Abstract

No.2:  "Protein Structure Modeling"

This tutorial will be an overview of the different computational methodologies currently available and in development for modeling and predicting three dimensional protein structures (i.e. known as "the protein folding problem"). Our discussion will cover comparative(homology) modeling methods, threading and ab initio methods and how to evaluate the quality and validity of the predicted protein model structure. Automated web server methods will be discussed as well as options available in the public domain software arena and commercial modeling software tools and their relative performance at recent CASP(Critical Assessment of Techniques for Protein Structure Prediction) competitions. We will briefly touch on the use of protein structural models for computational docking studies and for predicting protein-protein and protein-ligand interactions.

No.3: "Elements of DNA Computing"

DNA is an excellent molecular platform for information process. This tutorial will introduce its basic knowledge and techniques, including DNA biochemistry, DNA self-assembly, and DNA nanomachines. It is the collection of those techniques that make the rapid evolution of DNA computing possible. Note that this tutorial (#3) is closely related to tutorial #4 (DNA Computer Design).

A short CV of the presenter:

Dwyer received his B.S. in computer engineering from the Pennsylvania State University in 1998, and his M.S. and Ph.D. in computer science from the University of North Carolina at Chapel Hill in 2000 and 2003, respectively. He worked in the Department of Physics & Astronomy at UNC as a Postdoctoral Fellow and at the Department of Computer Science at Duke as a Visiting Assistant Professor from 2003-2004. His research interests include Self-assembling Computer Architecture, Nanoscale System Design & Simulation, Circuit Design and Visualization, Self-assembling Device Fabrication, and DNA-guided Self-assembly

No.5: "Dynamic Logic and Neurodynamics of Consciousness"

A ubiquitous property of neural dynamics of consciousness is evolution from vague, fuzzy and less conscious states to more concrete and conscious states. The talk will compare a dynamic logic description of this process with chaotic neurodynamics observed in EEG data, where high-dimensional chaotic states transition into low-dimensional, "less" chaotic states. Cognitive functions of concepts, emotions, instincts, imaginations, intuitions are mathematically described. These are inseparable from perception and cognition. I briefly discuss engineering applications (data mining, fusion, financial predictions, Internet search engines); and present results demonstrating orders of magnitude improvement in classical detection and tracking in noise. 

The last part of the talk moves to future research directions: roles of the beautiful, music, sublime in the mind, cognition, consciousness, and evolution of cultures. The current "East vs. West" confrontation turns out related to differences in grammar between English and Arabic. Presented mathematical theory is related to the knowledge instinct, which drives the mind to understand the world. This instinct is even more important than sex or food. Computers are connected to the mind, the high to the mundane. 

Bio. Dr. Leonid Perlovsky, Visiting Scholar at Harvard University, Principal Research Physicist and Technical Advisor at the Air Force Research Lab. He leads Semantic Web and other research projects. From 1985 to 1999, as Chief Scientist at Nichols Research, a $0.5 B high-tech organization, he led the corporate research in intelligent systems, neural networks, and sensor fusion. He served as professor at Novosibirsk and New York Universities; participated as a principal in startups developing tools for text understanding, biotechnology, and financial predictions. His company predicted the market crash following 9/11 a week before the event, detecting activities of Al Qaeda traders, and later helped SEC looking for these guys. He delivered invited keynote plenary talks and tutorial lectures worldwide, published more then 250 papers, 7 book chapters, and authored a monograph Ħ°Neural Networks and Intellect,Ħħ Oxford University Press, 2001 (currently in the 3rd printing). Three books will be published in 2007 "Neurodynamics of High Cognitive Behavior," "Sapient Systems," and "The Knowledge Instinct." Dr. Perlovsky serves as Associate Editor for "IEEE Transactions on Neural Networks," Editor-at-Large for "Natural Computations," and Editor-in-Chief for "Physics of Life Reviews." He organizes conferences on Computational Intelligence, Chairs IEEE Boston Computational Intelligence Chapter, received the IEEE Boston Section Distinguished Member Award 2005, the AFRL Charles Ryan Award for basic research 2007, International Neural Network Society Gabor Award 2007 for achievements in engneering/applications.  

No.7: "Introduction to Pattern Recognition"

Pattern recognition can be applied to many practical situations and has become a hot subject in the past few years. This tutorial covers the basic techniques in programming the computer to recognize different types of patterns, involving pre-processing, feature extraction, recognition algorithms and classification schemes. Various applications will be illustrated, e.g. recognition of faces, fingerprints, iris, handwriting, speech, and miscellaneous natural and artificial objects and signals. Comparison will be drawn between computer and human recognition systems.

No.8: "Scenario Maps for Chance Discovery: Tools for Data-based Decision"

A chance is defined as an event, which might be rare and uncertain but is significant for the decision making of human. Discovering a chance has been required for the survival of mankind in the dynamic natural and the social environments. Since we initiated projects and workshops on "chance discovery" in 2000, we applied simple ideas and basic tools for solving problems to detecting significant signs of deseases, signs of earthquakes, and also to aiding business people in finding hints of their creative marketing and designing.

In this talk, the basic concepts in chance discovery, and methods and tools developed so far are reviewed. The human's process of chance discovery is compared with the established process of data mining. Based on this introduction, "scenario map," the visual tool for chance discovery is introduced. It will be shown how scenario maps support user's scientific discoveries and creative decisions. 

No.9: "How to build a Google News Service"

In this tutorial we will design and build a Google-like news service. Google News gathers stories from more than 4,500 English-language news sources worldwide, and automatically arranges them to present the most relevant news first. Google News is an unusual news service in the sense that the results are compiled solely by computer algorithms. Google News is highly customizable. You can get recommended headlines based on what news stories you've searched for and clicked on in the past. You can also obtain country-specific and source-specific searching.

How does it all work? What are the issues that one faces when dealing with real world data? What factors matter the most for delivering high quality results? As we will see in this tutorial, a great number of methods are employed for building such a news service. You will learn how to:

ĦE Cleanse and normalize data;
ĦE Deal with multi-lingual free text;
ĦE Build semantic ontologies;
ĦE Classify data with respect to your ontology;
ĦE Cluster your data;
ĦE Validate your results and assess the quality of your system.

Most of the techniques that we present are well known but some are original. Participants are encouraged to bring their laptop computers, Java source code will be provided along with the training material.

A short CV of the presenter:

Chengde Mao is an assistant professor in the Department of Chemistry at Purdue University. He received his B.S. in Chemistry in 1986 from Beijing University (Beijing, China) and Ph.D. in Chemistry in 1999 from New York University (New York, NY). After a postdoctoral training at Harvard University, he joined Purdue University in 2002. He is currently developing DNA nanostructures and exploring their applications in computation, biochemical research, and nanodevices.

Biography

Yukio Ohsawa is an associate professor in the School of Engineering, The University of Tokyo. He received Ph.D in Communication and Information Engineering from The University of Tokyo. He worked also for School of Engineering Science in Osaka University (research associate, 1995-1999), Graduate School of Business Sciences in University of Tsukuba (associate professor, 1999-2005), and Japan Science and Technology Corporation (JST researcher, 2000-2003). He initiated the research area of Chance Discovery and series of international meetings (conference sessions and workshops), e.g., the fall symposium of the American Association of Artificial Intelligence (2001). He edited the first book on Chance Discovery published by Springer Verlag and special issues in international and Japanese (domestic) journals. Chance discovery is growing: Journal issues has been published from the international journals, e.g., Journal of Contingencies and Crisis Management (2001), New Generation Computing (2003), New Mathematics and Natural Computing (2005), and from Jounal on Soft Computing in conjunction with the special issue on Web Intelligence (2006), etc, and new books are appearing. He is in the editorial boads and planning boards of academic journals, is co-chair of international conferences such as IEA/AIE2007, and is the chair of IEEE-SMC Technical Committee of Information Systems for Design & Marketing.