•
Tuesday, January 29th
12:00 – 1:00pm
105 Annenberg
*Lunch will be provided*
SPEAKER:
Alexandre Cunha
Center for Advanced Computing Research and Elliot Meyerowitz Lab, Caltech
TITLE:
Collaborative Image Analysis with the Masses: Challenges and Opportunities
ABSTRACT:
Extracting reliable quantitative information from digital images in an automatic fashion continues to be a difficult task. In many situations classical and contemporary algorithms only provide partial and sub-optimal results that might not be sufficient to carry on research studies thus leading practitioners to rely on manual annotations. We present our work on collaborative image segmentation, an online crowdsourcing system where computers, experts, and non-experts cooperate to produce robust results supporting the research of plant biologists. We address some of the technical and nontechnical challenges in building such a system and discuss the potential in employing the vision of crowds to help solve image processing problems which are still poorly solved by computers alone.
This is a work in progress in collaboration with Elliot Meyerowitz lab at Caltech and with Tsang Ing Ren lab at UFPE, Brazil.
•
Tuesday, May 8th
12:00 – 1:00pm
105 Annenberg
*Lunch will be provided*
SPEAKER:
Matthew Graham
Computational Scientist
Center for Advanced Computing Research, Caltech
TITLE:
Characterizing the Time Domain
ABSTRACT:
The new generation of synoptic sky surveys promise unprecedented amounts of data and information and automated processing and analysis is a necessity. Light curves, however, can show tremendous variation in their temporal coverage, sampling rates, errors and missing values, etc., which makes comparisons between them difficult and training classifiers even harder. A common approach to tackling this is to characterize a set of light curves via a set of common features and then use this alternate homogeneous representation as the basis for further analysis or training. Many different types of feature are used in the literature to capture information contained in the light curve: moments, flux and shape ratios, variability indices, periodicity measures, model representations. In this talk, we will review characterization features with particular attention to the problem of determining accurate and reliable periods for astrophysical objects.
•
“Toward the ‘grand unified theory’ of user interface”
Jiao Lin
Computational Scientist, Center for Advanced Computing Research (CACR)
Tuesday Sept 20
11AM
100 Powell-Booth
Abstract: Intuitive, responsive, and clean graphical user interface has become more and more important for scientific software applications. Building graphical user interface is tedious, however. Without extreme care, a user interface application can easily become unnecessarily complex and convoluted, and as a result, unmaintainable. Building web-based graphical user interface is harder due to inconsistent implementations of languages among browsers and multiple languages/standards/platforms that could be involved, and that renders management of a web UI project expensive, and sometimes chaotic. With the emergence of cloud computing, we will see many scientific computing packages turning to cloud and demand web or mobile-device user interfaces, while the traditional desktop user interface still has its large user base. A much simplified route of developing desktop/web/mobile-device user interface is needed. This work looks for the most compact set of abstract concepts and principles enough for constructing sophisticated UI. In practical, it intends to reduce the chaos and agony in building user interface applications, to dramatically lower the barrier of creating good user interfaces, and to make it much easier to maintain and evolve them.
•
Co-Sponsored by the Caltech Biological Network Modeling Center (BNMC)
Gustavo Kunde Rohde, Assistant Professor, Biomedical Engineering, Carnegie Mellon University
Monday, May 16, 2011
2:00 PM – 3:00 PM
Beckman Institute Auditorium
Novel biomedical imaging techniques have enabled the acquisition of quantitative information from cells, tissues, and organs with unprecedented accuracy and specificity. Combined with the availability of vast computational resources, quantitative biomedical imaging pipelines have the potential to accelerate scientific discovery and improve clinical practice. An important engineering problem in this area relates to extracting quantitative information related to the form (shape and texture) of cells, tissues, and organs. I will describe our recent efforts toward the development of a general purpose segmentation method and present preliminary evidence that a tool capable of high-enough accuracy for quantitative imaging pipelines may one day be available. In addition, recent efforts in developing geometric data analysis tools for mining morphological information from biomedical image data will be described. In particular, I will describe the application of deformation and transportation related metrics, in combination with discriminant analysis techniques, towards understanding the distribution of cellular patterns in cancerous and normal tissues.
•
Anthony Goldbloom
Kaggle (http://www.kaggle.com/)
Thursday January 6, 2011
2:30 PM
100 Powell-Booth
Abstract
Machine learning and data prediction is crucial to most organizations. Banks predict which loan applicants are likely to default, treasuries forecast tax revenues and medical researchers predict the likelihood of illness from gene sequences.
Crowdsourced data mining can lead to vastly better models. My project, Kaggle, recently hosted a bioinformatics contest, which required participants to pick markers in a series of genetic sequences that predict the progression of HIV. Within a week and a half, the best submission had already outdone the best methods in the scientific literature.
This result neatly illustrates the strength of competitions. Whereas the scientific literature or in-house models tend to evolve slowly (somebody tries something, somebody else tweaks that approach and so on), a competition inspires rapid innovation by introducing the problem to a wide audience. There are an infinite number of approaches that can be applied to any machine learning problem and it is impossible to know at the outset which technique will be most effective.
Bio
Anthony is the Founder and CEO of Kaggle, a global platform for data prediction competitions. In addition to founding Kaggle, Anthony continues to consult to hosts of Kaggle competitions to help them frame prediction tasks, to get the best out of the new platform and help them integrate insights into their day-to-day operations.
Before Kaggle, Anthony was a macroeconomic modeler for the Reserve Bank of Australia and before that the Australian Treasury. In these roles, Anthony built and maintained macroeconomic models of Australia’s economy to improve forecasting and model the economic effect of changes in policy parameters, such as interest rates and fiscal policy.
Anthony graduated with first class honours in econometrics at the University of Melbourne and has published in The Economist magazine and the Australian Economic Review.
