My name is Yu-Chia Chen and I am a 5th year PhD student advised by professor Marina Meilă at the University of Washington (UW). My research interests are in manifold learning, geometric data analysis and temporal graph modeling, with applications in Molecules dynamics simulation and Astronomy.
I interned in the Consumer Data and Analytics group at Microsoft during 2018 summer. We worked on modeling large scale temporal networks by dynamic stochastic block model and its extension to causal impact on dynamic social networks. The paper was accepted to KDD’19.
Please check out my CV here.
- Jul 2021 New paper on arXiv: The decomposition of the higher-order homology embedding constructed from the k-Laplacian.
- Jun 2021 New paper on ChemRxiv: Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants.
- Mar 2021 New paper on arXiv: Helmholtzian Eigenmap: Topological feature discovery & edge flow learning from point cloud data.
- Jun 2020 Machine learning internship at Facebook, Seattle, WA.
- Dec 2019 Presenting at NeurIPS’19, Vancouver, Canada (12/08/19 – 12/14/19).
- Nov 2019 Workshop paper Manifold coordinates with physical meaning accepted for poster presentation at the Machine Learning and the Physical Sciences (ML4PS) Workshop at NeurIPS’19.
- Sep 2019 Paper Selecting the independent coordinates of manifolds with large aspect ratios accepted for poster presentation at NeurIPS’19 (acceptance rate 21.2%).
- Sep 2019 Attending the IPAM long program – Machine Learning for Physics and the Physics of Learning at UCLA (09/04/19 – 12/08/19).
- Aug 2019 Presenting at KDD’19, Anchorage, AK (08/04/19 – 08/08/19).
- Jun 2019 New paper on arXiv: Selecting the independent coordinates of manifolds with large aspect ratios.
- Apr 2019 New paper On Dynamic Network Models and Application to Causal Impact accepted for poster presentation at the KDD’19 Research Track (acceptance rate 14.2%).