September 11, 2014
Title: ”Big Data Analytics in Science”
September 11, 2014
Title: ”Big Data Analytics in Science”
Big data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. Its revolutionary potential is now universally recognized. Data complexity, heterogeneity, scale, and timeliness make data analysis a clear bottleneck in many biomedical applications, due to the complexity of the patterns and lack of scalability of the underlying algorithms. Advanced machine learning and data mining algorithms are being developed to address one or more challenges listed above. It is typical that the complexity of potential patterns may grow exponentially with respect to the data complexity, and so is the size of the pattern space. To avoid an exhaustive search through the pattern space, machine learning and data mining algorithms usually employ a greedy approach to search for a local optimum in the solution space, or use a branch-and-bound approach to seek optimal solutions, and consequently, are often implemented as iterative or recursive procedures. To improve efficiency, these algorithms often exploit the dependencies between potential patterns to maximize in-memory computation and/or leverage special hardware for acceleration. These lead to strong data dependency, operation dependency, and hardware dependency, and sometimes ad hoc solutions that cannot be generalized to a broader scope. In this talk, I will present some open challenges faced by data scientist in biomedical fields and the current approaches taken to tackle these challenges.
Wei Wang is a professor in the Department of Computer Science at University of California at Los Angeles and the director of the Scalable Analytics Institute (ScAi). She is also a member of the UCLA Jonsson Comprehensive Cancer Center. Dr. Wang received her PhD degree in Computer Science from the University of California at Los Angeles in 1999. She was a professor at the University of North Carolina at Chapel Hill from 2002 to 2012, and was a research staff member at the IBM T. J. Watson Research Center between 1999 and 2002. Dr. Wang's research interests include big data, data mining, bioinformatics and computational biology, and databases. She has filed seven patents, and has published one monograph and more than one hundred research papers in international journals and major peer-reviewed conference proceedings.
Dr. Wang received the IBM Invention Achievement Awards in 2000 and 2001. She was the recipient of a UNC Junior Faculty Development Award in 2003 and an NSF Faculty Early Career Development (CAREER) Award in 2005. She was named a Microsoft Research New Faculty Fellow in 2005. She was honored with the 2007 Phillip and Ruth Hettleman Prize for Artistic and Scholarly Achievement at UNC. She was recognized with an IEEE ICDM Outstanding Service Award in 2012 and an Okawa Foundation Research Award in 2013. Dr. Wang has been an associate editor of the IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Knowledge Discovery in Data, Journal of Knowledge and Information Systems, International Journal of Knowledge Discovery in Bioinformatics, and an editorial board member of the International Journal of Data Mining and Bioinformatics and the Open Artificial Intelligence Journal. She serves on the organization and program committees of international conferences including ACM SIGMOD, ACM SIGKDD, ACM BCB, VLDB, ICDE, EDBT, ACM CIKM, IEEE ICDM, SIAM DM, SSDBM, BIBM.
April 3, 2014
Title: Impugning Alleged Randomness
According to a 1985 issue of New York Times, "The New Jersey Supreme Court today caught up with the Essex County Clerk and a Democrat who has conducted drawings for decades that have given Democrats the top ballot line in the county 40 times out of 41 times." But the clerk wasn't punished, and the case isn't unique. In the 1980s the Israeli tax authorities encouraged the public to request invoices (from plumbers, painters, etc.) and send the invoices in; big prizes were raffled off. But the operation collapsed when it turned out that the winner was none other than the Director of Customs and VAT at the time.
You may be convinced that such lotteries are rigged, but how would you justify your assertion in the court of law? The probability of the suspicious outcome is negligible but the probability of any particular outcome is negligible. What can you say? We attempt to furnish you with an argument.
Yuri Gurevich is Principal Researcher at Microsoft Research in Redmond, WA. He is also Professor Emeritus at the University of Michigan, ACM Fellow, Guggenheim Fellow, a member of Academia Europaea, and Dr. Honoris Causa of a Belgian and a Russian universities.
March 13, 2014
Title: Quest for Visual Intelligence
More than half of the human brain is involved in visual processing.
While it took mother nature billions of years to evolve and deliver us
a remarkable human visual system, computer vision is one of the
youngest disciplines of AI, born with the goal of achieving one of the
loftiest dreams of AI. The central problem of computer vision is to
turn millions of pixels of a single image into interpretable and
actionable concepts so that computers can understand pictures just as
well as humans do, from objects, to scenes, activities, events and
beyond. Such technology will have a fundamental impact in almost every
aspect of our daily life and the society as a whole, ranging from
e-commerce, image search and indexing, assistive technology,
autonomous driving, digital health and medicine, surveillance,
national security, robotics and beyond. In this talk, I will give an
overview of what computer vision technology is about and its brief
history. I will then discuss some of the recent work from my lab
towards large scale object recognition. I will particularly emphasize
on what we call the "three pillars" of AI in our quest for visual
intelligence: data, learning and knowledge. Each of them is critical
towards the final solution, yet dependent on the other. This talk
draws upon a number of projects ongoing at the Stanford Vision Lab.
Li Fei-Fei received the AB degree in physics from Princeton University and the PhD degree in electrical engineering from the California Institute of Technology. She is an associate professor in the Computer Science Department at Stanford University and the director of the Stanford Vision Lab.
Click here for more information about Fei-Fei Li's research.
February 27, 2014
University of Southern California
Title: Language Translation and Code-Breaking
In 1949, information theorist Warren Weaver proposed that code-breaking methods be applied to the problem of automatic language translation. He said: "When I look at an article in Russian, I say: this is really written in English, but it has been coded in some strange symbols. I will now proceed to decode".
Weaver's inspiration has borne fruit in this century, as statistical techniques have enabled us to build translation systems for many languages, with increasing accuracy. Other fruitful connections between code-breaking and translation are only starting to emerge. We will examine some, including the solution to the Copiale Cipher, a previously-undeciphered manuscript from the early 1700s.
Kevin Knight is a Senior Research Scientist and Fellow at the Information Sciences Institute of the University of Southern California (USC), and a Professor in USC's Computer Science Department. He received a PhD in computer science from Carnegie Mellon University and a bachelor's degree from Harvard University. His research interests include natural language processing, automata theory, machine translation, and decipherment.