2013 Discovery Informatics Symposium

Discovery Informatics: AI Takes a Science-Centered View on Big Data

Friday – Sunday, November 15–17, 2013

AAAI Fall Symposium Series

Arlington, Virginia

Discovery Informatics: AI Takes a Science-Centered View on Big Data

Discovery Informatics focuses on intelligent systems aimed at accelerating discovery, particularly in science but also from any data-rich domain. It is a generalization of scientific informatics work (e.g., medical-, bio-, eco- or geo-informatics) that seeks to apply principles of intelligent computing and information systems in order to understand, automate, improve, and innovate any aspects of discovery processes. A range of AI research is directly relevant including process representation and workflows; intelligent interfaces; causal reasoning; machine learning; knowledge representation and engineering; semantic web; advanced visualization toolkits and social computing. The proposed symposium builds on two prior successful meetings held in 2012: an NSF workshop and a AAAI Fall Symposium.

The application of AI approaches to assist in scientific discovery is an open ended knowledge-driven challenge with a very high potential impact. Following the delineation of three important areas of interest at previous meetings: (1) social computing for discovery; (2) computational support of discovery and (3) possible new models and data, we now seek to include ‘Big Data’ approaches in our view of discovery informatics, which provides the theme of this symposium.

Schedule

Friday, November 15th

8:45-9:00am

Welcome

9:00-10:30am

Invited talk: “Socially Intelligent Science” by Haym Hirsh, Cornell University (60 min.).

Paper presentation: Anita De Waard, Jeremy Alder, Shawn Burton, Richard C. Gerkin, Mark Harviston, David Marques, Shreejoy J. Tripathy and Nathaniel N. Urban. “Creating an Urban Legend: A System for Electrophysiology Data Management and Exploration” (30 min.).

(Session chair: G. Burns)

10:30-11:00am

Coffee Break

11:00am-12:30pm

Invited talk: "Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science" by Claire Monteleoni, George Washington University (60 min.).

Paper presentation: Elizabeth Bradley, Laura Rassbach de Vesine, Kenneth Anderson, Marek Zreda and Christopher Zweck. “Forensic Reasoning about Paleoclimatology” (30 min.).

(Session chair: N. Villanueva-Rosales)

12:30-2:00pm

Lunch

2:00-3:30pm

Paper presentation:  Nicholas Del Rio, Natalia Villanueva-Rosales, Deana Pennington, Karl Benedict, Aimee Stewart and Cj Grady. “ELSEWeb meets SADI: Supporting Data-to-Model Integration for Biodiversity Forecasting” (30 min.).

Lead discussion: "What is Discovery Informatics? What is it not?" (60 min.)

(Session chair: G. Burns)

3:30-4:00pm

Coffee Break

4:00-5:30pm

Invited Panel: "Impact of Discovery Informatics" Pietro Michelucci (ThinkSplash), Kayur Patel (Google), Barbara Ramson (NSF).

(Moderator: G. Burns)

6:00-7:00pm AAAI Reception for all Fall Symposia

Saturday, November 16th

9:00-10:30am

Invited talk: "Bioinformatics computation of metabolic models from sequenced genomes" by Peter Karp, SRI International (60 min.).

Paper presentation: Rinke Hoekstra and Paul Groth. “Linkitup: Link Discovery for Research Data” (30 min.).

(Session chair: Y. Gil)

10:30-11:00am

Coffee Break

11:00am-12:30pm

Note: This session will be hold on the Fitzgerald Ballroom B. Joint session with the Semantics for Big Data Symposium.

Invited talk: “Generating Biomedical Hypotheses Using Semantic Web Technologies” by  Michel Dumontier, Stanford University (60 min.).

Round table discussion (30 min.)

(Session chair: N. Villanueva-Rosales)

12:30-2:00pm

Lunch

2:00-3:30pm

Invited talk: "Predictive Modeling of Patient State and Therapy Optimization" by Zoran Obradovic, Temple University (60 min.).

Paper presentation :Kyle Ambert, Aaron Cohen, Gully Burns, Eilis Boudreau and Kemal Sonmez.“Finna: A Paragraph Prioritization System for Biocuration in the Neurosciences” (30 min).

(Session chair: Y. Gil)

3:30-4:00pm

Coffee Break

4:00-5:30pm

Invited talk: ”Representing and Reasoning with Experimental and Quasi-Experimental Designs” by David Jensen, University of Massachusetts at Amherst (60 min.).

Paper presentation: David Kale, Samuel Di, Yan Liu and Yolanda Gil. “Capturing Data Analytics Expertise with Visualization in Workflows” (30 min.).

(Session Chair: N. Villanueva-Rosales)

6:00-7:30pm

Plenary session with overview of all the AAAI Symposia

Sunday, November 17th

9:00-10:30am

Invited talk: "Case Studies in Data-Driven Systems: Building Carbon Maps to Finding Neutrinos" by Christopher Re, Stanford University (60 min.).

Paper presentation: Leonardo Salayandia, Deana Pennington, Ann Gates and Francisco Osuna. “MetaShare: From Data Management Plans to Knowledge-Based Systems” (30 min.).

(Session chair: Y. Gil)

10:30-11:00am

Coffee Break

11:00am-12:00pm

Invited talk: “Look at this gem: Automated data prioritization for scientific discovery of exoplanets, mineral deposits, and more” by Kiri L. Wagstaff, NASA Jet Propulsion Laboratory (20 min.).

Abstract presentation: Louiqa Rashid, Guillermo Palma, Maria-Esther Vidal, Andreas Thor. “Exploration Using Signatures in Annotation Graph Datasets” (20 min.).

Abstract presentation: Ashok K. Goel. “Computational Ideation in Scientific Discovery” (20 min.).

(Session chair: N. Villanueva-Rosales)

12:00-12:30pm

Final discussion

Invited Talks


Michel Dumontier: "Generating Biomedical Hypotheses Using Semantic Web Technologies"
Abstract: With its focus on investigating the nature and basis for the sustained existence of living systems, modern biology has always been a fertile, if not challenging, domain for formal knowledge representation and automated reasoning. Over the past 15 years, hundreds of projects have developed or leveraged ontologies for entity recognition and relation extraction, semantic annotation, data integration, query answering, consistency checking, association mining and other forms of knowledge discovery. In this talk, I will discuss our efforts to build a rich foundational network of ontology-annotated linked data, discover significant biological associations across these data using a set of partially overlapping ontologies, and identify new avenues for drug discovery by applying measures of semantic similarity over phenotypic descriptions. As the portfolio of Semantic Web technologies continue to mature in terms of functionality, scalability and an understanding of how to maximize their value, increasing numbers of biomedical researchers will be strategically poised to pursue increasingly sophisticated KR projects aimed at improving our overall understanding of the capability and behavior of biological systems.

Biography: Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of computational methods to increase our understanding of how living systems respond to chemical agents. At the core of the research program is the development and use of Semantic Web technologies to formally represent and reason about data and services so as (1) to facilitate the publishing, sharing and discovery of scientific knowledge, (2) to enable the formulation and evaluation scientific hypotheses and (3) to create and make available computational methods to investigate the structure, function and behavior of living systems. Dr. Dumontier serves as a co-chair for the World Wide Web Consortium Semantic Web in Health Care and Life Sciences Interest Group (W3C HCLSIG) and is the Scientific Director for Bio2RDF, a widely used open-source project to create and provide linked data for life sciences.


Haym Hirsh: Socially Intelligent Science
Abstract: Standing on the shoulders of giants" is a metaphor for how science progresses: our knowledge grows by expanding and building off what others have learned and taught us cialis online cheap in the past. Implicit in this metaphor is that science is a social enterprise -- we learn from others and we relate what we do to what others have done. However, in recent decades scientists have invented new ways to bring people together at unprecedented scale in the pursuit of advancing science and pushing the thresholds of what we know. These new forms of social enterprise -- made possible by innovations in computing and the widespread reach of the Internet -- are facilitating discovery and innovation in a range of areas of science and technology. In this talk I will survey examples of these new forms of "socially intelligent" science, while also providing a historical context that shows that elements of many of these ideas predate the Internet era.

Biography: Dr. Haym Hirsh is Dean of Computing and Information Science and Professor of Computer Science and Information Science at Cornell University. Previously, he was Professor of Computer Science at Rutgers University. From 2006 to 2010 he served as Director of the Division of Information and Intelligent Systems at the National Science Foundation.


David Jensen: Representing and Reasoning with Experimental and Quasi-Experimental Designs
Abstract: The formulation and widespread adoption of the randomized controlled trial is one of the most important intellectual achievements of the twentieth century. However, the precise logic of RCTs, and the extent to which similar logic can be extended to analysis of data collected under alternative conditions, is not widely known or easily formalized. The language of causal graphical models -- a well-developed formalism from computer science -- can describe much of the logic behind experimental and quasi-experimental designs, and recent extensions to that language can express an even wider array of designs. In addition, this formalization has revealed new types of designs and new opportunities for computational assistance in the analysis of experimental and observational data.

Biography: David Jensen is Associate Professor of Computer Science and Director of the Knowledge Discovery Laboratory at the University of Massachusetts Amherst. From 1991 to 1995, he served as an analyst with the Office of Technology Assessment, an agency of the United States Congress. He received his doctorate from Washington University in St. Louis in 1992. His research focuses on machine learning and knowledge discovery in complex data sets, with applications to computational social science, social network analysis, and fraud detection. His most recent work focuses on discovery of causal knowledge in massive data sets through the automated identification and application of quasi-experimental designs. He regularly serves on the program committees of the International Conference on Machine Learning and the International Conference on Knowledge Discovery and Data Mining. He was a member of the 2006-2007 Defense Science Study Group, he served on the Executive Committee of the ACM Special Interest Group on Knowledge Discovery and Data Mining from 2006 to 2012, and he served on DARPA's Information Science and Technology (ISAT) Group from 2007 to 2012.


Peter D. Karp: "Bioinformatics computation of metabolic models from sequenced genomes"
Abstract: The bioinformatics field has developed the ability to extract far more information from sequenced genomes than was envisioned in the early days of the Human Genome Project. By connecting a set of analytical programs into a computational pipeline, we can recognize genes within a sequenced genome, assign functions to those genes, infer reactions catalyzed by the gene products, arrange those reactions into metabolic pathways, and create a computational metabolic model of the organism. The computational methods used by pipeline components include machine learning, pattern matching, inexact sequence matching, and optimization. This success story can provide lessons to other areas of computational science, and raises interesting questions about what it means for machines to make scientific discoveries.

Biography: Peter D. Karp is Director of the Bioinformatics Research Group at SRI International. Dr. Karp's bioinformatics research has focused on metabolic-pathway bioinformatics, and on biological databases and ontologies. He has developed novel algorithms for predicting the metabolic pathway complement of an organism from its genome, for predicting which genes in an organism code for enzymes missing from its metabolic pathways, and for visualizing metabolic pathways. Karp developed the Pathway Tools software, the EcoCyc and MetaCyc databases, and the BioCyc database collection. Karp has also worked in the area of bioinformatics database integration. Dr. Karp has authored more than 100 publications in bioinformatics and computer science. He is a Fellow of the International Society for Computational Biology and an SRI Fellow. He received the Ph.D. degree in Computer Science from Stanford University, and was a postdoctoral fellow at the NIH National Center for Biotechnology Information.


Claire Monteleoni: "Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science"
Abstract: The threat of climate change is one of the greatest challenges currently facing society. Given the profound impact machine learning has made on the natural sciences to which it has been applied, such as the field of bioinformatics, machine learning is poised to accelerate discovery in climate science. Our recent progress on climate informatics reveals that collaborations with climate scientists also open interesting new problems for machine learning. I will give an overview of challenge problems in climate informatics, and present recent work from my research group in this nascent field. A key problem in climate science is how to combine the predictions of the multi-model ensemble of global climate models that inform the Intergovernmental Panel on Climate Change (IPCC). I will present three approaches to this problem. Our Tracking Climate Models (TCM) work demonstrated the promise of an algorithm for online learning with expert advice, for this task. Given temperature predictions from 20 IPCC global climate models, and over 100 years of historical temperature data, TCM generated predictions that tracked the changing sequence of which model currently predicts best. On historical data, at both annual and monthly time-scales, and in future simulations, TCM consistently outperformed the average over climate models, the existing benchmark in climate science, at both global and continental scales. We then extended TCM to take into generic viagra cheap account climate model predictions at higher spatial resolutions, and to model geospatial neighborhood influence between regions. Our second algorithm enables neighborhood influence by modifying the transition dynamics of the Hidden Markov Model from which TCM is derived, allowing the performance of spatial neighbors to influence the temporal switching probabilities for the best climate model at a given location. We recently applied a third technique, sparse matrix completion, in which we create a sparse (incomplete) matrix from climate model predictions and observed temperature data, and apply a matrix completion algorithm to recover it, yielding predictions of the unobserved temperatures.

Biography: Claire Monteleoni is an assistant professor of Computer Science at The George Washington University, which she joined in 2011. Previously, she was research faculty at the Center for cialis online generic Computational Learning Systems, and adjunct faculty in the Department of Computer Science, at Columbia University. She did a postdoc in Computer Science and Engineering at the University of California, San Diego, and completed her PhD and Masters in Computer Science, at MIT. Her research focus is on machine learning algorithms and theory for problems including learning from data streams, learning from raw (unlabeled) data, learning from private data, and Climate Informatics: accelerating discovery in Climate Science with machine learning. Her papers have received several awards. In 2011, she co-founded the International Workshop on Climate Informatics, which is now entering its third year. She is on the Editorial Board of the Machine Learning Journal, and she served as an Area Chair for ICML 2012, and NIPS 2013.


Zoran Obradovic: "Predictive Modeling of Patient State and Therapy Optimization"
Abstract: Uncontrolled inflammation accompanied by an infection that results in septic shock is the most common cause of death in intensive care units and the 10th leading cause of death overall. In principle, spectacular mortality rate reduction can be achieved by early diagnosis and accurate prediction of response to therapy. This is a very difficult objective due to the fast progression and complex multi-stage nature of acute inflammation. Our ongoing DARPA DLT project is addressing this challenge by development and validation of effective predictive modeling technology for analysis of temporal dependencies in high dimensional multi-source sepsis related data. This lecture will provide an overview of the results of our project, which show potentials for significant mortality reduction in severe sepsis patients.

Biography: Zoran Obradovic’s research interests include data mining, machine learning and complex networks applications in climate modeling and health management. He is the executive editor at the journal on Statistical Analysis and Data Mining, which is the official publication of the American Statistical Association and is an editorial board member at eleven journals. He is general co-chair for 2014 SIAM International Conference on Data Mining and was the program or track chair at many data mining and biomedical informatics conference. His data analytics work is published in more than 280 articles and is cited about 12,000 times (h-index 42).


Christopher Re: "Case Studies in Data-Driven Systems: Building Carbon Maps to Finding Neutrinos"
Abstract: The question driving my work is, how should one deploy statistical data-analysis tools to enhance data-driven systems? Even partial answers to this question may have a large impact on science, government, and industry---each of whom are increasingly turning to statistical techniques to get value from their data. To understand this question, my group has built or contributed to a diverse set of data-processing systems for scientific applications: a system, called GeoDeepDive, that reads and helps answer questions about the geology literature and a muon filter that is used in the IceCube neutrino telescope to process over 250 million events each day in the hunt for the origins of the universe. This talk will give an overview of the lessons that we learned in these systems, will argue that data systems research may play a larger role in the next generation of these systems, and will speculate on the future challenges that such systems may face.

Biography: Christopher (Chris) Re is an assistant professor in the Department of Computer Science at Stanford University. The goal of his work is to enable users and developers to build applications that more deeply understand and exploit data. Chris received his PhD from the University of Washington in Seattle under the supervision of Dan Suciu. For his PhD work in probabilistic data management, Chris received the SIGMOD 2010 Jim Gray Dissertation Award. Chris's papers have received four best-paper or best-of-conference citations, including best paper in PODS 2012, best-of-conference in PODS 2010 twice, and one best-of-conference in ICDE 2009). Chris received an NSF CAREER Award in 2011 and an Alfred P. Sloan fellowship in 2013.


Kiri L. Wagstaff: Look at this gem: Automated data prioritization for scientific discovery of exoplanets, mineral deposits, and more
Abstract: Inundated by terabytes of data flowing from telescopes, microscopes, DNA sequencers, etc., scientists in various disciplines have a need for automated methods for http://www.ejsmith.com/ prioritizing data for review. Which observations are most interesting or unusual, and why? I will describe DEMUD (Discovery by Eigenbasis Modeling of Uninteresting Data), which iteratively prioritizes items from large data sets to provide a diverse traversal of interesting items. By modeling what the user already knows and/or has already seen, DEMUD can focus attention on the unexpected, facilitating new discoveries. Uniquely, DEMUD also provides a domain-relevant explanation for each selected item that indicates why it stands out. DEMUD's explanations offer a first step towards automated interpretation of scientific data discoveries. We are using DEMUD in collaboration with scientists from the Mars Science Laboratory, the Mars Reconnaissance Orbiter, the Kepler exoplanet telescope, Earth orbiters, and more. It provides scalable performance, interpretable output, and new insights into very large data sets from diverse disciplines. This is joint work with James Bedell, Nina L. Lanza, Tom G. Dietterich, Martha S. Gilmore, and David R. Thompson.

Biography: Kiri L. Wagstaff is a senior researcher in artificial intelligence and machine learning and a tactical activity planner for the Opportunity Mars rover at the Jet Propulsion Laboratory. Her research focuses on developing new machine learning and data analysis methods, particularly those that can be used for in situ analysis onboard spacecraft such as orbiters, landers, rovers, and so on. She holds a Ph.D. in Computer Science from Cornell University and an M.S. in Geological Sciences from the University of Southern California. She received a 2008 Lew Allen Award for Excellence in Research for work on the sensitivity of machine learning methods to high-radiation space environments and a 2012 NASA Exceptional Technology Achievement award for work on transient detection methods in radio astronomy data. She is passionate about keeping machine learning relevant to real-world problems and is co-editing a special issue on Machine Learning for Science and Society.


Accepted Papers

  • Kyle Ambert, Aaron Cohen, Gully Burns, Eilis Boudreau and Kemal Sonmez.
    Finna: A Paragraph Prioritization System for Biocuration in the Neurosciences
  • Elizabeth Bradley, Laura Rassbach de Vesine, Kenneth Anderson, Marek Zreda and Christopher Zweck.
    Forensic Reasoning about Paleoclimatology
  • Nicholas Del Rio, Natalia Villanueva Rosales, Deana Pennington, Karl Benedict, Aimee Stewart and Cj Grady.
    ELSEWeb meets SADI: Supporting Data-to-Model Integration for Biodiversity Forecasting
  • Rinke Hoekstra and Paul Groth.
    Linkitup: Link Discovery for Research Data
  • David Kale, Samuel Di, Yan Liu and Yolanda Gil.
    Capturing Data Analytics Expertise with Visualization in Workflows
  • Leonardo Salayandia, Deana Pennington, Ann Gates and Francisco Osuna.
    MetaShare: From Data Management Plans to Knowledge-Based Systems
  • Anita De Waard, Jeremy Alder, Shawn Burton, Richard C. Gerkin, Mark Harviston, David Marques, Shreejoy J. Tripathy and Nathaniel N. Urban.
    Creating an Urban Legend: A System for Electrophysiology Data Management and Exploration


Accepted Abstracts

  • Ashok K. Goel.
    Computational Ideation in Scientific Discovery
  • Louiqa Raschid, Guillermo Palma, Maria-Esther Vidal, Andreas Thor.
    Exploration Using Signatures in Annotation Graph Datasets


Important Note to Authors

Authors of accepted submissions must submit the final version of their papers by September 12th, 2013 5:00 PM PDT to the AAAI submission site. AAAI has emailed the submission instructions to the authors directly, along with a request to submit a permission to distribute form. Papers should be no longer than 8 pages and follow the AAAI style files.

Any author with special special audio visual needs for their presentation (such as poster boards, power strips, flipcharts or laptop speakers/sound) should send the information in the audio/visual form below to the organizers by September 12th, 2013.

Register now! Attendance is limited, so we recommend registering as soon as possible and alert interested colleagues to do so. See below for registration details.

Registration

Attendance is limited, so we recommend that you register as soon as possible and alert any interested colleagues to do so.

Registration is already open, and can be done on-line at the AAAI Fall Symposia registration site.

This symposium will provide a forum for researchers interested in understanding the role of AI techniques in improving or innovating scientific processes. In particular, we encourage submissions that: (1) build on success stories that provide a contextual understanding of why certain approaches worked in scientific domains; (2) push the envelope of discoveries in big data; (3) characterizes the act of discovery as a computing challenge for intelligent systems.

Specific topics of discussion include:

  • What are the broad AI challenges in discovery in big data?
  • How do we characterize discovery informatics?
  • How can we support different ways in which scientists approach different kinds of big data?
  • How do we get to big data from smaller data through automated or assisted integration and aggregation?
  • What integrated AI capabilities are needed to tackle big data in science?
  • How can we improve our understanding of science and discovery processes and the role of AI in the context of those processes?
  • How can we capture science processes and open them to scientists in other disciplines and the broader public?
  • Can AI be effective in identifying surprises and facilitating insights, looking for knowledge gaps using big data?

Other general technical topics of interest also include:

  • Natural language processing techniques for organizing scientific literature
  • Ontologies, knowledge bases, and annotations that model particular areas of scientific knowledge
  • Workflow systems to manage complexbig data analysis processes
  • Semantic representations of metadata for all aspects of scientific processes
  • Knowledge discovery techniques that are embedded in the context of scientific investigations
  • Integrative approaches of machine learning and scientific model induction
  • Automated systems for experiment design, data analysis, and hypothesis generation, refinement and testing
  • User-centered design of intelligent systems that partner with scientists to perform complex tasks
  • Integrated approaches to visualizing big data, models, and the connections between them to foster new insights
  • Social computing systems that let novice participants contribute to scientific tasks

Submissions should be up to 6 pages, using the AAAI style files.

Submissions should be uploaded to the EasyChair submission site. Deadline for submssion June 7, 2013.

Camera-ready copies of accepted papers should be directly submitted to AAAI submission site . Please refer to the Accepted papers tab for more information.

AAAI will hold the compilation copyright on the set of papers for your symposium, and will make them freely accessible in the AAAI Digital Library. Authors of accepted papers will be required to sign the AAAI Distribution License. Authors are allowed to post their papers at their own sites, and retain copyright to their papers.

Co-Chairs

Program Committee

  • Jose Luis Ambite, University of Southern California
  • Yigal Arens, University of Southern California
  • Paolo Ciccarese, Harvard University
  • Kevin B. Cohen, University of Colorado
  • Roxana M. Danger Mercaderes, Imperial College London
  • Helena Deus, DERI Ireland
  • Anita de Waard, Elsevier
  • Michel Dumontier, Stanford University
  • Paul Groth, VU University Amsterdam
  • Tudor Groza, University of Queensland
  • Melissa Haendel, Oregon Health & Science University
  • Yongqun He, University of Michigan
  • Deana Pennington, University of Texas at El Paso
  • Pedro Szekely, University of Southern California
  • Karin Verspoor, National ICT Australia
  • Trish Whetzel, National Center for Biomedical Ontology

The symposium will be held at the Westin Arlington Gateway in Arlington, Virginia. The hotel is next to the Ballston Metro, and within walking distance of NSF, ONR, AFOSR, DARPA, and other government agencies.

The symposium is as part of the AAAI Fall Symposium Series. Please visit the site for the 2013 AAAI Fall Symposia for location, travel arrangements, and other information about the event.

Travel

Registration

Attendance to the symposium is limited. Please use the online registration form for the AAAI FSS-12

Symposium Location

The symposium will be held at the Westin Arlington Gateway in Arlington, Virginia. The hotel is next to the Ballston Metro, and within walking distance of NSF, ONR, AFOSR, DARPA, and other government agencies.

Hotel

A block of rooms has been reserved for attendees at the symposium hotel, the Westin Arlington Gateway. . To reserve a room online, please go to the on-line reservations site. Space is limited so we recommend that you make your reservation now. Reservations made after Wednesday, October 24 will be accepted based on availability at the hotel's prevailing rate.

Local Arrangements

The symposium is organized by AAAI as part of its AAAI Fall Symposium Series. Please visit the site for the 2013 AAAI Fall Symposia for location, travel arrangements, and other information about the event.

  • Submission deadline: June 7, 2013 . EasyChair submission site
  • Notification to authors: July 5, 2013
  • Camera-ready due: September 2, 2013 September 12, 2013, 5pm (PDT) through AAAI press
  • Invited participants registration deadline: September 20, 2013
  • General registration deadline: October 18, 2013
  • Hotel special rates cut-off date: October 18, 2013
  • Symposium: November 15-17, 2013