Discovery Informatics

DI-PSB 2015

admin — Fri, 23 May 2014 22:35:38 +0000

2015 PSB Workshop on Discovery Informatics in Biological and Biomedical Sciences: Research Challenges and Opportunities

In conjunction with the 2015 Pacific Symposium on Biocomputing

January 4-8, 2015

The Big Island of Hawaii

New discoveries in biological, biomedical and health sciences are increasingly being driven by our ability to acquire, share, integrate and analyze, and construct and simulate predictive models of biological systems. While much attention has focused on automating routine aspects of management and analysis of "big data", realizing the full potential of "big data" to accelerate discovery calls for automating many other aspects of the scientific process that have so far largely resisted automation: identifying gaps in the current state of knowledge; generating and prioritizing questions; designing studies; designing, prioritizing, planning, and executing experiments; interpreting results; forming hypotheses; drawing conclusions; replicating studies; validating claims; documenting studies; communicating results; reviewing results; and integrating results into the larger body of knowledge in a discipline. Against this background, the PSB workshop on Discovery Informatics in Biological and Biomedical Sciences explores the opportunities and challenges of automating discovery or assisting humans in discovery through advances (i) Understanding, formalization, and information processing accounts of, the entire scientific process; (ii) Design, development, and evaluation of the computational artifacts (representations, processes) that embody such understanding; and (iii) Application of the resulting artifacts and systems to advance science (by augmenting individual or collective human efforts, or by fully automating science).

The workshop, which is especially timely in the context of the NIH Big Data to Knowledge (BD2K) initiative, brings together a group of scientists with complementary expertise to explore research challenges and opportunities in the informatics of discovery in biomedical sciences including, but not limited to:

Representation and modeling languages with precise formal semantics, for describing, sharing, and communicating scientific models, theories, and hypotheses in biomedical sciences.
Novel approaches to interactive visualization and exploration of complex biomedical data.
Sophisticated approaches to construction of comprehensible and communicable predictive models and discovery of causal mechanisms from disparate types of observational and experimental data, literature, images, spatial, temporal, richly structured e.g., network data in biomedical sciences.
Effective approaches for acquiring and making effective use of background assumptions, hypotheses, knowledge, beliefs and conjectures, arguments, domain expertise, and process descriptions in biomedical sciences.
Algorithms and tools for automating various aspects of discovery

DI-KDD 2014

admin — Fri, 23 May 2014 22:31:09 +0000

ACM SIGKDD Workshop on Discovery Informatics

In conjunction with ACM SIGKDD Conference on Data Mining and Knowledge Discovery (KDD 2014)

August 24, 2014

New York, USA

The emergence of “big data” – data that are far more voluminous, diverse, and inter-related than we know how to cope with – has resulted in the transformation of many historically data poor disciplines into increasingly data rich disciplines. Much attention has focused on the challenges of management, processing, and analysis of big data. However, while we understand how to automate routine aspects of data management http://clanofthecats.com/ and analytics, humans are still largely responsible for most aspects of the scientific process: identifying gaps in the current state of knowledge; generating and prioritizing questions; designing studies; designing, prioritizing, planning, and executing experiments; interpreting results; forming hypotheses; drawing conclusions; replicating studies; validating claims; documenting studies; communicating results; reviewing results; and integrating results into the larger body of knowledge in a discipline. This state of affairs is simply untenable cialis online cheap if we are http://ippp.org/ to realize the full promise and potential of big data.

Against this background, the workshop focuses on Discovery Informatics, an emerging subfield at the intersection of Artificial Intelligence, Knowledge Discovery and Data Mining, Cyber-Human Systems, and Information Integration and Informatics. Discovery Informatics is concerned with (i) Understanding, formalization, and information processing accounts of, and organizational and social structures and incentives that underpin the entire scientific process; (ii) Design, development, and evaluation of the computational artifacts (representations, processes) that embody such understanding; and (iii) Application of the resulting artifacts and systems to advance science (by augmenting individual or collective human efforts, or by fully automating science).

The workshop aims to bring together researchers and graduate students to explore the research challenges, opportunities, and recent advances in all aspects of Discovery Informatics, including in particular:

Representation and modeling languages with precise formal semantics, for describing, sharing, and communicating models, theories, and hypotheses
Sophisticated approaches to construction of comprehensible and communicable predictive models and discovery of causal mechanisms
Effective approaches for acquiring and making effective use of background assumptions, hypotheses, knowledge, beliefs and conjectures, arguments, domain expertise, and process descriptions from literature.
Effective methods for identifying gaps in knowledge and formulating questions
Effective methods for describing, designing, prioritizing, planning observations and experiments
Effective methods for constructing comprehensible and communicable predictive models from observations and experiments
Effective methods for generating, prioritizing and testing hypotheses from data and models.
Sharable and communicable representations and processes, organizational and social structures and practices that facilitate collaborative discovery

SUBMISSIONS:

We invite participants to submit full papers (no longer than 8 pages, describing research results) or extended abstracts or position papers (no longer than 2 pages, describing research in progress, research challenges, or perspectives).

Submissions should use the standard ACM format. Papers and extended abstracts should be submitted electronically (in PDF form) through EasyChair.

Important Dates:
Submission deadline: June 7, 2014
Author notification: June 20, 2014
Submission of camera-ready papers: June 27, 2014
Workshop: August 24, 2014

2014 AAAI Discovery Informatics Workshop

admin — Tue, 14 Jan 2014 19:56:57 +0000

Discovery Informatics: Scientific Discoveries Enabled by AI

Sunday July 27, 2014

Co-located with AAAI 2014

Quebec City, Quebec

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Description
Program
Invited Speakers
Accepted Papers
Organizers
Submissions
Location
Registration

Discovery Informatics encompasses research on intelligent systems in support of scientific discoveries. At the core of Discovery Informatics research is modeling and capturing some aspect of the scientific processes that can lead to new discoveries. The focus of this workshop will be on new discoveries resulting from intelligent systems that use AI techniques, highlighting the importance of the discovery, the challenges that led to requiring an AI approach, and understanding the generality of the approach taken for other science problems and domains.

We are happy to announce the workshop's invited speakers:

Phil Bourne, Assistant Director for Data Science, National Institutes of Health
Paul Cohen, Program Manager, Defense Advanced Research Projects Agency
Tom Dietterich, Professor of Computer Science at Oregon State University and President-Elect of AAAI

Schedule

Download the symposium schedule.

Workshop Description

The focus of this workshop will be on new discoveries resulting from intelligent systems that use AI techniques, highlighting the importance of the discovery, the challenges that led to requiring an AI approach, and understanding the generality of the approach taken for other science problems and domains. Topics include but are not limited to machine reading from scientific articles, information integration and model synthesis, scientific knowledge modeling and inference, planning data analysis and experiment tasks, and learning from scientific data.

Program

The workshop will be focused on discussion, based on two invited talks, seven long paper presentations, and three short paper presentations. There will also be a session with short previews of six AAAI/IAAI conference papers relevant to the workshop topics.

Schedule

Download the symposium schedule.

Invited Speakers

Phil Bourne: "Ask not what the NIH can do for you; ask what you can do for the NIH"
Abstract: The NIH is about to embark on a series of initiatives intended to stimulate the development of an ecosystem surrounding biomedical research as a digital enterprise. The discovery informatics community can be major contributors to that enterprise. What those initiatives are and and how you could be involved will be discussed.

Biography: Philip E. Bourne PhD is the Associate Director for Data Science (ADDS) at the National Institutes of Health. Formally he was Associate Vice Chancellor for Innovation and Industry Alliances and Professor in the Department of Pharmacology and Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of California San Diego. He was also Associate Director of the RCSB Protein Data Bank. Bourne's professional interests focus on service and research. He serves the national biomedical community through contributing ways to maximize the value (and hence accessibility) of scientific data. His research focuses on relevant biological and educational outcomes derived from computation and scholarly communication. This implies algorithms, text mining, machine learning, metalanguages, biological databases, and visualization applied to problems in systems pharmacology, evolution, cell signaling, apoptosis, immunology and scientific dissemination. He has published over 300 papers and 5 books, one of which sold over 150,000 copies. Bourne is committed to furthering the free dissemination of science through new models of publishing and better integration and subsequent dissemination of data and results which as far as possible should be freely available to all. He is the co-founder and founding Editor-in-Chief of the open access journal PLOS Computational Biology. Bourne is a Past President of the International Society for Computational Biology, an elected fellow of the American Association for the Advancement of Science (AAAS), the International Society for Computational Biology (ISCB) and the American Medical Informatics Association (AMIA).

Paul Cohen: "Big Mechanism"
Abstract: Dr. John Snow's nineteenth century maps of cholera deaths in London were a kind of big data, but it took Snow's human ingenuity to infer from these data that a water pump was probably a causal mechanism of disease transmission. Nearly two centuries on, big data is vastly bigger, but human ingenuity is still required to infer causal mechanisms. DARPA's Big Mechanism program aims to change that. Big mechanisms are large, explanatory models of complicated systems in which interactions have important causal effects. The collection of big data is increasingly automated, but the creation of big mechanisms remains a human endeavor made increasingly difficult by the fragmentation and distribution of knowledge. To the extent that the construction of big mechanisms can be automated, it could change how science is done. The first challenge problem for the Big Mechanism program is cancer signaling pathways. The program has three primary technical areas: Computers should read abstracts and papers in cancer biology to extract fragments of cancer pathways. Next, they should assemble these fragments into complete pathways of unprecedented scale and accuracy, and should figure out how pathways interact. Finally, computers should determine the causes and effects that might be manipulated, perhaps even to prevent or control cancer. Although the domain of the Big Mechanism program is cancer biology, the overarching goal of the program is to develop technologies for a new kind of science in which research is integrated more or less immediately -- automatically or semi-automatically -- into causal, explanatory models of unprecedented completeness and consistency.

Biography: Dr. Paul Cohen joined DARPA as a program manager in September 2013. His research interests span artificial intelligence and include machine learning, language, vision, semantic technology, data analysis, information theory and education informatics. Dr. Cohen joined DARPA from the University of Arizona, where he is professor and founding director of the university’s School of Information: Science, Technology and Arts. He has also served as head of the university’s department of computer science. Prior to joining the University of Arizona, Dr. Cohen worked at the University of Southern California. At that institution, he served as director of the Center for Research on Unexpected Events and deputy director of the Intelligent Systems Division. He began his career as professor of computer science at the University of Massachusetts. Dr. Cohen has published nearly 200 peer-reviewed articles and is the author of Empirical Methods for Artificial Intelligence (MIT Press, 1995). He is co-author of five books and has contributed chapters to another 20 books. He is an elected Fellow of the Association for the Advancement of Artificial Intelligence. Dr. Cohen holds a Doctor of Philosophy degree in Computer Science and Psychology from Stanford University, a Master of Science degree in Psychology from the University of California, Los Angeles and a Bachelor of Science degree in Psychology from the University of California, San Diego.

Tom Dietterich: "Constructing a Continent-Scale Bird Migration Model to Understand Bird Decision Making"
Abstract: The BirdCast team (Cornell Lab of Ornithology, Oregon State, and U Mass, Amherst) is building a large, multi-species migration model for the US. One of the key goals of this project is to formulate and test hypotheses about bird migration. For example, what signals are birds using to decide when to begin migration, when to stop over, and when to continue? Are they waiting for favorable winds? Suitable temperatures? Humidity? Food availability? Are they on an absolute time schedule or is there temporal flexibility? This talk will describe our model, which is a latent variable graphical model expressed in Dan Sheldon's Collective Graphical Model formalism. Two challenges will be discussed: (a) the computational challenges of fitting this model and (b) the representational and inferential challenges of working with scientific hypotheses represented as latent variables.

Biography: Dr. Dietterich is Distinguished Professor and Director of Intelligent Systems in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. In 1987, he was named a Presidential Young Investigator for the NSF. In 1990, he published, with Dr. Jude Shavlik, the book entitled Readings in Machine Learning, and he also served as the Technical Program Co-Chair of the National Conference on Artificial Intelligence (AAAI-90). From 1992-1998 he held the position of Executive Editor of the journal Machine Learning. The Association for the Advancement of Artificial Intelligence named him a Fellow in 1994, and the Association for Computing Machinery did the same in 2003. In 2000, he co-founded a new, free electronic journal: The Journal of Machine Learning Research, and he is currently a member of the Editorial Board. He served as Technical Program Chair of the Neural Information Processing Systems (NIPS) conference in 2000 and General Chair in 2001. He is Past-President of the International Machine Learning Society, a member of the IMLS Board, and he also serves on the Advisory Board of the NIPS Foundation. He is currently President-Elect of the Association for the Advancement of Artificial Intelligence and will serve a 2-year term as President from 2014-2016. Dr. Dietterich's currently pursues interdisciplinary research at the boundary of computer science, ecology, and sustainability policy. He is PI (with Carla Gomes of Cornell) of an 5-year NSF Expedition in Computational Sustainability. He is part of the leadership team for OSU's Ecosystem Informatics programs including the NSF Summer Institute in Ecoinformatics.

Accepted Long Papers

Gully Burns and Hans Chalupsky.
‘Its All Made Up’ - Why we should stop building representations based on interpretive models and focus on experimental evidence instead
Ishanu Chattopadhyay and Hod Lipson.
Data Smashing: Uncovering lurking order in data
Ishanu Chattopadhyay and Hod Lipson.
Distilling Evidence Of Long-range Direction-specific Causal Cross-talk In Molecular Evolution Of Retro-viral Genomes
Ken-Ichi Fukui, Daiki Inaba and Masayuki Numao.
Discovery of Damage Patterns in Fuel Cell and Earthquake Occurrence Patterns by Co-occurring Cluster Mining
Ashok Goel and David Joyner.
Computational Ideation in Scientific Discovery: Interactive Construction, Evaluation and Revision of Conceptual Models
Kazjon Grace and Mary Lou Maher.
Using computational creativity to guide data-intensive scientific discovery
Emily Leblanc, Marcello Balduccini and William Regli.
Towards a Content-Based Material Science Discovery Network

Accepted Short Papers

Tim Clark, Carole Goble, Paolo Ciccarese.
Discoveries and Anti-Discoveries on the Web of Argument and Data
Anita de Waard.
Ten Habits of Highly Effective Data
Kevin M. Livingston, Michael Bada, William A. Baumgartner Jr., Lawrence E. Hunter.
Semantically Integrating Biomedical Databases to Support Inference

AAAI/IAAI Conference Paper Highlights

Hazem Radwan Ahmed, Janice I. Glasgow.
Pattern Discovery in Protein Networks Reveals High-Confidence Predictions of Novel Interactions
Gadi Aleksandrowicz, Hana Chockler, Joseph Y. Halpern, Alexander Ivrii.
The Computational Complexity of Structure-Based Causality
Alnur Ali, Rich Caruana, Ashish Kapoor.
Learning with Model Selection
James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani.
Automatic Construction and Natural-Language Description of Nonparametric Regression Models
Jun Yu, Rebecca A. Hutchinson, Weng-Keen Wong.
A Latent Variable Model for Discovering Bird Species Commonly Misidentified by Citizen Scientists
Jun Yu, Weng-Keen Wong, Steve Kelling.
Clustering Species Accumulation Curves to Identify Skill Levels of Citizen Scientists Participating in the eBird Project

Organizers

Yolanda Gil (Co-Chair), Information Sciences Institute and Department of Computer Science, University of Southern California

Lawrence Hunter (Co-Chair), Department of Pharmacology (Denver) and Department of Computer Science (Boulder), University of Colorado

Program Committee

Elizabeth Bradley, University of Colorado Boulder

Gully APC Burns, University of Southern California

Ishanu Chattopadhyay, Cornell University

Tim Clark, Harvard University

Anita De Waard, Elsevier Labs

Michel Dumontier, Stanford University

Saso Dzeroski, Jozef Stefan Institute

Susan L. Epstein, The City University of New York

Paul Groth, Vrije Universiteit Amsterdam

Ashok K. Goel, Georgia Institute of Technology

Melissa Haendel, Oregon Health & Science University

Jim Hendler, Rensselaer Polytechnic Institute

Haym Hirsh, Cornell University

Rinke Hoekstra, Vrije Universiteit Amsterdam

Vasant Honavar, Pennsylvania State University

David Jensen, University of Massachusetts Amherst

David Kale, University of Southern California

Peter Karp, SRI International

Craig Knoblock, University of Southern California

Hod Lipson, Cornell University

Yan Liu, University of Southern California

Claire Monteleoni, The George Washington University

Mark Musen, Stanford University

Nigam Shah, Stanford University

Loren Terveen, University of Minnesota

Natalia Villanueva-Rosales, University of Texas at El Paso

Kiri Wagstaff, NASA/JPL

Submissions

Submissions can be in three categories:

Abstracts that describe articles already published or soon to appear that describe discoveries made with AI systems. Abstracts should be 1 page in length.
Articles describing ongoing work that has the potential of leading to new discoveries. Articles should be at most 8 pages.
Position papers with unique perspectives on discovery informatics. Position papers should be at most 4 pages.

Submissions should use the AAAI style files.

Submissions can be made through this site.

Important Dates

Submission deadline: April 10, 2014

Notification date: May 1, 2014

Author accepted paper submission deadline: May 15, 2014

Workshop date: July 27 or July 28, 2014 (TBD)

Location

The workshop will be co-located with AAAI 2014.

Registration

To register for the workshop, please use the AAAI 2014 registration site.

2013 Discovery Informatics Symposium

admin — Sun, 10 Mar 2013 22:26:38 +0000

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

Friday – Sunday, November 15–17, 2013

AAAI Fall Symposium Series

Arlington, Virginia

Description
Program
Invited Talks
Accepted Papers
Registration
Organizers
Location
Travel
Submissions
Important Dates

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

Gully APC Burns , University of Southern California
Yolanda Gil, University of Southern California
Yan Liu, University of Southern California
Natalia Villanueva-Rosales, University of Texas at El Paso

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

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

What is Discovery Informatics

admin — Wed, 07 Mar 2012 22:59:31 +0000

The synergies between advances in computing and advances in science open the doors to exciting research agendas in computer science. Scientific questions have motivated computer science research in many areas including distributed sensor networks, high-end computing, distributed systems, scalable databases, statistical and data mining algorithms, computer networks and the web itself. Scientists have now the means to collect and process unprecedented amounts of data to understand phenomena that could not be studied before, from climate change to social networks to phylogenetics.

A new community of Discovery Informatics is emerging to understand the role of information and intelligent systems research in improving and innovating scientific processes in ways that will accelerate discoveries. Although computing has become central to science, there are important hallmarks in the 21st century that remain largely unaddressed and where AI research plays a central role.

First, discovery processes are increasingly complex and broader in scope. They generic cialis cheap remain largely human driven, and human cognitive limitations have become a bottleneck. New approaches are needed to address this complexity.

Second, data must be connected more closely than ever to the models of the phenomena under study. The current separation of models and data is hurting our ability to test and improve models. We must improve our understanding of how to link data with models of the phenomena under study.

Third, science is an increasingly social endeavor. Recent systems enable citizen volunteers to contribute large amounts of data, annotations, or complex processing results that result in scientific discoveries. We need to design new approaches to harness human abilities in all forms to contribute to science.

Discovery Informatics focuses on computing advances aimed at identifying scientific discovery processes that require knowledge assimilation and reasoning, and applying principles of intelligent computing and information systems in http://www.gulfcoastretirement.org/admin/generic/ order to understand, automate, improve, and innovate any aspects of those processes.

Interested in learning more? Read this report and join our mailing list

2012 Discovery Informatics Symposium

admin — Sun, 04 Mar 2012 17:53:15 +0000

Discovery Informatics Symposium: The Role of AI Research in Innovating Scientific Processes

November 2-4, 2012

AAAI Fall Symposium Series

Arlington, VA

Quick Links
Description
Program
Invited Talks and Panels
Accepted Papers
Registration
Organizers
Travel
Submissions

A wordl made from symposium notes:

A wordl made from twitter feed for #discoveryinformatics:

Download the agenda, and a flier with an overview of the symposium.

See the live session notes.

There is a paper missing from the proceedings: Marcelo Tallis, Drashti Dave and Gully Burns on "Preliminary meta-analyses of experimental design with examples from HIV vaccine protection studies", you can download it here.

Symposium Description

Addressing the ambitious research agendas put forward by many scientific disciplines requires meeting a multitude of challenges in intelligent systems, information sciences, and human-computer interaction. There are many aspects of the scientific discovery process that our community could help automate, facilitate, or make more efficient through artificial intelligence techniques. For example, although considerable efforts have been directed toward data modeling and integration, these activities continue to demand large investments of scientists’ time and effort. The scientific literature continues to grow and is becoming more and more unmanageable for researchers operating in the most active disciplines. Better interfaces for collaboration, visualization, and understanding would significantly improve scientific practice. Scientific data, publications, and tools could be published in open formats with appropriate semantic descriptions and metadata annotations to improve sharing and dissemination. Opportunities for broader participation in well-defined scientific tasks enable human contributors to provide large amounts of data, annotations, or complex processing results that could not otherwise be obtained. These are just some examples of areas where there are opportunities for artificial intelligent techniques could make a difference. Improvements and innovations across the spectrum of scientific processes and activities will have a profound impact on the rate of scientific discoveries.

This symposium provides a forum for researchers interested in understanding the role of AI techniques in improving or innovating scientific processes.

Download a flier advertising the symposium.

Program Highlights

The symposium will include invited talks, paper presentations, panel discussions, and plenary sessions. Six invited speakers will provide their personal perspectives on successes and challenges for Discovery Informatics. There will be seven full papers presented, interleaved with the invited talks. Two of the sessions will be panel discussions on current topics of interest, with panelists from a variety of perspectives including academia, funding agencies, and industry. The symposium will open and close with plenary sessions that will serve for exchange of general observations and synthesis of views for all attendees. AAAI will hold an evening reception as well as a joint session where major highlights of the other parallel symposia will be presented.

Download the symposium schedule.

Invited Speakers

Timothy W. Clark, Harvard University (bio)
William Cohen, Carnegie Mellon University (bio)
Lawrence Hunter, University of Colorado (bio)
Chris Lintott, University of Oxford (bio)
Hod Lipson, Cornell University (bio)
Jude Shavlik, University of Wisconsin Madison (bio)

Invited Panels

Challenges in Big Data: Discoveries at the Fringe of Science. Panelists: Lise Getoor, University of Maryland; Haym Hirsh, Rutgers University; Vasant Honavar, NSF; Steven Salzberg, Johns Hopkins University.
Discovery Informatics: Innovating Science Practice one Scientist at a Time. Panelists: Melissa Cragin, AAAS Science and Technology Fellow; Christopher Erdmann, The John G. Wolbach Library Harvard-Smithsonian Center for Astrophysics; Yolanda Gil, University of Southern California; Barbara Ransom, NSF.

Accepted Papers

Mohammad Taha Bahadori and Yan Liu
On Causality Inference in Time Series
Nicholas Del Rio and Paulo Pinheiro Da Silva
Capturing and Using Knowledge about the Use of Visualization Toolkits
Susan Epstein, Xingjian Li, Peter Valdez, Sofia Grayevsky, Eric Osisek, Xi Yun and Lei Xie
Discovering Protein Clusters
Pat Langley and Glen Hunt
A Web-Based Environment for Explanatory Biological Modeling
Arman Masoumi and Mikhail Soutchanski
Organic Synthesis Planning Using the Situation Calculus
Leonardo Salayandia, Ann Gates and Paulo Pinheiro
An Evaluation Approach for Interactions between Abstract Workflows and Provenance Traces
Marcelo Tallis, Drashti Dave and Gully Burns
Preliminary meta-analyses of experimental design with examples from HIV vaccine protection studies

The symposium is part of the AAAI Fall Symposium Series. Please visit the site for the 2012 AAAI Fall Symposium to learn more about AAAI and the overall event.

Program

Schedule

Download the symposium schedule.

Invited Talks

Timothy W. Clark: "Pervasive Semantic Annotation of Biomedical Literature using Domeo"
Abstract: Despite the fact that we now have access to almost all peer reviewed publications on the Web, these publications appear to us in a linear form which is a replica of print journals. At the same time there are increasingly attractive opportunities to surface data and concepts directly on the Web, using semantic organization. This talk will discuss how - for biomedical researchers - the Web of Documents and the Web of Data / Concepts can be bridged and integrated, using the Domeo Web Annotation Toolkit and the Annotation Ontology (AO). Domeo and AO can be used to annotate any HTML document whether or not it is under update control of the user. The AO annotation can be selectively shared and exchanged and is orthogonal to any specific biomedical domain ontology. We believe this approach will be extremely useful in drug discovery to break down information silos, increase information awareness and sharing, and integrate terminologies and data with documents and text, both public and private. We will discuss applications we are currently developing in collaboration with a major pharma.

Biography: Dr. Clark is Director of Bioinformatics, at the MassGeneral Institute for Neurodegenerative Disease & Instructor in Neurology of the Harvard Medical School. He trained as a computer scientist at Johns Hopkins, and began his work in life science informatics as one of the initial developers of the National Center for Biotechnology Information (NCBI) Genbank and a collaborator on the initial NCBI prototype of PubMed. He subsequently served as Vice-President of Informatics at Millennium Pharmaceuticals, where his team built one of the first integrated bio- and chemi-informatics software platforms in the pharmaceutical industry. He is a founding Editorial Board member of the journal Briefings in Bioinformatics, an Advisory Committee member of the World Wide Web Consortium (http://w3.org), and an Advisory Board member for the Neuroscience Information Framework (http://nif.nih.gov). Dr. Clark's research program focuses on multi-modal semantic integration of biomedical web communities, scientific discourse and experimental results. He is the Principal Investigator of the Semantic Web Applications in Neuromedicine (SWAN) (http://swan.mindinformatics.org) and Science Collaboration Framework (http://www.sciencecollaboration.org) projects. His informatics group built the reusable software platform for Stembook (http://www.stembook.org), an online review of stem cell biology published by the Harvard Stem Cell Institute, and created the Parkinson's Disease (PD) Online Research website (http://pdonlineresearch.org) in collaboration with the Michael J. Fox Foundation for Parkinson's Research.

William Cohen, Carnegie Mellon University: "Reasoning with Data Extracted from the Scientific Literature"
Abstract: The growing size of the scientific literature has led to a number of attempts to automatically extract entities and relationships from scientific papers, and then to populate databases with this extracted information. In my group we have been exploring techniques for using this sort of extracted information for specific tasks, including "bootstrapping" to improve the coverage of an extraction system, retrieval tasks involving the scientific literature, and modeling protein-protein interaction data. This joint work with Ramnath Balasubramanyan, Dana Movshovitz-Attias, and Ni Lao.

Biography: William Cohen received his bachelor's degree in Computer Science from Duke University in 1984, and a PhD in Computer Science from Rutgers University in 1990. From 1990 to 2000 Dr. Cohen worked at AT&T Bell Labs and later AT&T Labs-Research, and from April 2000 to May 2002 Dr. Cohen worked at Whizbang Labs, a company specializing in extracting information from the web. Dr. Cohen is President of the International Machine Learning Society, an Action Editor for the Journal of Machine Learning Research, and an Action Editor for the journal ACM Transactions on Knowledge Discovery from Data. He is also an editor, with Ron Brachman, of the AI and Machine Learning series of books published by Morgan Claypool. In the past he has also served as an action editor for the journal Machine Learning, the journal Artificial Intelligence, and the Journal of Artificial Intelligence Research. He was General Chair for the 2008 International Machine Learning Conference, held July 6-9 at the University of Helsinki, in Finland; Program Co-Chair of the 2006 International Machine Learning Conference; and Co-Chair of the 1994 International Machine Learning Conference. Dr. Cohen was also the co-Chair for the 3rd Int'l AAAI Conference on Weblogs and Social Media, which was held May 17-20, 2009 in San Jose, and was the co-Program Chair for the 4rd Int'l AAAI Conference on Weblogs and Social Media, which will be held May 23-26 at George Washington University in Washington, D. C. He is a AAAI Fellow, and in 2008, he won the SIGMOD "Test of Time" Award for the most influential SIGMOD paper of 1998. Dr. Cohen's research interests include information integration and machine learning, particularly information extraction, text categorization and learning from large datasets. He holds seven patents related to learning, discovery, information retrieval, and data integration, and is the author of more than 180 publications.

Lawrence Hunter, University of Colorado - Denver: "The First Artificial Mind Will Think About Molecular Biomedicine"
Abstract: Biomedicine, particularly as informed by genome-scale instrumentation, provides a unique domain for artificial intelligence and discovery informatics research. There are at least three phenomena that contribute to its status as a good domain for AI. First, there are several important characteristics of the domain, including (a) the knowledge-based (rather than law-like) nature of scientific explanation in biomedicine, (b) the modest role that common sense knowledge plays in biological reasoning, and (c) the possibility of embodiment of programs in the context of powerful automated experimental instrumentation. Second, there are a variety of highly significant resources available to researchers developing AI systems, including (a) extensive, continuously expanding, publicly available, and incompletely analyzed dataset of high value; (b) carefully constructed ontological resources constructed and maintained by diverse communities of experts, and (c) many specific use cases that offer clearly defined and highly significant problems that AI techniques have high potential to address. Finally, there is an extensive community of biomedical researchers and practitioners highly motivated to exploit and interact with computational systems that increase the quality, speed or ease of their scientific insights. The power of this combination of eager user community, valuable existing resources and appropriate domain characteristics is already clear from existing work in biomedical discovery informatics, but, as this talk will try to argue, the future is even brighter.

Biography: Dr. Lawrence Hunter is the Director of the Computational Bioscience Program and of the Center for Computational Pharmacology at the University of Colorado School of Medicine, and a Professor in the departments of Pharmacology and Computer Science (Boulder). He received his Ph.D. in computer science from Yale University in 1989, and then spent more than 10 years at the National Institutes of Health, ending as the Chief of the Molecular Statistics and Bioinformatics Section at the National Cancer Institute. He inaugurated two of the most important academic bioinformatics conferences, ISMB and PSB, and was the founding President of the International Society for Computational Biology. Dr. Hunter's research interests span a wide range of areas, from cognitive science to rational drug design. His primary focus recently has been cheap viagra online the integration of natural language processing, knowledge representation and machine learning techniques and their application to interpreting data generated by high throughput molecular biology.

Chris Lintott, University of Oxford: "Efficient crowdsourcing: How to do science with 600,000 participants"
Abstract: Citizen science in the form of crowdsourcing has to proved to be an effective response to the growing size of scientific datasets. This talk will present strategies and results from the Zooniverse, a collection of projects which have enabled more than 500,000 people to help scientists classify galaxies, discover planets, sort through whale songs and even transcribe ancient papyri. As datasets continue to grow, these projects must adapt, and the talk will concentrate on methods which move beyond the current naive random task assignment model. A dynamic bayesian classification of volunteers, applied to a supernova hunting project, was able to achieve much greater efficiency in classification, while dividing classifiers into communities based on their ability and behaviour. Future development of such systems will need to incorporate such analysis into their methodology, allowing user behaviour to guide task allocation, training and perhaps even collaboration.

Biography: Chris Lintott is a researcher in the department of physics at the University of Oxford where he is also a junior research fellow at New College. As PI of Galaxy Zoo and chair of the Citizen Science Alliance, he leads a large, transatlantic and multidisciplinary team of developers, scientists and educators with the aim of building the widest possible range of projects that enable meaningful public participation in science. His own research focuses on the formation of the present day population of galaxies, and he is a strong advocate of public understanding of science. In this latter role he serves on the board of trustees of Royal Museums Greenwich, and is co-presenter of the long-running BBC series 'The Sky at Night'.

Hod Lipson, Cornell University: "The Robotic Scientist: Distilling Natural Laws from Experimental Data, from particle physics to computational biology"
Abstract: Can machines discover scientific laws automatically? For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. This talk will outline a series of recent research projects, starting with self-reflecting robotic systems, and ending with machines that can formulate hypotheses, design experiments, and interpret the results, to discover new scientific laws. While the computer can discover new laws, will we still understand them? Our ability to have insight into science may not keep pace with the rate and complexity of automatically-generated discoveries. Are we entering a post-singularity scientific age, where computers not only discover new science, but now also need to find ways to explain it in a way that humans can understand? We will see examples from psychology to cosmology, from classical physics to modern physics, from big science to small science.

Biography: Hod Lipson is an Associate Professor of Mechanical & Aerospace Engineering and Computing & Information Science at Cornell University in Ithaca, NY. He directs the Creative Machines Lab, which focuses on novel ways for automatic design, fabrication and adaptation of virtual and physical machines. He has led work in areas such as evolutionary robotics, multi-material functional rapid prototyping, machine self-replication and programmable self-assembly. Lipson received his Ph.D. from the Technion - Israel Institute of Technology in 1998, and continued to a postdoc at Brandeis University and MIT. His research focuses primarily on biologically-inspired approaches, as they bring new ideas to engineering and new engineering insights into biology.

Jude Shavlik, University of Wisconsin - Madison: "Human-in-the-Loop Machine Learning"
Abstract: Machine learning has made tremendous progress over the past several decades. It has become one of today’s most important technologies for discovery and its future impact is likely to grow rapidly for the foreseeable future. However, to use the powerful capabilities offered by machine learning, domain experts typically need to find a collaborator who is a highly trained computer scientist possessing substantial experience with machine learning. This greatly limits the impact of this powerful technology. We are addressing the important challenge of reducing the barrier to entry for using machine learning by allowing domain experts to more directly communicate their expertise to machine learning algorithms. Providing such domain expertise in an effective manner promises to democratize machine learning, more generic cialis cheap quickly spreading this valuable technology to tasks where it can have a substantial impact. We are focusing on allowing domain experts to do more than providing (a) the features used to describe examples and (b) the desired outputs for training examples. We are creating learning algorithms that accept naturally expressed 'advice' whenever a domain expert has some knowledge that he or she wishes to provide. The human-provided advice need not be 100% correct since our learning algorithms are robust in the presence of imperfect advice.

Biography: Jude Shavlik is a Professor of Computer Sciences and of Biostatistics and Medical Informatics at the University of Wisconsin - Madison, and is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). He has been at Wisconsin since 1988, following the receipt of his PhD from the University of Illinois for his work on Explanation-Based Learning. His current research interests include machine learning and computational biology, with an emphasis on using rich sources of training information, such as human-provided advice. He served for three years as editor-in-chief of the AI Magazine and serves on the editorial board of about a dozen journals. He chaired the 1998 International Conference on Machine Learning, co-chaired the First International Conference on Intelligent Systems for Molecular Biology in 1993, co-chaired the First International Conference on Knowledge Capture in 2001, was conference chair of the 2003 IEEE Conference on Data Mining, and co-chaired the 2007 International Conference on Inductive Logic Programming. He was a founding member of both the board of the International Machine Learning Society and the board of the International Society for Computational Biology. He co-edited, with Tom Dietterich, "Readings in Machine Learning." His research has been supported by DARPA, NSF, NIH (NLM and NCI), ONR, DOE, AT&T, IBM, and NYNEX.

Invited Panels

"Challenges in Big Data: Discoveries at the Fringe of Science"
Panelists: Lise Getoor, University of Maryland; Haym Hirsh, Rutgers University; Vasant Honavar, NSF; Steven Salzberg, Johns Hopkins University.

"Discovery Informatics: Innovating Science Practice one Scientist at a Time"
Panelists: Melissa Cragin, AAAS Science and Technology Fellow; Christopher Erdmann, The John G. Wolbach Library Harvard-Smithsonian Center for Astrophysics; Yolanda Gil, University of Southern California; Barbara Ransom, NSF.

Accepted Papers

Mohammad Taha Bahadori and Yan Liu
On Causality Inference in Time Series
Nicholas Del Rio and Paulo Pinheiro Da Silva
Capturing and Using Knowledge about the Use of Visualization Toolkits
Susan Epstein, Xingjian Li, Peter Valdez, Sofia Grayevsky, Eric Osisek, Xi Yun and Lei Xie
Discovering Protein Clusters
Pat Langley and Glen Hunt
A Web-Based Environment for Explanatory Biological Modeling
Arman Masoumi and Mikhail Soutchanski
Organic Synthesis Planning Using the Situation Calculus
Leonardo Salayandia, Ann Gates and Paulo Pinheiro
An Evaluation Approach for Interactions between Abstract Workflows and Provenance Traces
Marcelo Tallis, Drashti Dave and Gully Burns
Preliminary meta-analyses of experimental design with examples from HIV vaccine protection studies

Important Note to Authors

Authors of accepted submissions must submit the final version of their papers by September 7, 2012. Papers should be no longer than 8 pages and follow the AAAI style files. AAAI will email the submission site will be emailed to the authors directly, along with a request to submit the permission to distribute form.

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

AUTHOR AUDIO/VISUAL FORM

All rooms in which the Symposia are held will have as standard equipment an LCD projector and screen. Individuals requiring special audio visual needs (such as poster boards, power strips, flipcharts or laptop speakers/sound) for their presentations are requested to return the form by September 7, 2012 to AAAI at fss12@aaai.org..

PRESENTER NAME:
SYMPOSIUM:
PAPER TITLE:
DATE AND TIME OF YOUR PRESENTATION:
TELEPHONE:
EMAIL:
SPECIAL A/V REQUEST (please only list what you would like AAAI to provide):

Please note that A/V requests are subject to budget restrictions. Authors are required to provide their own laptop computers, as well as all software needed to operate programs. Additional connections to the Internet in meeting rooms are restricted viagra online cheap to availability and budget considerations, and must be requested at least two months prior to the event.

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. To register by mail, use the registration form.

We seek submissions that: (1) report on success stories that illustrate the potential of future research in this field; (2) discuss lessons learned in the process of addressing challenging aspects of the scientific process; (3) analyze the impact of a particular technique in an area of science and reflect on its potential for broader applicability in other sciences; and (4) propose future concepts grounded in lessons learned and an understanding of the challenges in the scientific discovery process.

Topics of interest include but are not limited to:

Ontologies and knowledge bases that model particular areas of scientific knowledge
Semantic representations of metadata for all aspects of scientific processes
Techniques for organizing scientific literature
Workflow systems to manage complex data analysis 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 and refinement
User-centered design of intelligent systems that partner with scientists to perform complex tasks
Integrated approaches to visualizing data, models, and the connections between them to foster new insights
Cognitive-centered design of scientist aids
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 submission site no later than June 5 2012 before midnight on the timezone of your choice.

Important Dates

Submission deadline: June 22, 2012
Notification to authors: July 31, 2012
Camera-ready due: September 7, 2012
Registration deadline: September 14, 2012
Symposium: November 2-4, 2012

CO-CHAIRS

Will Bridewell, Stanford University
Yolanda Gil, University of Southern California
Haym Hirsh, Rutgers University
Kerstin Kleese van Dam, Pacific Northwest National Laboratory
Karsten Steinhaeuser, University of Minnesota

PROGRAM COMMITTEE

Cecilia Aragon, University of Washington
Phil Bourne, University of California San Diego
Elizabeth Bradley, University of Colorado
Paolo Ciccarese, Harvard University
Susan Davidson, University of Pennsylvania
Helena Deus, Digital Enterprise Research Institute
Tom Dietterich, Oregon State University
Yolanda Gil, University of Southern California
Clark Glymour, Carnegie Mellon University
Carla Gomes, Cornell University
Alexander Gray, Georgia Institute of Technology
Larry Hunter, University of Colorado
David Jensen, University of Massachusetts Amherst
Vipin Kumar, University of Minnesota
Hod Lipson, Cornell University
Huan Liu, Arizona State University
Yan Liu, University of Southern California
Miriah Meyer, University of Utah
Mark Musen, Stanford University
Andrey Rzhetsky, University of Chicago
Steve Sawyer, Syracuse University
Alex Schliep, Rutgers University
Christian Schunn, University of Pittsburgh
Nigam Shah, Stanford University
Alex Szalay, The Johns Hopkins University
Loren Terveen, University of Minnesota
Raul E. Valdes-Perez, Vivisimo Inc.
Evelyne Viegas, Microsoft Research

Travel

Symposium Location

The symposium is as part of the AAAI Fall Symposium Series. Please visit the site for the 2012 AAAI Fall Symposia for location, travel arrangements, and other information about the event.

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 10 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 2012 AAAI Fall Symposia for location, travel arrangements, and other information about the event.

2012 Discovery Informatics Workshop

admin — Tue, 29 Nov 2011 22:52:12 +0000

Discovery Informatics Workshop: Science Challenges for Intelligent Systems

February 2-3, 2012

Home
Description
Agenda
Location
Organizers
Participants
Documents
Sponsorship

Highlights

An executive summary of the workshop report is available: Executive Summary of the 2012 NSF Discovery Informatics Workshop Report.

The final workshop report is available: 2012 NSF Discovery Informatics Workshop Report.

A slide presentation is available: 2012 NSF Discovery Informatics Workshop Presentation, given at NSF on June 2012.

Join us at this follow-on event: 2012 Discovery Informatics Symposium, on November 2-4, 2012 in Washington DC.

What is Discovery Informatics?

First, discovery processes are increasingly complex and broader in scope. They remain largely human driven, and human cognitive limitations have become a bottleneck. New approaches are needed to address this complexity.

Workshop Description

In order to address the ambitious research agenda put forward by many science disciplines, many challenges must be addressed in the areas of information sciences, intelligent systems, and human-computer interaction. Data modeling and integration still require large investments of scientist time and effort. The scientific literature grows so quickly in many areas that it becomes unmanageable for scientists. Many aspects of the scientific discovery process are often largely manual and could be automated, improved, or made more efficient. Better interfaces for collaboration, visualization, and understanding would significantly improve scientific practice. At the same time, recent research in information and intelligent systems has opened up new opportunities for scientific discovery. Social computing has been successfully demonstrated as a novel approach to scientific discoveries. Social robotics is an emerging area that presents new opportunities to redesign data collection. The Semantic Web offers radically new paradigms for data publication and sharing.

The goal of this workshop is to investigate the opportunities that scientific discoveries present to information sciences and intelligent systems as a new area of research called discovery informatics.

Possible themes in discovery informatics for discussion at the workshop include:

Efficient experimentation and discovery processes: Scientific discovery processes involve many steps that are often managed or executed manually. While a lot of research has been devoted to managing complex data analysis processes in distributed resources for large-scale execution, many aspects of the management of those processes have been neglected, such as: How can scientists formulate data analysis workflows efficiently? How can systems best support and document the repeated hypothesis-experiment-test cycle that leads to discoveries? What kinds of intelligent reasoning can be brought to bear in the experimentation process? What roles can robots play in experimental processes? What new scientific discoveries could be enabled by robots? How can collaborative and cross-disciplinary process formulation be facilitated? How can discovery processes be shared, reused, and efficiently adapted to new questions? Can scientific processes be easily reproducible, repeatable, and repurposed? Is it possible to set formal guarantees that a dataset has been fully exploited and no opportunities for discoveries remain? Can we quantify the cost of a scientific question in terms of resources to explore the hypothesis space?
Practical issues in learning models from science data: Many discoveries are the result of applying data mining and machine learning techniques to experimental data. In recent years, there has been a lot of research on data-intensive computing and on novel machine learning algorithms. However, many key aspects of learning from data remain largely open research questions, such as: What kind of assistance can be provided to scientists to help them formulate hypotheses and then create experiments to test them with data? How can hypotheses be linked to data needs and therefore be used to control data collection instruments? What are appropriate methodologies to address data cleaning and preparation? What feature selection techniques are appropriate for given kinds of data or for a given question posed by a scientist? How can human ideas and insight be combined with machine learning algorithms? How could a system highlight what is unusual about a dataset that grants further investigation? How can insightful visualizations become commonplace?
Social computing for science: The raise of the social web has uncovered social computing as a completely new approach to discoveries. From protein folding to proving theorems, anyone with a little training can become a participant and even contribute to novel discoveries. This is a very recent area that is still highly exploratory and with many open research questions: What scientific tasks are amenable to social computing approaches? How can tasks be organized piecemeal to enable many contributors to understand what is expected of them? How can different contributor roles be defined and assigned to optimize the formulation of scientific tasks? How can we facilitate the development of reusable software infrastructure for social computing in science? How can we develop social computing approaches that enable K-12 students to take more active roles in scientific discoveries as a novel way to integrate research and education?

These themes, as well as other salient themes that may arise from discussions at the meeting, will be articulated in detail in the final workshop report outlining the challenges and opportunities in discovery informatics.

The workshop will be held in the Gallery III meeting room of the Hilton Arlington in Arlington, VA (one block from NSF).

Slides and notes from the sessions are available under the Documents tab

Thursday February 2, 2012

8:00-8:30 Continental Breakfast
8:30-9:00 Introductions, welcome, meeting plans
9:00-10:30 Plenary session: Themes in Discovery Informatics
10:30-11:00 Break
11:00-12:00 Plenary session: Themes in Discovery Informatics (continued)
12:00-1:00 Working lunch: Planning breakout topics
1:00-3:00 Breakout sessions: Elaborating themes in Discovery Informatics

Topic 1 (Gallery III Room)
Topic 2 (Renoir Room)
Topic 3 (da Vinci Suite)

3:00-3:30 Break
3:30-4:30 Plenary session: Breakout reports on Elaborating themes in Discovery Informatics
4:30-5:30 Breakout sessions: Vision Scenarios for Science Areas

Topic 1 (Gallery III Room)
Topic 2 (Renoir Room)
Topic 3 (da Vinci Suite)

5:30-6:00 Plenary session: Breakout reports on Vision Scenarios for Science Areas

Friday February 3, 2012

8:00-8:30 Continental Breakfast
8:30-9:30 Plenary session: What each of us learned from yesterday
9:30-10:00 Plenary session: Planning breakout groups
10:00-10:30 Break
10:30-12:00 Breakout sessions: Discovery Informatics Challenges in vision scenarios

Topics 1 & 2 (Gallery III Room)
Topic 3 (Renoir Room)
Topic 4 (da Vinci Suite)

12:00-1:00 Working lunch with presentations from breakout groups
1:00-2:00 Workshop report planning
2:00-3:00 Final presentation
3:00-4:00 Q&A with NSF and government attendance
4:00-... All available start to draft final report

Location

The workshop will be held in the Gallery III meeting room of the Hilton Arlington in Arlington, VA (one block from NSF). The address is 950 North Stafford Street, Arlington, VA 22203.

Travel Information

The Hilton Arlington is holding a block of rooms for the workshop until Wednesday January 11, 2012. To reserve one of these rooms, make a reservation requesting the code "UOS", either through the hotel web site or by calling call 1-800-Hiltons.

Submitting Expenses for Reimbursement

Flights must be booked as a round trip from the home city to Washington, be reasonably priced, and have economy fare. If your travel plans involve other stops or have any questions about flight arrangements, please contact the organizers before booking the tickets regarding the requirements for reimbursement in those cases.

Transportation and meals will be reimbursed when receipts are provided and expense is within reason.

Hotel room charges will be paid directly from the workshop account. Room Internet charges and other incidental expenses will not be covered.

Reimbursements must be pre-approved by the organizers. Car rental, Internet and/or others expenses will not be reimbursed.

Expenses submitted after February 15 will not be reimbursed.

The information contained in the reimbursement guide needs to be provided. The guide also specifies detailed requirements for reimbursement.

Organizers

Invited Participants

Cecilia Aragon, University of Washington (interaction and visualization)
Phil Bourne, University of California San Diego (biology, future scientific publications)
Elizabeth Bradley, University of Colorado (qualitative reasoning)
Will Bridewell, Stanford University (machine learning and discovery)
Paolo Ciccarese, Harvard University (ontologies and semantic web)
Susan Davidson, University of Pennsylvania (databases and provenance)
Helena Deus, Digital Enterprise Research Institute (semantic web)
Yolanda Gil, University of Southern California (workflows and semantic web)
Clark Glymour, Carnegie Mellon University (philosophy of science, causality)
Carla Gomes, Cornell University (constraint reasoning and sustainability)
Alexander Gray, Georgia Institute of Technology (data mining and astrophysics)
Haym Hirsh, Rutgers University (social computing)
Larry Hunter, University of Colorado Denver (natural language and biology)
David Jensen, University of Massachusetts Amherst (machine learning)
Kerstin Kleese van Dam, Pacific Northwest National Laboratory (semantic scientific data management)
Vipin Kumar, University of Minnesota (machine learning and climate)
Pat Langley, Arizona State University (computational scientific discovery)
Hod Lipson, Cornell University (robotics)
Huan Liu, Arizona State University (social computing)
Yan Liu, University of Southern California (data mining and biology)
Miriah Meyer, University of Utah (scientific visualization)
Andrey Rzhetsky, University of Chicago (genetics)
Steve Sawyer, Syracuse University (social computing)
Alex Schliep, Rutgers University (bioinformatics)
Christian Schunn, University of buy viagra online Pittsburgh (cognitive science and discovery)
Nigam Shah, Stanford University (ontologies and semantic web)
Karsten Steinhaeuser, University of Minnesota (data mining and climate)
Alex Szalay, The Johns Hopkins University (astrophysics and citizen science)
Loren Terveen, University of Minnesota (interaction and social computing)
Raul E. Valdes-Perez, Vivisimo Inc. (commercialization, knowledge-based discovery)
Evelyne Viegas, Microsoft Research (semantic computing)

Government Observers

Dr. Josh Alspector, IDA
Dr. Mitra Basu, NSF CISE/CCF
Dr. Bonnie Dorr, DARPA
Dr. Le Gruenwald, NSF CISE/IIS
Dr. Vasant Honavar, NSF CISE/IIS
Dr. David Jakubek, OSD
Dr. Jia Li, NSF MPS/DMS
Dr. Mark Luker, NCO NITRD
Dr. Wen Masters, ONR
Dr. Michael Nelson, Georgetown University
Dr. Grace Peng, NIH NIBIB
Dr. Marc Rigas, NSF OD/OCI
Dr. Edwina Rissland, NSF CISE/IIS
Dr. Tom Russell, NSF OD/OIA
Dr. Carey Schwartz, ONR
Dr. Abdul Shaikh, NIH NCI
Dr. Julia Skapik, AAAS Science and Technology Fellow
Dr. George Strawn, NCO NITRD
Dr. Kenneth Whang, NSF CISE/IIS
Dr. Maria Zemankova, NSF CISE/IIS
Dr. Fen Zhao, NSF CISE/CCF

Final Workshop Report

The final workshop report is available: 2012 NSF Discovery Informatics Workshop Report.

A slide presentation is available: 2012 NSF Discovery Informatics Workshop Presentation, given at NSF on June 2012.

Records from the Meeting Sessions

Sessions appear in reverse chronological order

Day 2 Plenary Session: Final Workshop Presentation and Q/A discussion with all attendees
- Slides of the final presentation

Day 2 Plenary Session: Planning the Final Workshop Report
- Outline of the report and writing assignments

Day 2 Breakout Group Reports on Vision Scenarios in Science Disciplines and Discovery Informatics Challenges

Day 2 Plenary session: All participants share what they learned from this workshop so far

Day 1 Breakout Group Reports on Three Major Themes in Discovery Informatics

Day 1 Plenary Session on Themes in Discovery Informatics

Day 1 Introduction
- Introduction from chairs
- Introduction from Vasant Honavar, NSF/IIS

Participant Backgrounds and Contributions

Participants contributed materials prior to the workshop, such as current interests, position papers, and relevant publications:

Reports from Previous Workshops

Several workshops have been organized http://varley.net/online/ in recent years on topics relevant to the proposed workshop, although the topic of discovery informatics is itself new. A series of workshops on the topic of Cyber-enabled Discovery and Innovation have been held in recent years, including the NSF Symposium on Cyber-Enabled Discovery and Innovation, held in September 2007, the NSF workshop on data mining and cyber-enabled discovery for innovation, held in October 2007, and the SC07 session on supercomputing and CDI, held in November 2007.

Related workshops on creativity and scientific discovery include NSF Symposium on Computational Approaches to Creativity in Science, held in March 2008, the NSF Workshop on Knowledge Management and Visualization Tools in Support of Discovery, held in March 2008, the NSF Innovation and Discovery Workshop: The Scientific Basis of Individual and Team Innovation and Discovery, held in August 2006.

Many workshops have been held concerning scientific challenges for cyberinfrastructure, including the NSF Workshop on Cyberinfrastructure for the Atmospheric Sciences in the 21st Century, held in June 2004, and the NSF SBE-CISE Workshop on Cyberinfrastructure and the Social Sciences, held in March 2005.

Sponsorship

This workshop is sponsored by the Division of Information and Intelligent Systems of the Directorate for Computer and Information Sciences at the National Science Foundation under grant number IIS-1151951.