Alex Cloninger is an Associate Professor in Mathematics and the Halıcıoğlu Data Science Institute at UC San Diego. He holds a B.S. degree from Washington University in St. Louis, and a PhD in Applied Mathematics and Scientific Computation from the University of Maryland College Park. He was previously an NSF Postdoc and Gibbs Assistant Professor of Mathematics at Yale University. Alex researches problems in the area of geometric data analysis and applied harmonic analysis. He focuses on approaches that model the data as being locally lower dimensional, including data concentrated near manifolds or subspaces. These types of problems arise in a number of scientific disciplines, including imaging, medicine, and artificial intelligence, and the techniques developed relate to a number of machine learning and statistical algorithms, including deep learning, network analysis, statistical distances, and imaging. Alex has recently organized special sessions on Laplacians and Applications at SIAM PDE Conference (2017), on AI and Deep Learning in Radiation Oncology at the ASTRO conference (2018), on High-Dimensional Signal Processing and Machine Learning at the Data Science Annual Conference at University of San Francisco Data Science Institute (2018), and on Distance Metrics and Mass Transfer Between High Dimensional Point Clouds at ICIAM (2019).
Manohar Kaul is an Associate Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology, Hyderabad (IIT-H), where he heads the क्रम(Krama) Lab. His research interests are applied algebraic topology (topological data analysis), geometric machine learning (graph and point-cloud representation learning), and optimal transport (discrete OT and assignment problems). His work also involves developing practical machine learning algorithms with rigorous theoretical foundations.
Ira Ktena is a research engineer at DeepMind working with the DeepMind for Google team. Previously, she was a Senior Machine Learning Researcher with the Cortex Applied Research team at Twitter UK, focusing on real-time personalisation and causal analysis for recommendations.
Nina Miolane is an Assistant Professor at the University of California, Santa Barbara (UCSB) in Computational Biomedicine. She holds an M.S. in Mathematics from Ecole Polytechnique (France) & Imperial College (UK), and a Ph.D. in Computer Science from INRIA (France) in collaboration with Stanford University. After her studies, Nina spent two years at Stanford University in Statistics as a postdoctoral fellow, and worked as a deep learning software engineer in the Silicon Valley. At UCSB, Nina directs the BioShape Lab, whose goal is to explore the “geometries of life” and turn biological shapes into biomedical insights. The BioShape Lab also co-develops Geomstats, an open-source software for geometric statistics and (deep) learning.
Bastian Rieck is the principal investigator of the AIDOS Lab at the Institute of AI for Health at the Helmholtz Centre Munich, Germany. Bastian received his M.Sc. degree in mathematics, as well as his Ph.D. in computer science, from Heidelberg University in Germany. Previously, he was a postdoctoral researcher and, later on, senior assistant at the Machine Learning and Computational Biology Lab at ETH Zurich. His main research interests are algorithms for graph classification and time series analysis, with a focus on biomedical applications and healthcare topics. Bastian is also enticed by finding new ways to explain neural networks using concepts from algebraic and differential topology. He is a big proponent of scientific outreach and enjoys blogging about his research, academia, supervision, and software development. Bastian recently organized a tutorial on topological machine learning for ECML, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, a workshop on Topological Data Analysis & Beyond at NeurIPS 2020, and a workshop on Geometrical and Topological Representation Learning at ICLR 2021.
Guy Wolf is an Associate Professor in the Department of Mathematics and Statistics at the Université de Montréal and a Core Member of Mila - the Québec AI Institute. He is also affiliated with IVADO - the institute of data valorization at Montreal. His research focuses on manifold learning, representation learning, and geometric deep learning for exploratory data analysis, including methods for dimensionality reduction, visualization, denoising, data augmentation, and coarse graining, with particular focus on applications in biomedical data exploration. His recent organization activities include the CRM-IVADO Industrial Problem Solving Workshop (IPSW 2020), a two-part mini-symposium on geometric deep learning as part of the inaugural SIAM Conference on Mathematics of Data Science held online in June 2020, and a NeurIPS 2020 workshop on Topological Data Analysis & Beyond.