Affiliated Centers + Institutes
- Linguistic Data Consortium (LDC)
Mark Liberman, Director
The Linguistic Data Consortium supports language-related education, research and technology development by creating and sharing linguistic resources, such as data, tools and standards.
- General Robotics, Automation, Sensing and Perception (GRASP) Lab
Mark Yim, Director; Ani Hsieh, Deputy Director
The General Robotics, Automation, Sensing and Perception (GRASP) Lab is a truly inter-disciplinary research center at the University of Pennsylvania. Founded in 1979, the lab has grown today to be one of the premier research centers focusing on fundamental research in robotics, vision, perception, control, automation and learning.
- Penn Institute for Computational Science (PICS)
Aleksandra Vojvodic, Director
The formation of the Penn Institute for Computational Science derives from the observation that computing is an important tool for research in essentially every field of study today; from the physical and biological sciences to every part of engineering to linguistics to medicine to psychology. Computing’s rapidly changing landscape, while presenting immense opportunities, makes it a challenge for researchers to stay at the state-of-the-art. PICS is formed to address these challenges and opportunities across the University of Pennsylvania.
- Penn Research in Embedded Computing and Integrated Systems Engineering (PRECISE)
Insup Lee, Director
PRECISE is the point of convergence for several related research efforts by the affiliated faculty in the areas of cyber-physical systems, distributed, real-time, and embedded systems, formal specification and verification, control theory, and trust management. Comprised of researchers from the departments of Computer and Information Science and Electrical and Systems Engineering, the center also collaborates closely with researchers in Robotics, Bioengineering, the School of Medicine, and Wharton. PRECISE research is being applied to several application domains, including embedded software-intensive medical devices, embedded software design and verification, wireless sensors, and robotics.
- Penn Research in Machine Learning (PRiML)
Shivani Agarwal and Alexander Rakhlin, co-Directors
The need to analyze and make effective use of the vast amounts of data has made Machine Learning indispensable in many fields of science, medicine and engineering, as well as technology powering modern high-tech industry. Machine Learning addresses the fundamental problems of extracting models and patterns from data. PRiML’s focus is on both theoretical and applied aspects of machine learning, especially dealing with fundamental challenges of large scale learning: high dimensionality, very large datasets, limited supervision, adversarial settings, structured outcome spaces.
- The ViDi Center
Norm Badler, Director
The ViDi Center will fundamentally address the connections between visual analysis and re-synthesis problems, primarily involving 3D objects and environments that pose significant and interesting questions in the Humanities (but also in Medicine and Engineering). The ViDi Center will bring together Engineering and Humanities faculty to embark on deeply collaborative investigations to discover new Computer Graphics modeling and animation methods and apply the best and most appropriate techniques to modeling and visualization challenges presented by human artistic, structural, and cultural artifacts.
- The Warren Center for Network & Data Sciences
Michael Kearns, Director; Rakesh Vohra, co-Director
The Warren Center for Data & Network Sciences is an incubator of forward-thinking research, culturally impactful innovation, and potent interdisciplinary collaboration that challenges how the world views technology. High-caliber faculty members from across the university have a place to centralize, share and develop their wide range of approaches to network science, from economics to sociology to cryptography and more. Here, the brightest data-centric minds at Penn spearhead collaborative research projects, generating collective solutions from once disparate schools of thought.