About
 

Jie Chen

Research Staff Member

Google Scholar Website

Who they work with

Categories

AI in Finance Graph Deep Learning Molecular design Statistics Time Series

Jie Chen is a senior research scientist and manager at the MIT-IBM Watson AI Lab, IBM Research. He received the B.S. degree in mathematics with honors from Zhejiang University and the Ph.D. degree in computer science from the University of Minnesota. His research spans a broad spectrum of disciplines, including machine learning, statistics, scientific computing, and parallel processing, with results published in prestigious journals and conferences in the respective fields. His interests include graph-based deep learning, kernel methods, dimension reduction, Gaussian processes, matrix functions, preconditioning, graph partitioning, and tensor approximations. He directs research projects that integrate scientific merits with practical values in industry and business, covering sectors including finance, energy, and materials; and supported by various member companies of the lab as well as the U.S. Department of Energy. He was a recipient of SIAM Student Paper Prize in 2009, a plenary speaker at the 2017 International Conference on Preconditioning Techniques for Scientific and Industrial Applications, and a recipient of IBM Outstanding Technical Achievement Award in 2018.

Top Work

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Graph Deep Learning

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

AI in Finance

Scalable Graph Learning for Anti-Money Laundering: A First Look

Scalable Graph Learning for Anti-Money Laundering: A First Look

AI in Finance

Publications with the MIT-IBM Watson AI Lab

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
ICML
 
GC-Flow: A Graph-Based Flow Network for Effective Clustering
GC-Flow: A Graph-Based Flow Network for Effective Clustering
ICML
 
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
ICML
 
A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
ICML
 
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
ICLR
 
DAG-GNN: DAG Structure Learning with Graph Neural Networks
DAG-GNN: DAG Structure Learning with Graph Neural Networks
ICML
 
Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization
Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization
AAAI
 
Data-Efficient Graph Grammar Learning for Molecular Generation
Data-Efficient Graph Grammar Learning for Molecular Generation
ICLR
 
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
ICLR
 
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
NeurIPS
 
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models
Machine LearningSocial Networks
 
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
AAAIMachine Learning
 
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Discrete Graph Structure Learning for Forecasting Multiple Time Series
ICLRTime Series
 
Directed Acyclic Graph Neural Networks
Directed Acyclic Graph Neural Networks
ICLRGraph Deep Learning
 
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
AI in FinanceGraph Deep Learning
 
Online AI planning with graph neural networks and adaptive scheduling
Online AI planning with graph neural networks and adaptive scheduling
Automated PlanningGraph Deep Learning
 
CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator
CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator
AAAI
 
A content-aware attack generator for AI cybersecurity
A content-aware attack generator for AI cybersecurity
Adversarial Machine LearningCybersecurity
 
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Graph Deep LearningAAAI
 
Embedding Compression with Isotropic Iterative Quantization
Embedding Compression with Isotropic Iterative Quantization
Natural Language Processing
 
A Sequential Set Generation Method for Predicting Set-Valued Outputs
A Sequential Set Generation Method for Predicting Set-Valued Outputs
AAAI
 
Scalable Graph Learning for Anti-Money Laundering: A First Look
Scalable Graph Learning for Anti-Money Laundering: A First Look
AI in FinanceGraph Deep Learning
 
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Generative ModelsGraph Deep Learning