For students at Economics

Speaker: Xinyu Zhang (Zhejiang University) Abstract:In this talk, I will discuss various aspects of tetrahedron instantons. Tetrahedron instantons can be realized using D0-branes probing a system of intersecting D6-branes in type IIA superstring theory, and can be viewed as particular solutions of general instanton equations in noncommutative field theory. The tetrahedron instanton partition function lies between the higher-rank Donaldson-Thomas invariants on Calabi-Yau threefolds and fourfolds, and can be computed exactly. Remarkably, the K-theoretic tetrahedron instanton partition function allows an interpretation in terms of the index of M-theory on a noncompact Calabi-Yau fivefold which is related to a superposition of Kaluza-Klein monopoles. 

Jakob Kusnick defends his PhD thesis: Visualization-based Storytelling for Digital Humanities.Associate Professor Melih Kandemir will act as chairman at the defence.

During each month in January to June 2024, the SDU Climate Cluster elite centre CUHRE arranges a series of online Wild Pedagogies Gatherings in a cooperation with the Simon Fraser University.You can join for a presentation, a dialogue and sharing among the vibrant, global community. The invitation is open to anyone engaging with or curious about Wild Pedagogies. Please get in touch if you would like to presentThe next event take place on: 27. juni 2024 at 09.00 – 10.30 p.m. You can read more here Wild Pedagogies

Meeting notice with time and agenda will be sent by email.

Speaker: Professor James BaileyAbstract: In this seminar, we will review a measure known as Local Intrinsic Dimensionality (LID), which can be used for characterizing the complexity of local neighbourhoods in data. LID can loosely be thought of as a measure for the number of latent variables needed for characterising a particular locality in multi dimensional space. In this talk we will review the LID measure and its uses in machine learning and data mining. In particular, we focus on two recent exciting applications.The first application is in anomaly detection, where we report on a ‘dimensionality-aware’ outlier detection method, DAO, which is derived as an estimator of an asymptotic local expected density ratio involving a query point and a close neighbor drawn at random. DAO significantly outperforms three popular and important benchmark local outlier detection methods.The second application is in the field of self supervised learning, where we show i) how the use of LID for dimensionality regularization at a local level can be used to mitigate an underfilling phenomenon known as dimensional collapse and ii) how the local dimensionality of deep representations can be used as a proxy target when searching for suitable data augmentation policies in contrastive learning. Short Biography: James Bailey is a Professor in the Faculty of Engineering and Information Technology at The University of Melbourne and Program Lead for the University’s Artificial Intelligence Platform. He was previously an Australian Research Council Future Fellow and is a researcher in the field of machine learning and artificial intelligence, including interdisciplinary applications and operational frameworks. His interests particularly relate to the assurance, certification and safety of systems based on machine learning and artificial intelligence. He works on the deployment of AI systems in collaboration with a wide range of industry and government partners across sectors such as defence, energy and health. He is co-General Chair for the Australasian Joint Conference in Artificial Intelligence to be held in Melbourne in December 2024.Read more here: https://findanexpert.unimelb.edu.au/profile/351-james-bailey

Speaker: Michael E. Houle, New Jersey Institute of Technology (NJIT), USAAbstract: In this seminar, we will review a measure known as Local Intrinsic Dimensionality (LID), which can be used for characterizing the complexity of local neighbourhoods in data. LID can loosely be thought of as a measure for the number of latent variables needed for characterising a particular locality in multi dimensional space. In this talk we will review the LID measure and its uses in machine learning and data mining. In particular, we focus on two recent exciting applications.The first application is in anomaly detection, where we report on a ‘dimensionality-aware’ outlier detection method, DAO, which is derived as an estimator of an asymptotic local expected density ratio involving a query point and a close neighbor drawn at random. DAO significantly outperforms three popular and important benchmark local outlier detection methods.The second application is in the field of self supervised learning, where we show i) how the use of LID for dimensionality regularization at a local level can be used to mitigate an underfilling phenomenon known as dimensional collapse and ii) how the local dimensionality of deep representations can be used as a proxy target when searching for suitable data augmentation policies in contrastive learning. 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

All staff at IMADA are invited to the Departmental Fora.Meeting notice and agenda will be sent by email.

Meeting notice with time and agenda will be sent by email.

Read more about the Teaching Committee: https://www.sdu.dk/da/om_sdu/institutter_centre/imada_matematik_og_datalogi/ledelse_administration/raad_naevn_udvalg/undervisningsudvalg

12.-13. september samles forskere fra Nordic Humanities Center til et symposium med nordiske kolleger.Titlen på symposiet er ”Individ og stat i den nordiske model – historiske erfaringer og fremtidig samfundsmæssig legitimitet", og det foregår på Linnéuniversitetet i Växsjö i Sverige.

All assistant, associate and full professors at IMADA are invited to the Professor Meeting.Meeting notice and agenda will be sent by email.

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

Meeting notice with time and agenda will be sent by email.

Read more about the Department Council: https://sdunet.dk/en/enheder/institutter/imada/udvalg-og-moeder/institutraad

Read more about the Teaching Committee: https://www.sdu.dk/da/om_sdu/institutter_centre/imada_matematik_og_datalogi/ledelse_administration/raad_naevn_udvalg/undervisningsudvalg

All staff at IMADA are invited to the Departmental Fora.Meeting notice and agenda will be sent by email.

Meeting notice with time and agenda will be sent by email.

Read more about the Teaching Committee: https://www.sdu.dk/da/om_sdu/institutter_centre/imada_matematik_og_datalogi/ledelse_administration/raad_naevn_udvalg/undervisningsudvalg

All assistant, associate and full professors at IMADA are invited to the Professor Meeting.Meeting notice and agenda will be sent by email.

Meeting notice with time and agenda will be sent by email.

Read more about the Department Council: https://sdunet.dk/en/enheder/institutter/imada/udvalg-og-moeder/institutraad

Read more about the Teaching Committee: https://www.sdu.dk/da/om_sdu/institutter_centre/imada_matematik_og_datalogi/ledelse_administration/raad_naevn_udvalg/undervisningsudvalg

All staff at IMADA are invited to the Departmental Fora.Meeting notice and agenda will be sent by email.

Read more about the Teaching Committee: https://www.sdu.dk/da/om_sdu/institutter_centre/imada_matematik_og_datalogi/ledelse_administration/raad_naevn_udvalg/undervisningsudvalg