Select a calendar:
Filter May Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for May 21, 2013
-
SAP TERP 10 Student Certification Academy
Tue, May 21, 2013 @ 08:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Speaker: Richard Vawter, USC Viterbi School of Engineering, USC Viterbi School of Engineering
Talk Title: SAP TERP 10 Student Certification Academy
Abstract: Speaker: Richard Vawter, USC Viterbi School of Engineering
Talk Title: SAP TERP 10 Student Certification Academy
Abstract: The University of Southern California, being an active member of SAP's Global University Alliances program since its inception in 1996, has been chosen to offer the TERP10 Academy to its students in early Summer 2012. The TERP10 Academy, and its certification, is a direct response to the global forecast of needed SAP skills in the market, estimated at between 30,000 and 40,000, in the next several years.
Students completing the TERP10 Academy and passing SAP's certification exam will have the advantage of being equipped with a good understanding of business processes adopted by companies around the world. They will also get insights into best business practices and how SAP can be used to optimize business processes. Students will find that the TERP10 Certification will open internship opportunities as well as full time jobs with consulting firms such as Deloitte, Ernst and Young, KPMG, Hitachi, and other SAP partner companies.
There will be two offerings of the SAP TERP10 Student Certification Academy in 2012. Both offerings will run for 9 full days, with the the certification examination to be given on the morning of the 10th day.
Biography: Although Prof. Richard Vawter hasn't flown for over a decade, he's had plenty of experience in the cockpit especially as a college student! His undergraduate degrees at both Embry-Riddle Aeronautical University in Arizona and UCLA were accomplished by literally flying between classes.
Upon completing his Engineering degree at UCLA, Richard Vawter started work at Rockwell International analyzing the dynamic loads placed upon the Space Shuttle during the launch and entry phases of a mission. After the Challenger incident, Richard Vawter was chosen to be part of NASA's Crew Egress Team and assigned the task to design a system and method for the crew to escape the shuttle during a controlled emergency descent.
Following the resumption of the Space Shuttle flights, Prof. Vawter began taking graduate classes at the School of Engineering. After only one graduate class, Prof. Vawter became hooked on USC, completing Masters degrees in both Aerospace Engineering and Business Administration. After two years as a computer consultant, Prof. Vawter returned to USC and worked for the Marshall School of Business as a Computer Systems and Applications Specialist. During that time, he had the opportunity to fill in for a week teaching an ITP class and discovered his teaching talents when the students started clamoring for him to come back. Prof. Vawter began teaching officially at ITP in 1996 and currently focuses on SAP.
Host: Corporate and Professional Programs
More Info: http://gapp.usc.edu/professional-programs/short-courses/terp10
Host: Corporate and Professional Programs
Audiences: Registered Attendees
Contact: Viterbi Professional Programs
-
CS Distinguished Lecture: Eric Xing (CMU) - Big Data, Big Model, and Big Learning
Tue, May 21, 2013 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Eric Xing, Carnegie Mellon University
Talk Title: Big Data, Big Model, and Big Learning
Series: CS Distinguished Lectures
Abstract: In many modern applications built on massive data, such as societal-scale event detection, social security and privacy, web commerce and marketing, and personalized medicine, one needs to handle extremely large-scale data and models that threaten to exceed the limit of current infrastructures and algorithms. Due to the extremely large volume, high dimensionality, and massive task complexity associated with this applications, many modern advancements in computational and statistical learning have been rendered un-leverageable due to their poor scalability on ultra-dimensional models and inability to extract values from massive data; practitioners are forced to turn to naive alternatives such as KNN or K-means cluster for complex problems purely due to their simplicity and scalability, but not for their model validity and correctness. In this talk, I will present some thoughts and work on big learning problems in web-scale social data mining, computational biology, and computer vision. I will discuss some insights and promising directions toward large data size, large feature dimension, and large concept space, including parallelizable and online Monte Carlo for infinite dynamic topic models, fast 1st-order convex optimization algorithms for learning ultra high-dimensional sparse structured input/output models, and output coding techniques for massive multi-task and transfer learning, and I will discuss the design and issues of low level computer architecture and operating systems supporting large learning, applied to a wide range of problems.
Biography: Dr. Eric Xing is an associate professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in social and biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) large-scale information & intelligent system in social networks, computer vision, and natural language processing. Professor Xing has published over 180 peer-reviewed papers, and is an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He is a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, the IBM Open Collaborative Research Award, and best paper awards in a number of premier conferences including UAI, ACL, SDM, and ISMB.
Host: Gaurav Sukhatme, Michael Waterman
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
