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SUNMONTUEWEDTHUFRISAT
Events for December 06, 2011
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Fall Study Day
Tue, Dec 06, 2011 @ 10:00 AM - 04:00 PM
Viterbi School of Engineering Student Affairs
Student Activity
Come join fellow Viterbi undergrads and get ready for finals!
10:00 am - 1:00 pm
Study sessions for:
CHEM 105a
CSCI 101
MATH 125, 126, 226, & 245
PHYS 151 & 152
1:00 - 4:00 pm
Open study space in CED, VARC, and RTH classrooms
Workshop on exam-prep strategies
Breakfast snacks and care packages will be provided for participants.
Upper-division Study Partners will be on hand to answer questions.
Study space will be open throughout the second floor of RTH.
Simply bring your books and notes to the RTH lobby.
Stop by VARC or CED for more information!
**Want to be a Study Partner? You must have earned at least a B+ in the class you are interested in. Please email Christine D'Arcy at cdarcy@usc.edu iif you are interested in being a Study Partner. Study Partners are volunteers.Location: Ronald Tutor Hall of Engineering (RTH) - Lobby
Audiences: Undergrad
Contact: Christine D'Arcy
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ENGR 499 Electric Vehicle Solar Charging Station Demo
Tue, Dec 06, 2011 @ 11:00 AM - 01:00 PM
USC Viterbi School of Engineering
Student Activity
Join us on the E-Quad Tuesday as Prof. Alice Parker's ENGR 499 Alternative Energy Project Course students demo the solar charging stations they've designed using one of USC's electric cars. Materials supplied by USC Viterbi and Trojan Batteries.
Location: Engineering Quad
Audiences: Everyone Is Invited
Contact: Katie Dunham
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Epstein Institute Seminar Series / ISE 651 Seminar
Tue, Dec 06, 2011 @ 04:00 PM - 05:20 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Xiao-Li Meng, Whipple V. N. Jones Professor of Statistics and Chair, Department of Statistics, Harvard University
Talk Title: "Machine Learning with Human Intelligence: Principled Corner Cutting (PC2)"
Series: Epstein Institute Seminar Series
Abstract: With the ever increasing availability of quantitative information, especially data with complex spatial and/or temporal structures, two closely related fields are undergoing substantial evolution: Machine learning and Statistics. On a grand scale, both have the same goal: separating signal from noise. In terms of methodological choices, however, it is not uncommon to hear machine learners complain about statisticiansâ excessive worrying over modeling and inferential principles to a degree of being willing to produce nothing, and to hear statisticians express discomfort with machine learnersâ tendency to let ease of practical implementation trump principled justifications, to a point of being willing to deliver anything. To take advantage of the strengths of both fields, we need to train substantially more principled corner cutters. That is, we must train researchers who are at ease in formulating the solution from the soundest principles available, and equally at ease in cutting corners, guided by these principles, to retain as much statistical efficiency as feasible while maintaining algorithmic efficiency under time and resource constraints. This thinking process is demonstrated by applying the self-consistency principle (Efron, 1967; Lee, Li and Meng, 2012) to handling incomplete and/or irregularly spaced data with non-parametric and semi-parametric models, including signal processing via wavelets and sparsity estimation via the LASSO and related penalties.
Biography: Dr. Xiao-Li Meng is the Whipple V. N. Jones Professor of Statistics and Chair, Department of Statistics at Harvard University. His research interests include:
⢠Statistical inference with partially observed data, pre-processed data, and simulated data.
⢠Quantifying statistical information and efficiency in scientific studies, particularly for genetic and environmental problems.
⢠Statistical principles and foundational issues, such as multi-party inferences, the theory of ignorance, and the interplay between Bayesian and frequentist perspectives.
⢠Effective deterministic and stochastic algorithms for Bayesian and likelihood computation; Markov chain Monte Carlo, especially perfect sampling.
⢠Bayesian inference, ranking and mapping.
⢠Multi-resolution modelling for signal and image data.
⢠Statistical issues in astronomy and astrophysics.
⢠Modelling and imputation in health and medical studies.
⢠Elegant mathematical statistics.
Education
⢠1990: Ph.D. in Statistics - Harvard University
⢠1987: M.A. in Statistics - Harvard University
⢠1986: Diploma in Graduate Study of Mathematical Statistics - Research Institute of Mathematics, Fudan University, Shanghai, P.R. China
⢠1982: B.S. in Mathematics - Fudan University, Shanghai, P.R. China
Host: Daniel J. Epstein Department of Industrial and Systems Engineering
More Information: Seminar-Meng.doc
Location: Hughes Aircraft Electrical Engineering Center (EEB) - Room 248
Audiences: Everyone Is Invited
Contact: Georgia Lum
