I am a research scientist at JPMorgan AI Research. My research interest lies in reinforcement learning, multi-agent system, human behavior models, and graph generative models. Currently I am developing RL models for sub-rational human traders.
I obtained my Ph.D. degree in Computer Science and Engineering from University at Buffalo, advised by Prof. A. Erdem Sarıyüce. My Ph.D. research mainly focuses on on graph learning, graph generative model, and dense subgraph discovery.
Publications
- Using Motif Transitions for Temporal Graph Generation
Penghang Liu, A. Erdem Sarıyüce
KDD 2023 - ACM SIGKDD Conference on Knowledge Discovery and Data Mining - Biased or Limited: Modeling Sub-Rational Human Investors in Financial Markets
Penghang Liu, Kshama Dwarakanath, Svitlana S Vyetrenko
ICAIF 2022 - Workshop on Machine Learning for Investor Modelling - Temporal motifs in patent opposition and collaboration networks
Penghang Liu, Naoki Masuda, Tomomi Kito, A. Erdem Sarıyüce
Scientific Reports, 12, 1917 (2022) - Temporal Network Motifs: Models, Limitations, Evaluation
Penghang Liu, Valerio Guarrasi, A. Erdem Sarıyüce
TKDE - IEEE Transactions on Knowledge and Data Engineering - Characterizing and Utilizing the Interplay Between Core and Truss Decompositions
Penghang Liu, A. Erdem Sarıyüce
BigData 2020 - IEEE International Conference on Big Data - Analysis of Core and Truss Decomposition on Real-World Networks
Penghang Liu, A. Erdem Sarıyüce
MLG 2019 (in conj. with SIGKDD'19) - International Workshop on Mining and Learning with Graphs - Socio-spatial Networks, Multilingualism, and Language Use in a Rural African Context
Pierpaolo Di Carlo, Jeff Good, Ling Bian, Yujia Pan, Penghang Liu
COSIT 2017 - International Conference on Spatial Information Theory
Awards
- Student Travel Grant, SIGKDD 2019