2/02David Salt, Horticulture and Landscape Architecture, Purdue University
Mapping connections between the genome, ionome and the physical landscape.
Abstract: Understanding how organisms control their ionome or mineral nutrient and trace element composition, could have a significant impact on both plant and human health. Furthermore, associating the genetic determinants that underlie natural ionomics variation, with the landscape of the individuals that carry these genotypes, will provide insight into the genetic basis of adaptation and speciation. We have employed high-throughput mineral nutrient and trace element profiling, using inductively coupled plasma – mass spectrometry (ICP-MS), as a tool to determine the biological significance of connections between an organisms genome and its ionome. Our focus is on genes that control uptake and accumulation of mineral elements, including Ca, K, Mg, P (macronutrients in plant fertilizer), Co, Cu, Fe, Li, Mn, Mo, Ni, Se, Zn, (micronutrients of significance to plant and human health) and As, Cd, Na and Pb (elements causing agricultural or environmental problems). To date we have analyzed the ionome of over 100,000 Arabidopsis plants and 20,000 yeast samples. This includes several Arabidopsis forward genetic screens (Lahner et al., 2003 Nat. Biotechnol. 21:1215), a screen of 360 natural Arabidopsis accession, and a complete analysis of all 5153 strains of the yeast deletion collection (Danku et al., 2009 JAAS (in press)). We have successfully used PCR-based positional cloning, DNA microarray based approaches, QTL and association mapping to identify numerous genes that control the ionome (for example Rus et al., 2006 PLoS Genetics 2(12): e210; Baxter et al., 2008 PLoS Genetics 4(2):e1000004). Association of variation in these genes with the landscape in which these plants naturally grow is starting to reveal the genetic architecture underlying specific adaptations to the environment. We are also finding that specific ionomic “fingerprints” are associated with functionally related sets of genes, and also with the physiological status of the organism (Baxter et al., 2008 PNAS 105: 12081-12086). To maximize the value of this ionomics approach, we have developed a publicly searchable online database containing ionomic information on over 1000,000 samples from over 1500 different experiments (www.ionomicshub.org; Baxter et al., 2007 Plant Physiol 143: 600-611), and the database is being updated regularly.
2/09 Frank H. Page Jr., Economics, Indiana University, Bloomington
Endogenous Network Dynamics
Abstract: In all social and economic interactions, individuals or coalitions choose not only with whom to interact but how to interact, and over time both the structure (the ``with whom'') and the strategy (``the how'') of interactions change. Our objectives here are to model the structure and strategy of interactions prevailing at any point in time as a directed network and to address the following open question in the theory of social and economic network formation: given the rules of network and coalition formation, the preferences of individuals over networks, the strategic behavior of coalitions in forming networks, and the trembles of nature, what network and coalitional dynamics are likely to emergence and persist. Our main contributions are (i) to formulate the problem of network and coalition formation as a dynamic, stochastic game, (ii) to show that this game possesses a stationary correlated equilibrium (in network and coalition formation strategies), (iii) to show that, together with the trembles of nature, this stationary correlated equilibrium determines an equilibrium Markov process of network and coalition formation which respects the rules of network and coalition formation and the preferences of individuals, and (iv) to show that, although uncountably many networks may form, this endogenous process of network and coalition formation possesses a nonempty finite set of ergodic measures and generates a finite, disjoint collection of nonempty subsets of networks and coalitions, each constituting a basin of attraction.
Bio: Professor Page’s current research interests lie in two areas: (1) strategic network formation and (2) competitive nonlinear pricing games. In the area of strategic network formation, his current work focuses on the emergence of stochastic network dynamics from strategic behavior and stochastic elements in nature. In the area of competitive nonlinear pricing games, his current work focuses on the Nash existence problem in such games. Professor Page has published in Econometrica, the Journal of Economic Theory, Economic Theory, the Journal of Mathematical Economics, the International Journal of Game Theory, the Journal of Financial and Quantitative Analysis, the Journal of Public Economic Theory, the Annals of Finance, the Journal of Economic Behavior and Organization, Canadian Mathematical Bulletin, Journal of Global Optimization, Optimization, Review of Economic Design, Social Choice and Welfare, Mathematical Social Sciences, and Economic Letters. Professor Page is an Associate Editor of the Journal of Public Economic Theory, the Annals of Finance, and Economics Bulletin. He is regularly Visiting Professor at the University of Paris 1 (Pantheon-Sorbonne) and he has twice (1996 and 2006) been the organizer of the NSF/NBER Decentralization Conference. He is Vice President of the Association for Public Economic Theory.
2/23 Ann McCranie, Indiana University, Bloomington
Co-Authorship Networks in the Mental Illness Recovery Research Movement
Abstract: The field of mental health services research incorporates researchers and practitioners from the fields such as psychology, psychiatry, social work, health policy, and consumer advocacy. This diverse field saw a marked increase in the publication of recovery-oriented literature and the development of recovery-oriented clinical and organizational practices starting in the mid 1980s and continuing to the present. In services research, the meaning of recovery in severe mental illness (SMI) is contested, but refers broadly to the idea that the long-term prospects of people with SMI need not be dire and illness-defined. Instead, the concept of recovery suggests the care of SMI should be person- and future-oriented and should allow individuals to work toward personally meaningful goals. While this may not seem to outsiders as much of a challenge to service providers, in the 1980s and beyond, recovery became a rallying cry for those who sought to contest overly custodial and pessimistic providers and systems of care. The recovery "movement" in the research literature appears to be what Frickel and Gross termed a scientific/intellectual movement (2005). This study expands the network argument of the SIM framework and examines the evolution of the recovery research network through co-authorship and past and present academic, clinical, disciplinary and other professional affiliations. Multiple types of approaches, including centrality, evolutionary models, and p* models are used to explore this scientific movement.
Bio: McCranie is a PhD candidate in the Department of Sociology at Indiana University Bloomington. Her research interests are in organizational studies, network analysis, and medical sociology. Her dissertation will be about institutional change in mental health care services in the United States, in particular the introduction of "recovery" based services for people with serious mental illness.
3/02 George Kampis, Department of History and
Philosophy of Science at Eötvös University in Budapest and Director of
the Budapest Semester in Cognitive Science
Food Webs From RNA Structures: The Emergence and Analysis of Complex Ecological Networks
Abstract: Understanding ecosystems is one of the most important challenges for theoretical biology and Artificial Life. We offer a bottom-up, fully individual-based model where phenotype-to-phenotype interactions of organisms define ecological networks and we study how simple conditions give rise to complex food webs if we allow for the evolution of phenotypes and hence phenotype interactions. A key element of the model is the notion of "rich phenotype" realized as a set of nonlinear tradeoffs in a multi-trait system. To approach this, we have chosen one of the best understood phenotypes, RNA structures, and assigned ecological functions to their features. In a series of experiments we show the emergence of complex food webs with generic properties, which indicates that minimalist assumptions such as having rich phenotype interactions might be sufficient to generate complex ecosytems and to explain some puzzling ecological features.
Bio: Founding chairman and Professor, since 1994, of the department of History and Philosophy of Science at Eötvös University in Budapest (http://hps.elte.hu). He holds a PhD and a Habilitation in Biology as well as a D.Sc. in Philosophy of Science. Main research interests in Artificial Life, cognitive science (Director of the Budapest Semester in Cognitive Science, http://hps.elte.hu/BSCS), complex systems and evolutionary modeling (www.evotech.hu), especially using agent based systems. He was guest professor at Hokkaido University (in 2001 and 2004), in 2002/3 he was Fujitsu Chair of Complex Systems at JAIST (Japan Advanced Institute for Science and Technology), and Wayne G. Basler Chair of Excellence at East Tennessee State University in 2007. Dr Kampis has of over 100 scientific publications, he is the author or editor of several books (with Pergamon, Kluwer, Springer etc.). He mastered several translations to Hungarian, among other things, The Origin of Species, and Darwin’s Dangerous Idea. His most recent research monograph “Feedback Self-Organization” will be published by Springer in 2010 (together with computer scientist Dr Laszlo Gulyas, http://user.aitia.ai/~gulyas_laszlo/). He is currently a fellow of Collegium Budapest (www.colbud.hu), member of the EC FP6 project QosCosGrid (www.coscosgrid.eu), project leader of the TexTrend project (www.textrend.hu), leader of the Comparative Mind Database module of the ESF Network CompCog (www.compcog.org) ,and Hungarian group leader of the upcoming FP7 project DynaNets. He is co-organizer, together with Professor Eörs Szathmary, of the upcoming European Conference on Artificial Life (ECAL2009, www.ecal2009.org). In the Spring of 2009, he is a Fulbright Scholar at Indiana University, Bloomington, at Cognitive Science/Complex Systems.
3/09 Chen Yu, Psychological & Brain Sciences, Indiana University, Bloomington
Visual Data Mining of Multimedia Data for Social and Behavioral Studies
Abstract:With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, etc.) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge since most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) A smooth interface between visualization and data mining; (2) A flexible tool to explore and query temporal data derived from raw multimedia data; and (3) A seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables and as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare, and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.
Bio: Chen Yu received a Ph.D. in computer science from University of Rochester in 2004 before he joined IUB. Currently he is an assistant professor in Department of Psychological and Brain Sciences, Cognitive Science Program and Department of Computer Science. His main research area is human cognition and learning using both computational and empirical approaches, with emphases on understanding both how language learning depends on coupled multimodal dynamics among brain, body and environment, and the underlying learning mechanisms that support statistical multimodal learning. His research is supported by NIH, NSF and NIJ. His lab’s website at http://www.indiana.edu/~dll/.
Micah Linnemeier & the NWB Team, Cyberinfrastructure for Network Science Center, Indiana University, Bloomington
Network Workbench: Current and future development at the Cyberinfrastructure for Network Science Center
Abstract: The presentation will discuss the various projects of the Cyberinfrastructure for Network Science Center, demonstrating recent developments in the Network Workbench tool, as well as describing some of our future work: the EpiC and SciPolicy cyberinfrastructure projects.
Network Workbench helps network scientists process, analyze, and visualize network data. It is highly-extensible, allowing users to contribute their own algorithms to the tool, which are able to interact seemlessly with existing Network Workbench functionality. This flexibility is made possible by the CIShell cyberinfrastructure framework, which is the foundation of our upcoming cyberinfrastructure tools as well. New functionality in Network Workbench includes a collection of algorithms for handling weighted networks, and support for scientometrics analysis and processing.
The upcoming EpiC (short for Epidemics Cyberinfrastructure) project aims to create a tool to aid in the modeling, analysis, and visualization of epidemics data. The EpiC project is also developing a community website to facilitate the sharing of datasets in the epidemics community.
SciPolicy will expand the scientometrics functionality in Network Workbench into a separate full-fledged tool, making it easy for science policy makers to visualize and understand large sets of scientometrics data.
Bio: Micah Linnemeier is a senior systems architect and project manager at the Cyberinfrastructure for Network Science Center founded by Dr. Katy Börner. Linnemeier graduated from Indiana University with a B.S. in Computer Science in December 2007. His current projects include Network Workbench (nwb.slis.indiana.edu), SciPolicy, EpiC (epic.slis.indiana.edu), and Cyberinfrastructure Shell (www.cishell.org). His research interests comprise Software Engineering and Cyberinfrastructure development.
3/30Aaron Koblin, Design Technology Lead at the Creative Lab, Google Inc., San Francisco, California
Data + Art
Abstract: Human senses are finely tuned to understand and evaluate
visible and audible objects in a dynamic and interactive way. My talk
will discuss a number of experiments and artworks that employ expressive
interfaces to investigate data in compelling ways. The work will explore
the concept of narrative through data visualization and the use of data
as a medium for art. Specific works will range from more traditional
visualizations of air traffic, cellphone networks, and wired telephony
systems, to more abstract multi-media content collected from workers
online, and laser range finders used for both understanding ecology, and
making music videos.
Bio: Aaron Koblin is an Artist/Designer/Researcher focused on creating
and visualizing human systems. Currently part of Google's Creative Lab
in San Francisco, California, Aaron creates software and architectures
to transform social and infrastructural data into rich digital
expression. Koblin's work has been shown internationally and is part of
the permanent collections at the Museum of Modern Art (MoMA) in New York.
4/06Olaf Sporns, Department of Psychological and Brain Sciences, Indiana University, Bloomington
Complex Brain Networks
Abstract: The human brain is a complex network. My talk will be about emerging links between the connectivity structure of the brain and its functional dynamics, and about how we might construct a computational network model of the brain. We now know that structural brain networks exhibit a number of topological features, including small-world attributes, modularity, and hubs. How do these structural features relate to functional characteristics of brain networks, to their dynamic patterns, to their processing power, robustness, or capacity to support flexible behavior? I will review recent work on complex brain networks that aims to identify how brain networks are organized and how they process and integrate information. I will also outline how these efforts may inform the design of a comprehensive structural and dynamic model of the human brain.
Reference: Bullmore, E.T, Sporns, O. (2009) Nature Reviews Neuroscience 10, 1-13.
Bio: Olaf Sporns received a Ph.D. in neuroscience from Rockefeller University (New York) and conducted postdoctoral work at The Neurosciences Institute in New York and San Diego. Currently he is Professor and Associate Chair in the Department of Psychological and Brain Sciences at Indiana University in Bloomington. His main research area is theoretical and computational neuroscience, with an emphasis on network complexity, brain connectivity, and neurorobotics. He serves on the editorial boards of several journals, including PLoS ONE, PLoS Computational Biology, and Neuroinformatics.
4/13Jaideep Srivastava, SC&E, University of Minnesota
Web Mining - Accomplishments and Future Directions
Abstract: From its very beginning, the potential of extracting
valuable knowledge from the Web has been quite evident. Web mining -
i.e. the application of data mining techniques to extract knowledge
from Web content, structure, and usage - is the collection of
technologies to fulfill this potential. Interest in Web mining has
grown rapidly in its short existence, both in the research and
practitioner communities. This talk provides an overview of the
accomplishments of the field - both in terms of technologies and
applications - and outlines key future research directions.
Bio: Jaideep Srivastava is a
professor at the University of Minnesota, where he has established and
led a research laboratory which conducts research in the information
and knowledge aspects of computing. He has supervised 26 Ph.D.
dissertations and 53 M.S. theses, and authored or co-authored over 220
papers in refereed journals and conferences. Dr. Srivastava has served
on the editorial boards of various journals, including IEEE TPDS, IEEE
TKDE, and the VLDB journal. He has also served as Program and
Conference Chair for a number of prominent conferences, especially in
the area of data mining, and is on the Steering Committee for the
PAKDD series of conferences. He has delivered a number of keynote
addresses, plenary talks, and invited tutorials at major conferences.
Dr. Srivastava has a very active interaction with the industry, in
both consulting and executive roles. Specifically, during a 2-year
sabbatical during 1999-2001, he lead a corporate data mining team at
Amazon.com (www.amazon.com) and built a data analytics department at
Yodlee (www.yodlee.com) from the ground up. More recently, he spent
two years as the Chief Technology Officer for Persistent Systems
(http://en.wikipedia.org/wiki/Persistent_Systems), where he built an
R&D division and oversaw the redesign of the training and technical
vitalization program for 2,200+ engineers. He has provided technology
and technology strategy advice to a number of large corporations
including Cargill, United Technologies, IBM, Honeywell, 3M, and Eaton.
He has served in an advisory capacity to a number of small companies,
including Lancet Software and Infobionics.
Dr. Srivastava has also played an active advisory role in the
government sector. Specifically, he has served as the US federal
government's expert witness in a nationally significant tax case. He
is presently serving as Senior Technology Advisor to the State of
Minnesota, and is on the Technology Advisory Council to the Chief
Minister of Maharashtra, India. He is a Fellow of the IEEE, and has
been an IEEE Distinguished Visitor.
4/20Luis Rocha and Manuel Marque-Pita, Indiana University, Bloomington
Emergent Computation In Complex Network Dynamics
Abstract: Complex systems approaches to biological modeling often aim at the discovery of the "laws" of Biology. This endeavor entails a trade-off between generality and predictability. I argue that complex systems approaches, on their search for universal principles, have erred too much on the side of generality with very few examples of successful modeling of actual biological systems. In particular, we focus on the study of emergent computation in networks of automata. While there have been advances toward understanding the structure of natural networks, as well as some modeling of specific biological systems as networks of automata, it is still largely an open question how the dynamics of complex networks can lead to emergent, collective computation and how to control or "program" it to perform specific tasks. We discuss a new methodology based on Holland's schemata, for characterizing the dynamics of large automata networks, such as cellular automata and Boolean networks. We focus on examples from the systems biology literature, such as the segment polarity network of the Drosophila Melanogaster (21 nodes), and a large biochemical intracellular signal transduction network (139 nodes). We discuss how our approach is useful to characterize regulation, control, robustness, modularity and collective computation in networks of automata.
Bio: Luis M. Rocha is an Associate Professor and director of the Complex Systems graduate Program in Informatics, member of the Center for Complex Networks and Systems, and core faculty of the Cognitive Science Program, at the Indiana University, Bloomington, USA. He is also the director of the FLAD Computational Biology Collaboratorium and in the direction of the associated PhD program in Computational Biology at the Instituto Gulbenkian da Ciencia, Portugal. His research is on complex systems, computational biology, artificial life, embodied cognition and bio-inspired computing. He received his Ph.D in Systems Science in 1997 from the State University of New York at Binghamton. From 1998 to 2004 he was a permanent staff scientist at the Los Alamos National Laboratory; where he founded and led a Complex Systems Modeling Team during 1998-2002 and part of the Santa Fe Institute research community. He has organized major conferences in the field such as the Tenth International Conference on the Simulation and Synthesis of Living Systems (Alife X) and the Ninth European Conference on Artificial Life (ECAL 2007). He has published many articles in scientific and technology journals, and has been the recipient of several scholarships and awards. Details about his research and teaching are available on his web site: http://informatics.indiana.edu/rocha.
Alessandro Flammini, School of Informatics, Indiana University, Bloomington
Optimal Transportation Networks
Abstract: Our current understanding of networks structure and evolution is largely based on the description of the dynamical processes that have shaped them. Alternative approaches based on principles of optimality have been proposed, but certainly are not mainstream. Although there are good reasons for that, I will discuss examples where such approaches are fruitful, focusing especially on the case of road networks.
Bio: Alessandro Flammini did his graduate studies at the Institute for Advanced Studies of Trieste, Italy, where he received a PhD in Condensed Matter Physics. After that, he spent 3 years as a postdoc, first at MIT, and then at the University of Cambridge, UK. He held senior research positions at ISAS Trieste and at the University of Lausanne Switzerland.
He joined the School of Informatics at IU in August 2004. He is currently a member of the Center for Complex Networks and Systems Research.
His research interests are generally in the area of complex networks, and in information and transportation networks in particular.
David Lazer, Harvard University
Life in the network: The coming age of computational social science
Abstract: An increasing fraction of human interactions are digitally captured.
These digital breadcrumbs, combined with substantial computational
power, create enormous opportunities for ground breaking science. This
talk will discuss what some of the potential opportunities are for
developing an improved understanding of collective human behavior, as
well the potential barriers to the emergence of a "computational
social science." In particular, the objective of this talk will be to
spur discussion regarding how to bridge the gap between various
methods for data mining and enhancing understanding of human behavior.
Bio: David Lazer is Director of the Program on Networked Governance at Harvard University, and Associate Professor of Public Policy. His research focuses on issues around the collective governance issues that arise when we think of network as a public good. He has examined this issue in many domains, from examining how agencies work together, to how interest groups are linked, to how teams work together. His research has been published in the top general scientific journals, as well as top journals in political science and organizational theory.
Potential Speakers for Fall 09:
June 2009: Andrea Scharnhorst: Walking through landscapes in construction - the use of dynamic models in information science.
Sept 21, Arnim Wiek,
School of Sustainability,
Arizona State University
Sept 28, 2009: Allen Carroll is chief cartographer and executive vice president of National Geographic Maps.
J. Scott Long, Sociology, IUB
William Pridemore in Crinimal Justice, IUB (alcohol/violence epidemiology)
Tony Grubesic, Geography, IUB (epidemics, transportation networks)
David Kidd, NESCent
Dirk Helbing, ETHZ, CH
Qunfeng Dong, Center for Genomics and Bioinformatics, IU: Developing a web-based bioinformatics workflow for the METYCyt System Microbiology Project
Francisco Veloso, CMU - Social Capital and the Creation of Knowledge
Erik Schultes, Duke U.
Richard Bonneau, New York University
James H. Fowler, Department of Political Science, University of California, San Diego
Kathleen Carley, CASOS, CMU
Dan Stokols, University of North Carolina
Jonathon Cummings, Northwestern U
Robert Ackland, Fellow, Australian Demographic and Social Research Institute, College of Arts and Social Sciences, The Australian National University
Herbert Van de Sompel, LANL
Eytan Adar, University of Washington
Francis Narin, CHI Research, Inc.
Valdis Krebs, InFlow, Cleveland. Networks in Economic Development.
David François Huynh, MIT, http://people.csail.mit.edu/dfhuynh/projects.html
Bill Tash, Evaluating Research Centers and Institutes for Success! A Manual and Guide with Case Studies. Based on a NSF Funded National Study of 300 Research Centers and other Recent Data
March 3-5, James Hendler, Rensselaer Polytechnic Institute, Mon talk on Thur March 4, 4p.
April 19, Una O. Osili, Associate Professor of Economics, Interim Director of Research The Center on Philanthropy at Indiana University, Indianapolis, IN.