We are happy to feature the following keynote speakers :

Keynote Speaker : Kelin Xia, Nanyang Technological University

Title: Mathematical AI for molecular data analysis

Abstract: Artificial intelligence (AI) based molecular data analysis has begun to gain momentum due to the great advancement in experimental data, computational power and learning models. However, a major issue that remains for all AI-based learning models is the efficient molecular representations and featurization. Here we propose advanced mathematics-based molecular representations and featurization (or feature engineering). Molecular structures and their interactions are represented as various simplicial complexes (Rips complex, Neighborhood complex, Dowker complex, and Hom-complex), hypergraphs, and Tor-algebra-based models. Molecular descriptors are systematically generated from various persistent invariants, including persistent homology, persistent Ricci curvature, persistent spectral, and persistent Tor-algebra. These features are combined with machine learning and deep learning models, including random forest, CNN, RNN, Transformer,

BERT, and others. They have demonstrated great advantage over traditional models in drug design and material informatics.

Short Bio: Dr. Kelin Xia obtained his Ph.D. degree from the Chinese Academy of Sciences in

Jan 2013. He was a visiting scholar in the department of Mathematics, Michigan State

University from Dec 2009-Dec 2012. From Jan 2013 to May 2016, he worked as a visiting

assistant professor at Michigan State University. He joined Nanyang Technological

University at Jun 2016. His research focused on Mathematical AI for molecular sciences. He

has published >60 papers and has been PI and Co-PI for 15 grants (>3.0M SGD).

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Keynote Speaker : Anqi Qiu, National University of Singapore

Title: Spectral Laplace-Beltrami Wavelets and Geometric Convolutional Neural Network for Signal Processing and Classification

The Laplace-Beltrami operator is a generalization of the Euclidean representation of the Laplace operator to an arbitrary Riemannian manifold. It is a self-adjoint operator and its eigenfunctions form a complete set of real-valued orthonormal basis functions. In this talk, I will introduce spectral Laplace-Beltrami wavelets and its computational algorithm. I will then demonstrate its use for smoothing and classification of the data defined on smooth surfaces embedded in the 3-D Euclidean space. Furthermore, I will discuss that the spectral Laplace-Beltrami Wavelets can be used for the construction of geometric convolutional neural network (CNN) and then introduce a vertex-based geometric CNN algorithm for regular surfaces in which translation and downsampling on surfaces can be the same as those in the regular grid. I will show the use of this method for the prediction of Alzheimer’s Disease.

Short Bio: Dr. Qiu has been devoted to innovation in computational analyses of complex and informative datasets comprising of disease phenotypes, neuroimage, and genetic data to understand the origins of individual differences in health throughout the lifespan. Her research group published ~140 peer-reviewed journal papers. Among them, 87% of papers were published in top 10% international journals in the field (based on SJR). Her team has developed a series of algorithms to analyze MRI data for understanding the brain anatomical and functional organization. These algorithms were derived based on theories of differential geometry, manifold statistics, and calculus of variation. They are powerful tools for the brain atlas generation, shape analysis, and studying statistical influences on complex structural shapes and functional organization. This series of works on medical image analysis were published in the top journals in the field, including IEEE transactions on medical image, IEEE transactions on image processing, and Medical Image Analysis.