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Keynote Speakers

Patrick Doherty. STeDy 2012

Prof. Gautam Biswas
Vanderbilt University, US

Christian-Freksa. STeDy 2012

Prof. Nancy Nersessian
Georgia Institute of Technology, US

Gautam Biswas

Prof. Gautam Biswas, Vanderbilt University, US
Case Studies in Qualitative Modeling and Reasoning

The field of Qualitative Reasoning (QR) has come a long way since the first workshop was held in 1986. Among other applications QR methods have been extensively used in K-12 STEM education, cognitive modeling, as well as engineering applications in diagnosis, design, and verification. In this talk, I will focus on case studies of the applications of QR that are directly and indirectly related to my research in (1) developing computer-based learning environments to help K-12 students gain a deep understanding of science phenomena, and (2) developing efficient methods for diagnosis of dynamic systems. In addition, I will also discuss the role of QR and related methods for verification of vehicle designs in the Adaptive Vehicle Make (AVM) project that I participate in. I will use the case studies to illustrate the important and essential role QR techniques play in these applications, but also bring up some limitations that may be addressed by aligning QR methods with more quantitative analyses.
About the Speaker
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Gautam Biswas is a Professor of Computer Science, Computer Engineering, and Engineering Management in the EECS Department and a Senior Research Scientist at the Institute for Software Integrated Systems (ISIS) at Vanderbilt University. He has an undergraduate degree in Electrical Engineering from the Indian Institute of Technology (IIT) in Mumbai, India, and M.S. and Ph.D. degrees in Computer Science from Michigan State University in E. Lansing, MI.

Prof. Biswas conducts research in Intelligent Systems with primary interests in hybrid modeling, simulation, and analysis of complex embedded systems, and their applications to diagnosis, prognosis, and fault-adaptive control. More recently, he is working on data mining for diagnosis, and developing methods that combine model-based and data-driven approaches for diagnostic and prognostic reasoning. This work, in conjunction with Honeywell Technical Center and NASA Ames, includes developing sophisticated data mining algorithms for extracting causal relations amongst variables and parameters in a system. For this work, he recently received the NASA 2011 Aeronautics Research Mission Directorate Technology and Innovation Group Award for Vehicle Level Reasoning System and Data Mining methods to improve aircraft diagnostic and prognostic systems. In other research projects, he is involved in developing simulation-based environments for learning and instruction. The most notable project in this area is the Teachable Agents project, where students learn science by building causal models of natural processes. More recently, he has exploited the synergy between computational thinking ideas and STEM learning to develop systems that help students learn science and math concepts by building simulation models. His research has been supported by funding from NASA, NSF, DARPA, and the US Department of Education. He has published extensively, and has over 400 refereed publications.

Nancy Nersessian

Prof. Nancy Nersessian, Georgia Institute of Technology., Atlanta, Georgia, US
Engineering Analogies: Model-Based Reasoning in Bioengineering Sciences

Research on analogy in the cognitive sciences has largely been based on cases in which the analogical source is ready-to-hand. In my research on creative analogies in historical scientific discoveries, I argued that in many instances the source analogy itself needs to be constructed in an iterative process of building conceptual models that incorporate both target and source domain constraints. Now I have been extending that research to examine the processes of building physical and computational models. In the bioengineering sciences whole fields are based on the practice of “engineering” in vitro and in silico models to serve as source analogies through which to reason about biological phenomena that for reasons of experimental control, complexity, or ethics cannot be experimented on in vivo. I will examine some cases based on my 12-year ethnographic study of these practices in bioengineering research labs and discuss the implications for both a richer understanding of analogy and of how scientists build cognitive powers through building modeling environments.
About the Speaker
Prof. Nancy Nersessian
Nancy J. Nersessian is Regents’ Professor and Professor of Cognitive Science at the Georgia Institute of Technology, where she holds faculty positions in the School of Interactive Computing, the School of Public Policy, and the School of Architecture. She has been a Fellow of several research institutes, most recently Radcliffe Institute for Advanced Study at Harvard.

Her research focuses on the creative research practices of scientists and engineers, especially on how various kinds of modeling practices lead to fundamentally new ways of understanding the world. She has numerous publications on this topic, including Creating Scientific Concepts (MIT, 2008, awarded the Patrick Suppes Prize) and Science as Psychology: Sense-making and Identity in Science Practice (with L. Osbeck, K. Malone, W. Newstetter, Cambridge, 2011, awarded the William James Book Prize).

In current work, she is investigating creative problem solving in the emerging transdisciplinary field of integrative systems biology. Funded by the National Science Foundation, this research seeks to understand the dynamic interplay of cognition and culture in pioneering research laboratories, and how these laboratories foster and sustain creative and innovative practices.

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