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Events for January 15, 2016
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Shared Memory Reconfigurable Computing for the Cloud
Fri, Jan 15, 2016 @ 10:45 AM - 11:45 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Peter Hofstee, IBM Austin Research Laboratory
Talk Title: Shared Memory Reconfigurable Computing for the Cloud
Abstract: This talk covers recent work on shared memory reconfigurable computing. We start with enhancements made to the Power 8 processor, in particular the CAPI interface that allows for off-chip accelerators with full coherent access to all shared system resources. Next we cover some examples of how this kind of acceleration can improve system functions such as networking and storage. We then turn to gene sequencing as an example of compute acceleration and show that each of the major stages can be improved with reconfigurable logic. We end by discussing prototype cloud-based infrastructure that researchers can use to develop and deploy their own solutions.
Biography: H. Peter Hofstee (Ph.D. California Inst. of Technology, 1995) is a distinguished research staff member at the IBM Austin Research Laboratory, USA, and a part-time professor in Big Data Systems at Delft University of Technology, Netherlands. Peter is best known for his contributions to heterogeneous computer architecture as the chief architect of the Synergistic Processor Elements in the Cell Broadband Engine processor, used in the Sony Playstation3 and the first supercomputer to reach sustained Petaflop operation. After returning to IBM research in 2011 he has focused on optimizing the system roadmap for big data, analytics, and cloud, including the use of accelerated compute. His early research work on coherently attached reconfigurable acceleration on Power 7 paved the way for the new coherent attach processor interface on POWER 8. Peter is an IBM master inventor with more than 100 issued patents and a member of the IBM Academy of technology.
Host: Prof. Viktor Prasanna
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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SmartCare: A Discovery and Clinical Decision Support System for Personalized Healthcare
Fri, Jan 15, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Mihaela van der Schaar, UCLA
Talk Title: Ai Seminar: SmartCare: A Discovery and Clinical Decision Support System for Personalized Healthcare
Series: Artificial Intelligence Seminar
Abstract: Modern technology makes it possible to collect more and more data for each patient; modern record-keeping makes it possible to access more and more data from past patients. Unfortunately, it has so far not been possible to make use of all this data to create a system of personalized diagnosis and treatment.Instead, current diagnosis and treatment continues to rely on Clinical Practice Guidelines, which are largely based on experience and opinion rather than on scientific analysis and evidence, are geared towards the -lowest common
denominator-, ignore the strengths of a given institution (e.g. specialists, technology) and are targeted toward a representative patient rather than toward the unique characteristics of the current patient.
In this talk, I will present a novel framework SmartCare that integrates the (longitudinal, multi-modal) data of the current patient (demographic information, current medical condition, medical/family history, availability of home care, etc.) with what has been learned from previous patients to recommend personalized diagnosis and treatment. To do so,
SmartCare must overcome enormous conceptual, theoretical and practical challenges, some of which will
be discussed in the talk.
SmartCare has already recorded an important (even remarkable) success: a vastly improved procedure for breast cancer screening. Current clinical practice (BI-RADS: Breast Imaging
Report and Data System) results in an enormous number of false positives, leading to many further invasive and unnecessary procedures (including surgery) that involve needless risk,
suffering and expense. The SmartCare procedure reduces false positives by 39% while maintaining the same misdetection rate. In the U.S. alone, this means 80,000 fewer false positives per year.
Biography: Mihaela van der Schaar is Chancellor's Professor of Electrical Engineering at University of California, Los Angeles. She received an NSF CAREER Award (2004), the Okawa Foundation Award (2006), the IBM Faculty Award (2005, 2007, 2008), and several best paper awards, including the 2011 IEEE Circuits and Systems Society Darlington Award Best Paper Award. She holds 33 granted US patents. She is also the founding and managing director of the UCLA Center for Engineering Economics, Learning, and Networks (see http://netecon.ee.ucla.edu). Her research interests are in data science, medical informatics, machine learning, game theory, and network science. For more information about her research visit: http://medianetlab.ee.ucla.edu/
Host: Greg Ver Steeg
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=2cad8868c8314e248c72e1ba11c4c0e61dLocation: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=2cad8868c8314e248c72e1ba11c4c0e61d
Audiences: Everyone Is Invited
Contact: Peter Zamar
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W.V.T. Rusch Engineering Honors Program Colloquium
Fri, Jan 15, 2016 @ 01:00 PM - 01:50 PM
USC Viterbi School of Engineering, Viterbi School of Engineering Student Affairs
University Calendar
Join us for a presentation by Dr. Carmen Boening, from the Jet Propulsion Laboratory, titled "Measuring Climate Change with Gravity."
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Ramon Borunda/Academic Services
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NL Seminar-Learning Open Domain Knowledge From Text
Fri, Jan 15, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Gabor Angeli, Stanford University
Talk Title: Learning Open Domain Knowledge From Text
Series: Natural Language Seminar
Abstract: The increasing availability of large text corpora holds the promise of acquiring an unprecedented amount of knowledge from this text. However, current techniques are either specialized to particular domains or do not scale to large corpora. This dissertation develops a new technique for learning open-domain knowledge from unstructured web-scale text corpora. A first application aims to capture common sense facts: given a candidate statement about the world and a large corpus of known facts, is the statement likely to be true? We appeal to a probabilistic relaxation of natural logic -- a logic which uses the syntax of natural language as its logical formalism -- to define a search problem from the query statement to its appropriate support in the knowledge base over valid (or approximately valid) logical inference steps. We show a 4x improvement at retrieval recall compared to lemmatized lookup, maintaining above 90% precision. This approach is extended to handle longer, more complex premises by segmenting these utterance into a set of atomic statements entailed through natural logic. We evaluate this system in isolation by using it as the main component in an Open Information Extraction system, and show that it achieves a 3% absolute improvement in F1 compared to prior work on a competitive knowledge base population task. A remaining challenge is elegantly handling cases where we could not find a supporting premise for our query. To address this, we create an analogue of an evaluation function in game playing search: a shallow lexical classifier is folded into the search program to serve as a heuristic function to assess how likely we would have been to find a premise. Results on answering 4th grade science questions show that this method improves over both the classifier in isolation and a strong IR baseline, and achieves the best published results on the task.
Biography: Gabor is a new graduate from Chris Manning's natural language processing lab. He graduated with a BS in electrical engineering/computer science from UC Berkeley in 2010, and defended his Ph.D. in the fall of 2015. His research focuses on natural language understanding, ranging from relation extraction and knowledge base population, textual entailment, common-sense reasoning, and question answering. He has led the Stanford knowledge base population project for the past three years, with Stanford ranking 5th, 1st, and 1st (tied) among teams participating in the TAC-KBP competition over those three years. In addition to publications at ACL, EMNLP and NAACL, he co-authored an EMNLP best dataset paper on collecting a large dataset for textual entailment. Outside of academia, he was the NLP architect for Baarzo in 2014 (acquired by Google), and is currently a fellow at XSeed Capital. In his free time, Gabor enjoys hiking, board games, and binge-watching Netflix shows.
Host: Xing Shi and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
