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Prof. Vanessa Robins (Australian National University)
Prof. Vanessa Robins (Australian National University)
Vanessa Robins, is Associate Professor in the Research School of Physics at the Australian National University. Her contributions to topological data analysis include early mathematical foundations for persistent homology, algorithms for computing persistence diagrams from 2D and 3D digital images, and their interpretation in scientific applications. She has contributed to over 50 publications with over 50 co-authors, spanning various disciplines including soft matter, crystallography, geological materials, theoretical physics, computer science, pure and applied mathematics. She is currently collaborating with medical scientists interested in quantifying and comparing geometric structure of the lung airway tree from chest CT scans.
Link to ANU-physics home page: https://physics.anu.edu.au/contact/people/profile.php?ID=75
Dr. Don Vicendese (The University of Melbourne)
Dr. Don Vicendese (The University of Melbourne)
Dr Don Vicendese (BSc Hons Mathematics, PhD 2015) is a full-time Research Fellow with the Allergy Lung Health Unit, Centre for Epidemiology and Biostatistics at the University of Melbourne, Adjunct Research Fellow, School of Engineering and Mathematical Sciences at La Trobe University & Honorary Fellow, Allergy and Immunology Murdoch Children’s Research Institute. He is an accredited statistician with the Statistical Society of Australia, a member of The International Association for Statistical Education and Australian Mathematical Sciences Institute, and a statistical sub-editor for Respirology.
He brings over 13 years experience applying statistical and machine learning methods across COPD, asthma, bronchiectasis, allergy, cancer, and nutrition. He collaborates across major Australian cohort studies, including the Tasmanian Longitudinal Health Study (TAHS), Melbourne Atopy Cohort Study (MACS), HealthNuts, The Centre for Research Excellence in Bronchiectasis in Children (BIC), and the National Health Medical Research Council (NHMRC) funded AusPollen project. He currently is developing clinical prediction models for COPD and food allergy outcomes and initiated an international collaboration with TAHS, the globe’s longest running respiratory study, The Australian National University (ANU) and the University of Tokyo to develop advanced algorithms for extracting morphological information from lung CT scans.
Prof. Yusuke Imoto (Kyoto University)
Prof. Yusuke Imoto (Kyoto University)
Yusuke Imoto is an Associate Professor at the Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University. His research focuses on developing mathematical and computational frameworks for single-cell omics data, drawing on mathematical theories such as high-dimensional statistics and topological data analysis to understand complex biological systems.
Presentation abstract: Single-cell RNA sequencing yields high-dimensional data, profiling more than 20,000 genes per cell. In such large dimensions, high-dimensional statistics demonstrate that estimators behave differently, revealing sample and population mismatches (the curse of dimensionality) that are not observed in lower dimensions. This often leads to conventional analytical methods overlooking crucial biological structures. To overcome this challenge, we developed RECODE (resolution of the curse of dimensionality), a noise reduction method specifically for single-cell data, leveraging Yata and Aoshima’s eigenvalue correction theory. This presentation will cover RECODE’s fundamental principles, its latest advancements, and future perspectives.
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