Affiliated Centers & Institutes
For complete descriptions of our affiliated Engineering centers and institutes, see the summaries below.
Jump to:
HMS | GRASP | IRCS | LRSM | NBIC | Pennergy | PRECISE | PRiML
Center for Human Modeling and Simulation (HMS) |
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Norman I. Badler, Director The Center for Human Modeling and Simulation exists to investigate computer graphics modeling and animation techniques for embodied agents, virtual humans, and their applications. Major foci involve developing behavior-based animation of human movement, especially for gesture, gait, and facial expression, constructing a parameterized action representation for real-time simulation and animation, and understanding the relationship between human movement, natural language, and communication. Learn more. |
General Robotics, Automation, Sensing and Perception (GRASP) Lab |
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Dan Lee, 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. Learn more. |
Institute for Research in Cognitive Science (IRCS) |
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John Trueswell, Director The Institute for Research in Cognitive Science fosters the development of a science of the human mind through the interaction of investigators from the disclipines of Linguistics, Mathematical Logic, Philosophy, Psychology, Computer Science, and Neuroscience. Learn more. |
Laboratory for Research on the Structure of Matter (LRSM) |
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Arjun G. Yodh, Director The Laboratory for Research on the Structure of Matter (LRSM) is the intellectual focal point of materials research at Penn. It hosts the Materials Research Science & Engineering Center (MRSEC), which consists of five Interdisciplinary Research Groups (IRGs) plus selected seed projects. The MRSEC provides crucial support for faculty, postdoctoral fellows, and graduate students drawn from different disciplines, to tackle complex materials science projects that can only be addressed in a truly collaborative mode. Learn more. |
Nano/Bio Interface Center (NBIC) |
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Dawn A. Bonnell, Director The Nano/Bio Interface Center at the University of Pennsylvania is a Nanoscale Science and Engineering Center (NSEC) that exploits Penn's internationally recognized strengths in design of molecular function and quantification of individual molecules. The Center unites investigators to provide, not only new directions for the life sciences, but also for engineering in a two-way flow essential to fully realizing the benefits of nano-biotechnology...Learn more. |
Penn Center for Energy Innovation (Pennergy) |
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Cherie Kagan and Andrew Rappe, Co-Directors |
Penn Research in Embedded Computing and Integrated Systems Engineering (PRECISE) |
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Insup Lee, Director PRECISE is the point of convergence for several related research efforts by theaffiliated 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. Learn more. |
Penn Research in Machine Learning (PRiML) |
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Ben Taskar and Sasha 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.Learn more. |