معرفی
Matthew Hirn is a computational mathematician with expertise in harmonic analysis, data science, and machine learning. He was a tenured Associate Professor at Michigan State University (MSU), jointly appointed in the Department of Computational Mathematics, Science & Engineering and the Department of Mathematics. As the scientific leader of the ComplEx Data Analysis Research (CEDAR) Team at MSU, he developed innovative methods for analyzing complex data, including wavelet scattering transforms for atomistic systems and geometric deep learning techniques. He is currently a Senior Quantitative Researcher at Citadel LLC, contributing to algorithmic trading strategies. His research bridges theoretical mathematics and applied data science, with notable contributions to graph neural networks, scattering transforms, and privacy-preserving methods.
His academic career included organizing conferences such as the SIAM Mathematics of Data Science mini-symposium on geometric learning and co-authoring influential software like the Kymatio library for wavelet scattering transforms. He advised PhD students Ryan LaRose, Xavier Brumwell, and Jieqian He, who have since pursued impactful roles in academia and industry. His work on 3D wavelet scattering networks for material science and geometric scattering on manifolds has advanced the intersection of mathematics and machine learning.
Matthew’s research interests span signal processing, graph representation learning, and applications in bioinformatics and many-particle physics. He has published extensively in top journals and conferences, including the Journal of Chemical Physics and the Journal of Machine Learning Research.
