Current challenges in statistical learning (11w5051)
Hugh Chipman (Acadia University)
Xiaotong Shen (University of Minnesota)
Rob Tibshirani (Stanford University)
Joseph S. Verducci (The Ohio State University)
Mu Zhu (University of Waterloo)
Ji Zhu (University of Michigan)
Recent developments in statistical learning provide an excellent opportunity to bring several research communities in Canada and the US together. At present, statistical learning is an emerging field of research, and there is an urgent need to develop this field in Canada. The proposed workshop will provide a platform for exchanging ideas between the communities, leading to productive collaborations, especially among young researchers. The proposed conference will be structured around various topics in machine learning, support vector machines, boosting, and other non-linear classification techniques, as well as on application areas of gene expressions via microarrays and bio/chemo-informatics. BIRS is ideal for hosting this workshop for advancing these objectives.
We place great emphasis on training future researchers in this field. The proposed workshop provides a great opportunity for graduate students and new researchers in Canada to get exposure to cutting-edge research in statistical learning. For this purpose, some presentations will be introductory, focusing on important problems and a general picture of recent developments. These introductory presentations, together with more technical topics, will be able to attract more graduate students and new researchers into this field.
We plan to have presentations in a variety of areas of statistical learning, with balanced presentations from senior and junior researchers, ranging from theory to application, and from methodology aspects to software developments. We hope that this workshop serves as a platform for significant further developments.