Proxemic Interactions: the New Ubicomp?

Tuesday, September 20, 2011

Abstract:
In the everyday world, much of what we do as social beings is dictated by how we interpret spatial relationships. This is called proxemics. What is surprising is how little people's expectations of spatial relationships are used in interaction design, i.e., in terms of mediating people's interactions with surrounding digital devices such as digital surfaces, mobile phones, tablets, and computers. Our interest is in proxemic interaction, which imagines a world of devices that have fine-grained knowledge of nearby people and other devices - how they move into range, their precise distance, their identity and even their orientation - and how such knowledge can be exploited to design interaction techniques. Just as people expect increasing engagement and intimacy as they approach others, so should they naturally expect increasing connectivity and interaction possibilities as they bring themselves and their devices in close proximity to one another and to other things in their everyday ecology.

This presentation will be accessible to all computer scientists and even to the lay public. I will describe and illustrates (through videos) work in progress rather than mature work. Thus I particularly welcome discussion, feedback, and critique from the community.

Bio:
Saul Greenberg is a Full Professor in the Department of Computer Science at the University of Calgary. While he is a computer scientist by training, the work by Saul and his talented students typifies the cross-discipline aspects of Human Computer Interaction, Computer Supported Cooperative Work, and Ubiquitous Computing. He and his crew are well known for their development of:

• toolkits enabling rapid prototyping of groupware and ubiquitous appliances;

• innovative and seminal system designs based on observations of social phenomenon;

• articulation of design-oriented social science theories, and

• refinement of evaluation methods.

His research is well-recognized. He holds the iCORE/NSERC/Smart Technologies Industrial Chair in Interactive Technologies. He also holds a University Professorship, which is a distinguished University of Calgary award recognizing research excellence. He received the CHCCS Achievement award in May 2007 and was elected to the ACM CHI Academy in April 2005 for his overall contributions to the field of Human Computer Interaction.

Bringing Web Databases to the Masses

Tuesday, October 11, 2011

Abstract:
The World-Wide Web contains vast quantities of structured data on a variety of domains, such as hobbies, products and reference data. Moreover, the Web provides a platform that can encourage publishing more data sets from governments and other public organizations and support new data management opportunities, such as effective crisis response, data journalism and crowd-sourcing data sets. To enable such wide-spread dissemination and use of structured data on the Web, we need to create a ecosystem that makes it easier for users to discover, manage, visualize and publish structured data on the Web.

I will describe some of the efforts we are conducting at Google towards this goal and the technical challenges they raise. In particular, I will describe Google Fusion Tables, a service that makes it easy for users to contribute data and visualizations to the Web, and the WebTables Project that attempts to discover high-quality tables on the Web and provide effective search over the resulting collection of 200 million tables.

Bio:
Alon Halevy’s team develops techniques for enabling a broad class of users to create, visualize, publish, and discover structured data on the web. Prior to joining Google, Dr. Halevy was a professor of Computer Science at the University of Washington. Dr. Halevy is a Fellow of the Association for Computing Machinery, received the Presidential Early Career Award for scientists and engineers in 2000, and was a recipient of the Alfred P. Sloan Fellowship in 1999.

Computational Insights and the Theory of Evolution

Tuesday, November 15, 2011

Abstract:
I shall discuss recent work (much of it joint with biologists Adi Livnat and Marcus Feldman) on some central problems in Evolution that was inspired and informed by computational ideas. Considerations about the performance of genetic algorithms led to a novel theory on the role of sex in Evolution based on the concept of mixability. And a natural random process on Boolean functions can help us understand better Waddington's genetic assimilation phenomenon, in which an acquired trait becomes genetic.

Bio:
Christos H. Papadimitriou is C. Lester Hogan Professor of Computer Science at UC Berkeley. Before joining Berkeley in 1996 he taught at Harvard, MIT, Athens Polytechnic, Stanford, and UCSD. He has written five textbooks and many articles on algorithms and complexity, and their applications to optimization, databases, AI, economics, and the Internet. He holds a PhD from Princeton, and honorary doctorates from ETH (Zurich), Athens Polytechnic, and the Universities of Macedonia, Athens, Cyprus, and Patras. He is a member of the Academy of Sciences of the US, the American Academy of Arts and Sciences, and the National Academy of Engineering, and a fellow of the ACM. His novel “Turing (a novel about computation),” was published by MIT Press in 2003, and his graphic novel "Logicomix" (with Apostolos Doxiadis) was translated in more than 20 languages.

Computational Thinking

Tuesday, February 7, 2012

Abstract:
My vision for the 21st Century: Computational thinking will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Bio:
Jeannette M. Wing is the President's Professor of Computer Science and Head of the Computer Science Department at Carnegie Mellon University. She received her S.B., S.M., and Ph.D. degrees from the Massachusetts Institute of Technology. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation.

Professor Wing's general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are on the foundations of trustworthy computing, with a focus on the science of security and privacy.

Professor Wing is on the editorial board of twelve journals. She is a member of Computing Research Association Board and the Microsoft Trustworthy Computing Academic Advisory Board. She has been a member of many other advisory boards, including: the Networking and Information Technology (NITRD) Technical Advisory Group to the President's Council of Advisors on Science and Technology (PCAST), the National Academies of Sciences' Computer Science and Telecommunications Board, ACM Council, the DARPA Information Science and Technology (ISAT) Board, NSF's CISE Advisory Committee, the Intel Research Pittsburgh's Advisory Board, and the Sloan Research Fellowships Program Committee. She served as co-chair of NITRD from 2007-2010. She is a member of Sigma Xi, Phi Beta Kappa, Tau Beta Pi, and Eta Kappa Nu. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).

Machine Learning and AI via Large-Scale Brain Simulation

Tuesday, February 28, 2012

Abstract:
By building large-scale simulations of cortical (brain) computations, can we enable revolutionary progress in AI and machine learning? Machine learning often works very well, but can be a lot of work to apply because it requires spending a long time engineering the input representation (or "features") for each specific problem. This is true for machine learning applications in vision, audio, text/NLP and other problems. To address this, researchers have recently developed "unsupervised feature learning "and "deep learning" algorithms that can automatically learn feature representations from unlabeled data, thus bypassing much of this time-consuming engineering. Many of these algorithms are developed using simple simulations of cortical (brain) computations, and build on such ideas as sparse coding and deep belief networks. By doing so, they exploit large amounts of unlabeled data (which is cheap and easy to obtain) to learn a good feature representation. These methods have also surpassed the previous state-of-the-art on a number of problems in vision, audio, and text. In this talk, I describe some of the key ideas behind unsupervised feature learning and deep learning, and present a few algorithms. I also speculate on how large-scale brain simulations may enable us to make significant progress in machine learning and AI, especially perception. This talk will be broadly accessible, and will not assume a machine learning background.

Bio:
Andrew Ng received his PhD from Berkeley, and is now an Associate Professor of Computer Science at Stanford University, where he works on machine learning and AI. He is also Director of the Stanford AI Lab, which is home to about 12 professors and 150 PhD students and post docs. His previous work includes autonomous helicopters, the Stanford AI Robot (STAIR) project, and ROS (probably the most widely used open-source robotics software platform today). He current work focuses on neuroscience-informed deep learning and unsupervised feature learning algorithms. His group has won best paper/best student paper awards at ICML, ACL, CEAS, 3DRR. He is a recipient of the Alfred P. Sloan Fellowship, and the 2009 IJCAI Computers and Thought award. He also works on free online education, and recently taught a machine learning class http://ml-class.org to over 100,000 students.

Bonus Lecture — Teaching machine learning to 100,000

Last year, Stanford University offered three online courses, which anyone in the world could enroll in and take for free. Students were expected to submit homeworks, meet deadlines, and were awarded a "Statement of Accomplishment" only if they met our high grading bar. Offered this way, my machine learning class had over 100,000 enrolled students. To put this number in context, in order to reach an audience of this size, I would have
had to teach my normal Stanford class (enrollment of ~400) for 250 years.

In this talk, I'll report on the outcome of this bold experiment in distributed education. I'll also describe my experience teaching one of these classes, and leading (together with Daphne Koller) the development of the platform used to teach two of the classes. I'll describe the key technology and pedagogy ideas used to offer these courses, ranging from easy-to-create video chunks, to a scalable online Q&A forum where students can get their questions answered quickly, to sophisticated autograded homeworks. Importantly, using a "flipped classroom" model, we also used these resources to improve the education of the enrolled, on-campus, Stanford students as well.

Whereas technology and automation have made almost all segments of our economy---such as agriculture, energy, manufacturing, transportation---vastly more efficient, education today isn't much different than it was 300 years ago. Given also the rising costs of higher education, the hyper-competitive nature of college admissions, and the lack
of access to a high quality education, I think there is a huge opportunity to use modern internet and AI technology to inexpensively offer a high quality education online. Through such technology, we envision millions of people gaining access to the world-leading education that has so far been available only to a tiny few, and using this education to improve theirlives, the lives of their families, and the communities they live in. Following the success of the first set of courses, there are now 14 planned courses for Winter quarter (offered by instructors from U. Michigan, UC Berkeley, and Stanford), and we hope to grow this effort further over time.

Verification and Control of Hybrid Systems using Reachability Analysis with Machine Learning

Tuesday, March 6, 2012

Abstract:
This talk will present reachability analysis as a tool for model checking and controller synthesis for dynamic systems. We will consider the problem of guaranteeing reachability to a given desired subset of the state space while satisfying a safety property defined in terms of state constraints. We allow for nonlinear and hybrid dynamics, and possibly nonconvex state constraints. We use these results to synthesize controllers that ensure safety and reachability properties under bounded model disturbances that vary continuously. The resulting control policy is a set-valued feedback map involving both a selection of continuous inputs and discrete switching commands as a function of system state. We show that new control policies based on machine learning are included in this map, resulting in high performance with guarantees of safety. We discuss real-time implementations of this, and present several examples from multiple aerial vehicle control, human-robot interaction, and multiple player games.

Bio:
Claire Tomlin’s research is in the area of hybrid systems and control, with applications to air traffic systems, robotics, and biology. she has been honoured with the Erlander Professorship of the Swedish Research Council in 2009, a MacArthur Fellowship in 2006, and the Eckman Award of the American Automatic Control Council in 2003. Prof. Tomlin previously held the positions of Assistant, Associate, and Full Professor at Stanford University from 1998-2007, joining Berkeley in 2005.