Work experience

LOOKING FOR JOBS, e.g., Data Scientist, Machine Learning Engineer, and Quantitative Researcher…

Education

Degree	Field of study	University	Year
PhD	Computational Science, supervised by Prof. Dr. Michael Multerer, focused on high performance computing for solving PDEs on random geometries	Università della Svizzera italiana	2024 2020
Master	Probability and Mathematical Statistics, supervised by Prof. Dr. Xicheng Zhang	Wuhan University	2018 2015
Bachelor	Statistics, supervised by Prof. Dr. Yuanyuan Liu	Central South University	2015 2011

Academic Visiting 

Degree	Field of study	University	Year
Exchange PhD	Artificial Intelligence, supervised by Prof. Dr. Michael Bronstein, focused on graph neural networks	University of Oxford	2024 2023

Skills

Knowledge area	Skills
Languages	Mandarin (native), English (fluent)
Programming languages	Python (proficient), C++ (proficient), Scala (basics)
Operating systems	macOS & Linux
Utility tools	Git, Vim
High performance computing	OpenMP, OpenMPI, Eiger/Piz Daint supercomputers by CSCS
Graph processing on a single CPU	networkx, The Topology ToolKit, METIS
Graph machine learning libraries on GPU	PyTorch, PyTorch Geometric, cuGraph
Large graph storage and computation tools	Neo4j, Spark GraphX, Pregel

Awards/Grants

• PhD Mobility Grant, Switzerland (2023)
• Summer Research Internship Grant, Switzerland (2019)
• Summer School Traveling Grant, Germany (2016)
• Meritorious Winner of “Mathematical Contest in Modelling” - MAA/SIAM, USA (2014)
• First Prize of “Undergraduate Mathematical Contest in Modelling” - CSIAM, China (2013)

Publications and Preprints

• W. Huang, M. Valsecchi, M. Multerer, “Anisotropic multiresolution analyses for deep fake detection,” arXiv preprint arXiv:2210.14874 (under review).

• J. Dölz, W. Huang, M. Multerer, “p-multilevel Monte Carlo for acoustic scattering from large deviation rough random surfaces,” arXiv preprint arXiv:2311.12565 (under review).

• R. Levie, W. Huang, L. Bucci, M. Bronstein, and G. Kutyniok, “Transferability of spectral graph convolutional neural networks,” Graph Representation Learning workshop, 33rd Conference on Neural Information Processing Systems, Vancouver, Canada

• R. Levie, W. Huang, L. Bucci, M. Bronstein, and G. Kutyniok, “Transferability of spectral graph convolutional neural networks,” The Journal of Machine Learning Research, vol. 22, no. 1, pp. 12 462–12 520.

• W. Huang and M. Multerer, “Isogeometric analysis of diffusion problems on random surfaces,” Applied Numerical Mathematics, vol. 179, pp. 50–65.

Talks and Posters

• Graph Representation Learning workshop, 33rd Conference on Neural Information Processing Systems, Vancouver, Canada; 2019, December; Transferability of Spectral Graph Convolutional Neural Networks.

• European Congress on Computational Methods in Applied Sciences and Engineering, Oslo, Norway; 2022, June; Isogeometric analysis of diffusion problems on random surfaces. certificate

• 11th International Conference on IsoGeometric Analysis, Lyon, France; 2023, June; Isogeometric analysis of diffusion problems on random surfaces. certificate

• European Conference on Numerical Mathematics and Advanced Applications conference, Lisbon, Portugal; 2023, September; Isogeometric analysis of Helmholtz problems with a roughly random scatterer. certificate

Other Activities

• CSCS summer school on Effective High-Performance Computing & Data Analytics with GPUs 2020, CSCS Swiss National Supercomputing Centre, Switzerland; 2020, July.

• The London Geometry and Machine Learning Summer School 2022 (LOGML), London, U.K; 2022, July; Helmhotlz-Hodge Laplacians: edge flows and simplicial learning, supervised by Prof. Stefan Schonsheck.

• The First Italian School in Geometric Deep Learning, Italy; 2022, July.