based exams and provides personalized feedback via visual analytics.

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Friday, July 8, 2016

11:00 a.m. - Noon
IS Building, Room 501

Coffee and refreshments will be served in room 522 at 10:00 a.m.

Mai Abdelhakim, Postdoctoral Research Scientist, OSRAM Sylvania Research Center

"Towards Secure and Efficient Wireless Networks"

Abstract: Cyber-physical systems and Internet of things (IoT) are transforming some of our most critical infrastructures such as smart energy networks, intelligent transportation, advanced manufacturing, healthcare systems, and others. However, the interconnectivity and flexibility, which underlie the tremendous advantages of IoT-enabled systems, also make such systems vulnerable to various threats. This talk will focus on wireless sensor networks technology, which is a key component of a wide range of military and civilian applications and a vital element in cyber-physical systems. I will present a reliable distributed detection approach under Byzantine attacks, where some authenticated sensors are compromised to report fictitious information. The theoretical analysis reveals that the accuracy of the decision making process improves as the nodes’ density increases even if the percentage of malicious sensors remains fixed. This insight could be directly incorporated in information processing for reliable target detection in critical applications, such as reconnaissance and surveillance. I will also present a unified framework for analyzing the architectures of wireless networks that encompasses quantitative metrics for detecting suspicious behavior, which enables reliable network design. Based on these principles, a mobile access coordinated wireless sensor network architecture is developed for robust and time-sensitive information exchange. Our results shed light on the trade-off between reliability and efficiency, which should be tailored in the design of network architectures, transmission protocols and information processing schemes in future networks.

Bio: Dr. Mai Abdelhakim is a postdoctoral research scientist at the OSRAM Sylvania Research Center, working on IoT technology, security, and applications. Before that, she was a postdoctoral research associate at Michigan State University, where she worked on reliable and efficient communications in wireless networks. She received her PhD in Electrical Engineering from Michigan State University in 2014, and her Bachelor’s and Master’s degrees in Electronics and Communications Engineering from Cairo University. She joined SySDSoft Inc. (currently Intel Mobile Communications) in 2006 as an embedded software engineer. She has also worked as a teaching assistant at the German University in Cairo, and in the engineering research department at the Egyptian National Center for Radiation Research and Technology. Dr. Abdelhakim serves as a symposium chair for the International Conference on Computing, Networking and Communication (ICNC’17). She has also served as a technical program committee (TPC) member for international conferences, and as a reviewer for several international journals and conferences. Her research interests include: wireless communication, network security, and signal processing.

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Tuesday, May 3, 2016

Xiaozhong Liu

2:30 p.m. - 3:30 p.m.
IS Building, 3rd Floor Quiet Study Room

Bambang Parmanto, Professor of Health Information Management, University of Pittsburgh

"Informatics in Health and Rehabilitation"

Abstract: Instead of presenting an in-depth single-study research, I will be providing an overview of the research portfolio in my lab and the Rehab Engineering Research Center on ICT Access that I lead. The goal is to explore potential collaborations with faculty and students at the School of Information Sciences that can be carried out immediately and potential future collaborative research. One model for collaboration that I envision is that I will be providing a context (breadth) where you can apply your specific area of research in computing or informatics (depth). The themes of my research have been on how to use connectivity and computation (such as mobile devices) to develop a new clinical intervention (as a sole or as adjuvant intervention), and on how to mitigate accessibility issues of the technologies for individuals with a range of impairments (both permanent and situational). Technology-based clinical interventions are more scalable to reach wider population, deliver better health outcomes while reducing cost, and potentially improve the lives of individuals with disabilities. I will touch briefly on recurring topics in these projects that are relevant to informatics and potential intersection for collaboration, including: connected health, human factors and usability, social computing (e.g. gamification), webometrics, privacy & security, large data set, and potential for context-based adaptive intervention using machine learning.

Bambang Parmanto is Professor of Health Information Management at the University of Pittsburgh. He is the Director of Rehab Engineering Research Center (RERC) on Information & Communication Technology (ICT) Access and Principal Investigator of a large project on mobile health (mHealth) system for self-management for individuals with chronic and complex conditions. He leads the Health and Rehabilitation Informatics (HARI) research group at the University of Pittsburgh. His research interest has been in developing technologies and in using connected health and computing (such as mobile and wearable technologies) to deliver adaptive and personalized interventions for individuals with chron.

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Friday, April 8, 2016

Xiaozhong Liu3:00 p.m. - 4:00 p.m.
IS Building, 3rd Floor Quiet Study Room

Vladimir Zadorozhny, Associate Professor, School of Information Sciences, University of Pittsburgh

"Information Fusion at Large and at Scale"

Abstract: Information fusion deals with reconstructing objects from multiple, possibly incomplete and inconsistent observations. The task of scalable information fusion is critical for interdisciplinary research where a comprehensive picture of the subject requires large amounts of data from disparate data sources. Despite its increasing availability, making sense of such data is not trivial. In this talk I will elaborate on challenges in developing an infrastructure that facilitates scalable information integration and fusion. I will introduce an efficient framework that enables systematic information fusion in the context of remotely related application domains and disciplines. In particular, I will consider how concepts of information fusion and crowdsourcing complement each other and accelerate novel research directions in scalable information sensemaking. I will explore each of those concepts and their synergy under prominent scenarios of large-scale historical data integration and situation assessment in multi-robot search and rescue.

Dr. Zadorozhny is an Associate Professor of Information Science and Technology in the School of Information Sciences at Pitt. His research interests include Networked information systems, complex adaptive systems, heterogeneous data fusion, wireless and sensor data management, query optimization in distributed environments, scalable architectures for wide-area environments with heterogeneous information servers.

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Monday, March 28, 2016

Xiaozhong Liu10:00 a.m. - 11:30 a.m.
IS Building, 3rd Floor

Xiaozhong Liu, Assistant Professor of Information Science, Indiana University Bloomington

"Scientific Information Understanding"

Abstract: STEM publications, for various reasons, generally do not place a premium on writing for readability, and young scholars/students struggle to understand the scholarly literature available to them. Unfortunately, few efforts have been made to help graduate students and other junior scholars understand and consume the essence of those scientific readings. This talk is based on the hypothesis and pilot-evidence that accessing multi-modal Open Educational Resources (OER) about a scholarly publication, including presentation videos, slides, tutorial, algorithm source code, or Wikipedia pages, in a collaborative framework will significantly enhance a student’s ability to understand the paper itself. To achieve this goal, I propose a novel learning/reading environment, OER-based Collaborative PDF Reader (OCPR), that incorporates innovative text plus heterogeneous graph mining algorithms that can: 1) auto-characterize a student’s emerging information need while he/she reading a paper; 2) personalize or communitize students’ information needs based on the computational user profiles, and 3) enable students to readily access OERs based on their information need and implicit/explicit feedback. Based on the information need and various kinds of user feedback, the proposed algorithms will generate and select novel ranking features on the heterogeneous graph at a low cost for semi-supervised random walk and OER recommendation. Experiment shows that the proposed system can effectively help graduate students and scholars better understand the complex publications in both cold start and context-rich environments.

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Colloquia

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