The Science Atlantic MSCS Conference has three invited lectures: the Sedgwick Lecture (in CS), the Blundon Lecture (in mathematics), and the Field Lecture (in statistics). These lectures are given in honour of Professors Art Sedgwick, W.J. Blundon and Chris Field.
Sedgwick Lecture: Models and hardness results for predicting secondary structure and kinetics of interacting DNA strands.
The field of molecular programming aims to build computing devices, such as logic circuits, by harnessing DNA’s four-letter digital sequence and propensity for secondary structure formation via Watson-crick base pairing. DNA programs execute when sets of interacting molecules change structure over time, consistent with DNA kinetics. Accordingly, it is very useful to computationally predict DNA structure and kinetics, i.e., changes in structure over time. We’ll describe our recent progress in developing computational models of DNA kinetics, as well as hardness results on the computational complexity of predicting secondary structure of multiple strands.
Anne Condon is Professor of Computer Science at the University of British Columbia.
Her research interests are in the areas of theoretical computer science and biomolecular computation, with a current focus on ways to computationally predict and design nucleic acid structures. Anne received her Bachelor’s degree from University College Cork, Ireland, and her Ph.D. at the University of Washington. She is an ACM Fellow and a Fellow of the Royal Society of Canada.
Blundon Lecture: Hearing the shape of a drum
Fifty years ago Marc Kac posed the question “Can you hear the shape of a drum?” Specifically, if you know all of the frequencies at which a planar region resonates can you deduce its shape? It took almost 30 years to answer Kac’s question and there are several basic questions that are still open. We will survey some of the wonderful mathematics associated with hearing the shape of a drum.
Kabe Moen is an associate professor of mathematics at the (University of Alabama), Tuscaloosa (AL). Kabe has a wide variety of research interests ranging from harmonic analysis and partial differential equations to chess compositions. With a Simons Foundation grant supporting his work, Kabe’s recent interests focus on weighted estimates in harmonic analysis and PDE. Kabe earned his B.A. in mathematics at William Jewell College (MO) in 2002, his M.A. in 2005 and Ph.D. in 2009 both from the University of Kansas, Lawrence (KS).
Field Lecture: An Overview of Statistical Learning
Like machine learning, the field of statistical learning seeks to “learn from data”. I will review some of the central ideas that identify statistical learning, including regularization and the bias-variance trade-off, resampling methods such as cross-validation for selecting the amount of regularization, the role of a probability model for data, and quantification of uncertainty. These ideas will be discussed in the context of popular recent approaches to supervised and unsupervised learning.
Hugh Chipman received his B.Sc. from Acadia University in 1990 and his M.Math and Ph.D.from Waterloo (1991, 1994). He is a P.Stat. Before joining the faculty at Acadia in 2004, he held faculty appointments at the University of Chicago and the University of Waterloo. He has served as the Statistical Society of Canada’s Electronic Services Manager and local arrangements chair for the 2011 SSC meeting in Wolfville. He is President-Elect of the SSC. He has served the broader Statistical Sciences community as a member and chair of the NSERC Grant Selection Committee for Statistics and as Editor of Technometrics. He held a Canada Research Chair from 2004-14, has been awarded the CRM-SSC prize and is a fellow of the American Statistical Association. His research interests include statistical learning, computational statistics, industrial statistics and Bayesian methods.