Temple University, Philadelphia, PA 19122
I am a tenure-track Assistant Professor in Department of Computer and Information Sciences at Temple University. I am also a Visiting Research Collaborator in Department of Electrical and Computer Engineering at Princeton University. Before joining Temple University, I worked as a postdoctoral scholar, advised by H. Vincent Poor, in Department of Electrical and Computer Engineering at Princeton University. I received my Ph.D. in Statistics from Southern Methodist University advised by Hon Keung Tony Ng and Yulia R. Gel.
My research interests are machine learning, deep learning, graph mining, topological data analysis, reliability theory, nonparametric statistics, and their applications. For more details, see my CV.
I am looking for highly motivated and self-driven students NOW, who are interested in machine learning, deep learning on graphs, data mining, and topological and geometric methods in statistics. If you are interested, please contact me at firstname.lastname@example.org. Include your CV and brief highlights of ML/DL/Statistics-related projects.
|Aug 9, 2023||Honored to receive the research grant from NSF for the project Collaborative Research: Planning: FIRE-PLAN: Advancing Wildland Fire Analytics for Actuarial Applications and Beyond!|
|Jun 7, 2023||One paper was accepted to ECML-PKDD 2023!|
|Mar 18, 2023||Our workshop Data Mining for Climate Change and Health Equity (DMC2HE) was accepted by ICDM 2023!|
NeurIPSTime Dimension Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series ForecastingIn Advances in Neural Information Processing Systems 2022
ECML-PKDDTopoAttn-Nets: Topological Attention in Graph Representation LearningIn European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022
ICLRTAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series ForecastingIn International Conference on Learning Representations 2022
AAAIBScNets: Block Simplicial Complex Neural NetworksIn Proceedings of the AAAI Conference on Artificial Intelligence 2022
NeurIPSTopological Relational Learning on GraphsAdvances in Neural Information Processing Systems 2021