UCLA Department of Statistics Speakers Series
| 3 - 4 pm, Tuesday, May 6, 2008 |
| 6627 Math Sciences Bldg. |
Zhangzhang SiStatistics UCLA |
Active Basis for Modeling, Learning and Recognizing Deformable Templates |
We propose an active basis model, a shared sketch algorithm, and a computational architecture of sum-max maps for representing, learning, and recognizing deformable templates. In our generative model, a deformable template is in the form of an active basis, which consists of a small number of Gabor wavelet elements at selected locations and orientations. These elements are allowed to slightly perturb their locations and orientations before they are linearly combined to generate the observed image. The active basis model, in particular, the locations and the orientations of the basis elements, can be learned from training images by the shared sketch algorithm. The algorithm selects the elements of the active basis sequentially from a dictionary of Gabor wavelets at a dense collection of locations and orientations. When an element is selected at each step, the element is shared by all the training images, and the element is perturbed to encode or sketch a nearby edge segment in each training image. The recognition of the deformable template from an image can be accomplished by a computational architecture that alternates the sum maps and the max maps. The computation of the max maps deforms the active basis to match the image data, and the computation of the sum maps scores the template matching by the log-likelihood of the deformed active basis. Joint work with Ying Nian Wu, Haifeng Gong, and Song-Chun Zhu. |
UCLA Department of Statistics Speakers Series
| 4 - 5 pm, Thursday, May 1, 2008 |
| 5137 Math Sciences Bldg. |
Dennis PearlDept. of Statistics Ohio State University |
Cooking for the Buffet: Individualizing Course Content to Improve Learning |
Education reforms in the last fifteen years have enlivened the teaching of introductory statistics with fewer lectures and more active learning, fewer recipes and more conceptual thinking, fewer contrivances and more real data. However, bringing these reforms to large multi-section introductory courses has been a difficult challenge. Thus, the buffet model was developed at The Ohio State University to use class size as a strength rather than a weakness, to optimize learning for the individual rather than norming for the group, and to integrate technology as an efficient tool rather than an expensive add-on. Students learn in different ways so, in the buffet model, different course sections are geared toward different learning styles and students are offered a choice of interchangeable paths to learn the same course objectives. In order to promote student commitment to follow through on their choices and to enable efficient tracking of each student's progress through the course, the choice of learning modes is exercised through an on-line "contract" entered into by students at the beginning of the quarter. Students can make an informed choice based on the results of their own learning styles inventory and by reading testimony from previous students most like themselves. The buffet structure has been successful in increasing both student satisfaction and student learning. For example, scores on common exams have increased by about a half-letter grade while dropouts and students needing to retake this required course have decreased by about 40%. Finally, key elements of the buffet strategy can also be adapted to smaller classes to improve student learning. |
Nicolas Christou Receives 2008 Copenhaver Award for Innovation in Teaching with Technology |
Dr. Nicolas Christou has been honored as one of 3 recipients of the prestigious Brian P. Copenhaver Award for Innovation in Teaching with Technology for 2008. Nicolas' award is in recognition of his development of the suite of interactive learning materials and simulation tools available through the Statistics Online Computational Resource and used by students and researchers at UCLA and around the globe[1]. More specifically, his recognition is for[2]: • His contributions to the Statistics EBook • His lead in building SOCR curriculum resources and classroom integration of SOCR materials in many lower and upper division classes • His development of novel interactive activities included in the SOCR Experiments and SOCR Analyses • His developments of dozens of learning modules (e.g., Relations between distributions) Recipients of the Award were selected by the Faculty Committee on Educational Technology and prior award recipients. Award recipients and nominees will be recognized at a reception May 19, 2008 at the Faculty Center. To promote the open exchange of ideas in the use of technology in teaching, the Office of Instructional Development will, as in previous years, be interviewing all nominees for the Copenhaver Award. The interviews will be put on line as they become available.[1] [1] http://www.oid.ucla.edu/edtech/bpcaward/bpca2008[2] http://wiki.stat.ucla.edu/socr/index.php/SOCR_Awards_Christou_BC2008 |
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Ariana Anderson: Collegium of University Teaching Fellow |
Ariana Anderson has been awarded a teaching fellowship by the Collegium of University Teaching Fellows committee. "The CUTF is an innovative program that creates unique learning opportunities for both graduate teaching fellows and undergraduate students on campus. Through the program, some of UCLA's very best advanced graduate students have the opportunity to develop and teach a lower division seminar in their field of specialization on a one-time only basis. This experience serves as a "capstone" to the teaching apprenticeship, preparing them for the academic job market and their role as future faculty. At the same time, undergraduates enrolled in CUTF seminars have the chance to take courses that are at the cutting edge of a discipline, and to experience the benefits of participating in a small-seminar environment."[1] Ariana's accepted proposal is entitled "Pseudo-Science: Rational thought Processes". Ariana explains: "In this class, we will learn how to objectively read writings and judge common, yet often controversial, claims in American culture. Sample topics covered will be the relationship between autism and vaccinations, global warming and human behavior, holistic medicine, and the safety of genetically modified foods. Students will learn how to locate scientific sources to validate claims, and how to objectively interpret statements in the light of data and logic. The last portion of the course will focus on the neurophysiological and logical implications of belief, discovering how the brain evaluates and invalidates arguments. The beliefs held in the natural world of science will be contrasted to the beliefs held in our personal life, illuminating how we develop our convictions on both. By evaluating polarizing topics through the lens of statistics and logic, we will learn how faith and beliefs bias not only other people's conclusions, but also our own." The course is a 5 unit course, will meet two days a week, and will be conducted in the Winter/Spring 2008 term. Ariana is a third year in the Statistics department and has a strong interest in statistical applications in Psychiatry. [1] From Overview at http://www.oid.ucla.edu/Cutf |
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2008 Seminar on Teaching Statistics Series |
| Presented by the
UCLA Center for Teaching Statistics and the Statistics Online Computational Resource (SOCR)
Days and Time: Thursday, 4-5pm
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Position Available for Professor of Statistics in the Department of Statistics, UCLA
Status: Open
Applications and nominations are invited for the position of Professor of Statistics, any level (tenure-track Assistant Professor, tenured Associate Professor or tenured Full Professor), in the Department of Statistics at the University of California, Los Angeles.
The position targets candidates with high quality research, a strong teaching record, and with expertise preferably in one or more of the following areas: Environmental Statistics, Social Statistics, and Spatial Statistics. Qualified candidates must have a Ph.D. in Statistics or Biostatistics. The position is effective July 1, 2009.
Reviews for the position begin May 1, 2008, and will continue until the position is filled. Interested applicants should send a letter describing how their qualifications and interests would fit with the position description, along with their curriculum vitae, to:
Professor Jan de LeeuwDepartment of Statistics
University of California at Los Angeles
8125 Math Sciences Building
Box 951554
Los Angeles, CA 90095-1554
The applicants should arrange for three letters of recommendation to be sent to Professor de Leeuw. Until the file is complete with the requested information, the application cannot be given full consideration.
The University of California Los Angeles and the Department of Statistics are interested in candidates who are committed to the highest standards of scholarship and professional activities, and to the development of a campus climate that supports equality and diversity.
The University of California is an Affirmative Action/Equal Opportunity Employer.
Contact: Jan de Leeuw
Posted: 2008-03-19, 08:21:00 by Jose Hales-Garcia
Workshop: Integrating Computing into the Statistics Curricula |
Date: Sunday, July 13 to Thursday, July 17, 2008 Location: U.C. Berkeley Computing is an increasingly important element of statistical research and practice. It is an essential tool in our daily work, it shapes the way we think about statistics, and broadens our concept of statistical science. Although many agree that there should be more computing in the statistics curriculum and that statistics students need to be more computationally capable and literate, it can be difficult to determine how the curriculum should change because computing has many dimensions. The aim of the workshop is to bring together faculty for a hands-on program where we cover material and work on actual labs/projects/exercises that would make up courses in modern statistical computing and data technologies. The goal is to assist faculty in changing their statistics programs in innovative ways to incorporate more computing in existing courses and to add new courses to the curriculum. More information + registration: http://www.stat.berkeley.edu/users/statcur/Workshop2/index.html |
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Registration Deadline for the CensusAtSchool International Workshop |
Registration Deadline for the CensusAtSchool International Workshop for k-12 Teachers closes on May 30th. Entries for the poster competition during the workshop also closes on May 30th. Please, visit the the workshop's website to download the registration form http://censusatschool-california.stat.ucla.edu/ or contact the conference organizer, Juana Sanchez, (jsanchez@stat.ucla.edu) |
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2nd CensusAtSchool International Workshop, July 28-29, 2008 |
2nd CensusAtSchool International Workshop to be held at UCLA, Los Angeles, July 28-29, 2008 The International CensusAtSchool Project, http://www.censusatschool.ntu.ac.uk/ and http://www.stat.auckland.ac.nz/~iase/islp/census is one of the most comprehensive teacher training program and one of the most complete set of prepared resources for K-12 teachers interested increasing the data handling and chance skills of their students in compliance with GAISE and NCTM standards. The C@S teacher training resources and student activities are all built around the classroom data and the international data set resulting from more than 5 years of surveying children in their classrooms across 5 countries in the world. Every year or every two years, Australia, New Zealand, Canada and the UK, allow thousands of teachers to register online their students to participate on an online survey that asks fun questions. South Africa did this in 2001, too. The data collected from each classroom is immediately sent to the teachers for use in lessons previously prepared by the C@S leaders, and at the same time is compiled by the Royal Statistical Society in an International Data Base in the UK. Anybody in the world can access this database and select a random sample of up to 500 students from these countries, over several years and across 5 countries. C@S resources not only engage students in learning data handling and chance but also provides them with an international experience where they can compare themselves with students from 5 countries in the world. In addition to that, the leaders of this project train the teachers beforehand on how to make the most out of these resources. All of this is done free to the teachers and the students. The questionnaires, the activities and data sets are free to anyone. Please visit the event's web site (http://censusatschool-california.stat.ucla.edu) for more information. The event is organized by Juana Sanchez. |
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SOCR NSF Funding Extended Through 2011 |
On September 24, 2007, the National Science Foundation (NSF) extended the funding for the SOCR project for another 4 years(1). This is a great accomplishment for the SOCR team and recognition of the SOCR Resource achievements since 2002. In the next 4 years, SOCR will design, test, validate, and disseminate:
For more information about the SOCR project, visit the SOCR website at: http://socr.ucla.edu The National Science Foundation (NSF) is an independent federal agency created by Congress in 1950 "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense...". With an annual budget of about $5.92 billion, we are the funding source for approximately 20 percent of all federally supported basic research conducted by America's colleges and universities. In many fields such as mathematics, computer science and the social sciences, NSF is the major source of federal backing. (1) Grant number 0716055 |
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UCLA Department of Statistics Speakers Series
| 3 - 4 pm, Tuesday, April 29, 2008 |
| 6627 Math Sciences Bldg. |
Eugene BartCaltech |
Unsupervised learning of visual taxonomies |
| I will describe a method for organizing multiple visual categories into a taxonomy. Such organization becomes crucial as the number of available categories increases. The proposed method extends current non-parametric Bayesian techniques such as Nested Chinese Restaurant Process (NCRP). The method is completely unsupervised. It discovers commonalities among images and exploits these commonalities to represent images compactly in a hierarchical manner. Visual categories emerge and become organized in a taxonomy automatically during this process. |
UCLA Department of Statistics Speakers Series
| 4 - 5 pm, Thursday, April 24, 2008 |
| 5137 Math Sciences Bldg. |
Andee RubinTERC |
Software as a Learning Context: the Case of TinkerPlots and Statistical Reasoning |
Sophisticated statistical tools have made data analysis accessible to an increasingly wide variety of people, from scientists to students, demographers to historians. Yet, statistics is still generally regarded as a difficult — if not impossible — topic to understand. How can the powerful technology used for statistical analysis be harnessed to support students' understanding of statistical concepts? At least two projects in the last decade have taken on this challenge and designed educational environments that are both a tool that can carry out statistical analyses and a tool box with which budding analysts can try out and compare a variety of approaches to a statistical situation. I will discuss one of these, TinkerPlots, a statistics education tool that can be used as early as middle school and at least through high school. While I will describe the educational model that TinkerPlots is based on and demonstrate some of its features, I will focus in particular on the ways in which the software acts as a learning context and share several examples of students and teachers exploring statistical concepts using the TinkerPlots tool box. Bio |
Profile
Laboratory of Statistical Genomics Sequences of entire genomes, genotypes of individual variations in thousands of polymorphic loci and hundreds of individuals, gene expression measurements via cDNA chips on thousands of genes in a variety of conditions: these are some of the types of datasets which are now available to genetic researchers.... The statistical genetics laboratory uses tools from information theory, Bayesian statistics, Markov chain Monte Carlo to identify in these massive datasets scientifically valuable information. [read more]
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