SCI hosts a number of organizations for students that will provide you with opportunities to network, allow you to get to know your fellow students, and help you to boost your leadership/professional skills.
Advanced Data Management Technologies Lab
The Advanced Data Management Technologies Laboratory at SCI is co-directed by Professors Panos K. Chrysanthis and Alexandros Labrinidis. Research projects are targeted towards network-centric data management applications (e.g., mobile data management, sensor networks, web-databases, etc) and the approach taken is user-centric: emphasis is given on Quality of Service (QoS) and Quality of Data (QoD) returned to the users, and on controlling the trade-off between QoS and QoD, in a way that is prescribed by the users.
Lead faculty: Panos Chrysanthis, Alexandros Labrinidis
Computational Social Dynamics Lab
At the Computational Social Dynamics Lab (PICSO LAB), we are working toward modeling and analyzing patterns of change within complex social systems, with a focus on patterns of change that emerge from citizen activities, interactions and relationships, and their sense-making processes. Our research mission is to use data, big and small, in the service of humanity.
Lead faculty: Yu-Ru Lin
Geoinformatics Lab
Geoinformatics encompasses a collection of special techniques, technologies, and tools for the acquisition, processing, management, analysis, and presentation of geospatial data. Learn more about the Geoinformatics Lab.
Lead faculty: Hassan Karimi
The Human-Autonomy Teaming (HAT) Lab
This lab aims to improve human performance, safety, and well-being in intelligent systems, with applications ranging from transportation, healthcare, to military domains. By applying both data-driven and theory-driven methods, the work (1) analyzes how humans interact with technologies; (2) develops computational models to simulate and predict human behaviors; and (3) proposes and evaluates design ideas to enable effective and safe collaborations between humans and intelligent agents.
Lead faculty: Na Du
Information Retrieval, Integration, and Synthesis (iRiS) Lab
iRiS lab researchers and educators are interested in, and are actively working on various sub-topics areas of IR, which include adaptive and interactive information retrieval, cross-language information retrieval, digital library, information seeking on the Web and in traditional library settings, interface and visualization, and multimedia indexing and retrieval. The goal of the iRiS Lab is to develop effective, efficient and friendly tools for people to access large, distributed, and heterogeneous information collections.
Lead faculty: Daqing He
Laboratory for Education and Research on Security Assured Information Systems (LERSAIS)
The Laboratory for Education and Research on Security Assured Information Systems (LERSAIS) provides a framework for long-term goals of (1) establishing a premier research program that focuses on the diverse problems related to security and survivable information systems, networks, and infrastructures, and (2) developing and supporting high quality education in security and information assurance. The University of Pittsburgh has been designated as a National Center of Academic Excellence in Information Assurance Education since 2004 jointly by the US National Security Agency (NSA) and the Department of Homeland Security (DHS). LERSAIS is Pitt’s representative CAE, and it has also been designated as CAE-Research.
Lead faculty: Amy Babay, Ahmed Ibrahim, James Joshi, Prashant Krishnamurthy, Balaji Palanisamy, David Tipper
Learning Technologies Lab (LTL)
The Learning Technologies Lab (LTL) at DINS is an interdisciplinary research and development laboratory that fosters innovative research in the area of learning technologies and transformational / serious games. The LTL offers new educational opportunities to undergraduate and graduate students, and catalyzes collaboration among University of Pittsburgh schools and departments.
Lead faculty: Peter Brusilovsky, Dmitriy Babichenko
Personalized Adaptive Web Systems (PAWS) Research Lab
The goal of the Personalized Adaptive Web Systems (PAWS) Research Lab is development and evaluation of innovative user- and group-adaptive Web-based technologies, systems, and architectures. The Lab currently explores a range of user modeling, adaptation and personalization technologies.
Lead faculty: Peter Brusilovsky
Pitt EducaTional and Language Technology Lab (PETAL)
Learn more about the Pitt EducaTional and Language Technology Lab.
Lead faculty: Diane Litman
Power-management and Real-Time Systems Lab (PORTS)
Prognostic Lab
The Prognostic Lab is an experimental systems research group at SCI. Our work focuses on the design of core systems software capable of fully utilizing next generation hardware environments while at the same time being amenable to dynamic resource managers. Our research is based primarily in the context of high performance and extreme scale computing. To increase the applicability of high performance systems, we seek to provide unmodified applications transparent access to high performance resources. Our methods are based on the design, implementation and evaluation of experimental systems.
Lead faculty: John Lange
Quantum Information and Networking Group
The QIN research group specializes in quantum information processing and networking technologies, and applications toward developing quantum information technologies and connecting them over a robust quantum internet for applications with deep societal impact.
Lead faculty: Kaushik Seshadreesan
Resilient Systems and Societies Lab
The Resilient Systems and Societies Lab is a computer systems research group with a focus on dependable infrastructure. The aim of this lab's work is to make the networked systems our society relies on resilient to failures and attacks, and to develop new network technologies that help bring people together.
Lead faculty: Amy Babay
Spatial Information Science Research Interest Group
The Spatial Information Science Research Interest Group at Pitt is dedicated to the understanding and use of spatial information across a wide variety of contexts. Spatial information science is a growing field that is concerned with computational, technical, and cognitive aspects of acquiring, storing, manipulating, using and understanding spatial information. Spatial information science incorporates developments in geographic information systems, location-based services, virtual environments, web navigation and aspects of scientific visualization.
Sustainable Social Computing
At the Sustainable Social Computing lab at the University of Pittsburgh, we research the interplay of technology and society in terms of (1) how social computing systems can be studied, designed, and implemented to more effectively achieve the goals of their different stakeholders; (2) what role these technologies play within different communities and how individuals and communities are effected by them; and (3) how social technologies are utilized by different communities and individuals, specifically with respect to how digital gap and established inequalities can contribute to inequalities on the social technology platforms.
Lead faculty: Rosta Farzan
Teaching and Learning Research Lab (TALER)
The goal of the Teaching And Learning Research Lab (TALER) at SCI is to design, develop, and evaluate tools that can support students in learning and professors in teaching Information Science courses. TALER Lab was established in 2001 with support from an Innovation in Education grant (ACIE) awarded through the Provost’s Advisory Council for Instructional Excellence.
Usability Research Lab
The Usability Research Lab was opened in the mid 1990’s to provide experimental space for Professors Korfhage and Lewis’ NSF-sponsored research in visual information retrieval interfaces as well as to function as a conventional Usability (now User Experience) lab. With an influx of new projects, the lab’s focus shifted to human-agent interaction, interactive simulation and VR, and since 2003, human-robot interaction. Current projects involve human supervision of robotic swarms, trust in automation, and reinforcement learning models for human-machine teaming.
Lead faculty: Michael Lewis