# Organizers

**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.