Department of Biology

Speaker: Michael E. Houle, New Jersey Institute of Technology (NJIT), USAAbstract: Researchers have long considered the analysis of similarity applications in terms of the intrinsic dimensionality (ID) of the data. Although traditionally ID has been viewed as a characterization of the complexity of discrete datasets, more recently a local model of intrinsic dimensionality (LID) has been extended to the case of smooth growth functions in general, and distance distributions in particular, from its first principles in terms of similarity, features, and probability. Since then, LID has found applications — practical as well as theoretical — in such areas as similarity search, data mining, and deep learning. LID has also been shown to be equivalent under transformation to the well-established statistical framework of extreme value theory (EVT). In this tutorial, we will survey some of the wider connections between ID and other forms of complexity analysis, including EVT, power-law distributions, chaos theory, anddynamical systems. We will then see how LID can potentially serve as a unifying framework for the understanding of these theories in the context of machine learning in general, and deep learning in particular.Short Biography: Michael Houle obtained his PhD degree in 1989 from McGill University in Canada, in the area of computational geometry. Since then, he developed research interests in algorithmics, data structures, and relational visualization, first at Kyushu University and the University of Tokyo in Japan, and from 1992 at the University of Newcastle and the University of Sydney in Australia.From 2001 to 2004, while at IBM Japan's Tokyo Research Laboratory, he first began working on approximate similarity search and shared-neighbor clustering methods for data mining applications.From 2004, at the National Institute of Informatics, Tokyo, his research interests expanded to include dimensionality and scalability in the context of fundamental AI / machine learning / data mining tasks such as search, clustering, classification, and outlier detection.In 2021, he relocated to Vancouver, BC, Canada. Currently he is with the New Jersey Institute of Technology in Newark, NJ, USA, and divides his time between Newark and Vancouver.Read more here: https://people.njit.edu/profile/meh43

Speaker: Manuel Del Piano, PhD studentAbstract: TBALocation: The DIAS Meetingroom Syd (V22-503a-2)You can also join via Zoom (passcode: 060379).The event is open to all.

Speaker: Marika D’Avanzo, PhD studentAbstract: TBALocation: The DIAS Meetingroom Syd (V22-503a-2)You can also join via Zoom (passcode: 060379).The event is open to all.

David Wallace holds the Judith Rodin Chair of English at the University of Pennsylvania. He is a prominent literary historian who has broken out of the national paradigm in innovative ways. He has published widely on late medieval English literature, and his collaborative project which resulted in the two-volume Europe – A Literary History 1348-1418 (Oxford UP, 2016) is highly acclaimed. He is now editing a global literary history, National Epics, which focuses on national literatures in a large chronological and comparative frame – from Homer to the present day.This event is open for all

This year's special theme: Supervision

This year's special theme: Supervision