Spring 2009
All seminars are 4:00  5:00 p.m., in Cullimore Hall
Room 611 (Math Conference Room) unless noted otherwise.
Refreshments are usually served at 3:30 p.m., and talks start at 4:00 p.m.
If you have any questions about a particular seminar, please contact
the person hosting the speaker.



Thursday 
Yujun Wu, SanofiAventis Approaches to Handling Data When a Phase II Trial Deviates from the Prespecified Simon’s TwoStage Design (abstract) 
Sunil Dhar 
Thursday April 30,2009 4:00PM 
Ganesh K. (Mani)
Subramaniam,
AT&T
Labs  Research, Florham Park, NJ 
Sunil Dhar 
ABSTRACTS
Simon’s ‘optimal’ and ‘minimax’ twostage designs are common methods for conducting phase IIA studies investigating new cancer therapies. However, these designs are rather rigid in their settings because of the prespecified rejection rules and fixed sample sizes at each stage. In practice, we often encounter the problem that a study is unable to adhere to the event number and sample sizes of the original twostage design. In this paper, we consider some approaches in handling situations where deviations or interruptions from the original Simon’s twostage design occur because recruitment of patients is slower than expected. We consider four scenarios and use conditional probabilities to address the issues commonly inquired by the scientific review board. We also discuss how to report pvalues in these situations. Yujun Wu ~ March 5, 2009 
Some Approaches to Mine Time Series Data: Business decisions and business
process monitoring are often based on time series data that represent an
aggregation of a large number of time series. Although the inferences
are generally based on the aggregate data, significant insights lurk in
the underlying time series that had been combined. The challenge that
analysts currently face is the large number and complexity of data
underlying aggregate time series. This paper provides a framework that
supports drill down analysis and screening of large scale time series
data by developing feature extraction rules. We develop an exploratory
method based on functional data analysis, where we fit smooth functions.
One feature extraction involves estimating derivatives from these
models. These provide insights on the bumps and dips for the underlying
time series. Ganesh K. (Mani) Subramaniam ~ April 30, 2009 