Tom Goldstein
University of Maryland, College Park, Robust, Private and Fair ML for Financial Models
A. Erdem Sariyuce
University at Buffalo, Detecting Fraudulent Transactions in Online Marketplaces Using Temporal Network Motifs
Austin R Benson
Cornell University, Pattern-based Heterogeneous Graph Clustering at Scale
Furong Huang
University of Maryland, College Park, Robust, Private and Fair ML for Financial Models
Rok Sosic
Stanford University, ROLAND: Representation Learning and Anomaly Detection in Financial Networks
Leandros Tassiulas
Yale University, Distributed Ledgers for Enhancing the Trust and Performance of Financial Networks
Naoki Masuda
University at Buffalo, Detecting Fraudulent Transactions in Online Marketplaces Using Temporal Network Motifs
Jure Leskovec
Stanford University, ROLAND: Representation Learning and Anomaly Detection in Financial Networks
Fabio Caccioli
University College London, Network Methods for the Generation of Synthetic Datasets
Huijia Lin
University of Washington, Secure Data Analytics with a Single Untrusted Server
Tal Malkin
Columbia University, MAGIC: Machine Learning Through a Cryptographic Len
Elaine Shi
Cornell University, CryptML: Cryptographic Machine Learning
Rafail Ostrovsky
UCLA, SECURE: SEcure CompuUtation for fRaud dEtection
Rafael Pass
Cornell University, CryptML: Cryptographic Machine Learning
Daniela Rus
MIT CSAIL, Secure Private Computing Using Coresets
Giulia Fanti
Carnegie Mellon University, Producing Privacy-Preserving, Synthetic Time Series Datasets with Generative Adversarial Networks
Yan Liu
University of Southern California, HR-Neural ODE: Multivariate Multiresolution Time Series Synthesizer via Neural Ordinal Differential Equations
Yarin Gal
University of Oxford, Uncertainty Aware Data-driven Generative Models and Multi-agent Simulators
Stefano Tessaro
University of Washington, Secure Data Analytics with a Single Untrusted Server
Rachel Cummings
Georgia Tech, Differentially Private Synthetic Data Generation
Vyas Sekar
Carnegie Mellon University, Producing Privacy-Preserving, Synthetic Time Series Datasets with Generative Adversarial Networks
Henry Lam
Columbia University, Calibrating Large-Scale Simulation Models via Distributionally Robust Optimization
Michael Wellman
University of Michigan, Multiagent Modeling of the Financial Payments System
Michael Barr
University of Michigan, Multiagent Modeling of the Financial Payments System
Gabriel Rauterberg
University of Michigan, Multiagent Modeling of the Financial Payments System
Chelsea Finn
Stanford University, Continuous Meta-Reinforcement Learning in Non-Stationary Environments
Michael Wooldridge
Oxford University, Opponent Modeling in Adaptive Markets
Fernando Fernández
Universidad Carlos III de Madrid, Learning Similarity Metrics Between Simulation and the Real World
Sarit Kraus
Bar-Ilan University, Agents Supporting Large-scale Environments of Teams of People and Computer Systems –Y2
Sergey Levine
UC Berkeley, Multi-Agent Modeling with Inverse RL and POMDP Models
Uday Rajan
University of Michigan, Multiagent Modeling of the Financial Payments System
Yun Fu
Northeastern University, Reinforced Graph-Structured Expert Model for Business Intelligence
Reid Simmons
Carnegie Mellon University, Timely Suggestions for Improving Data Analyst Cognitive Workflows
William Yang Wang
UC Santa Barbara, Combining Knowledge Base and Unstructured Text for Open-Domain Financial Question Answering
Craig Knoblock
University of Southern California, Supporting Cognitive Workflows with Hybrid Knowledge Graphs
Kamalakar Karlapalem
IIIT Hyderabad, Guided Discovery of Cognitive Steps within a Task
Jay Pujara
University of Southern California, Supporting Cognitive Workflows with Hybrid Knowledge Graphs
Stephanie Rosenthal
Carnegie Mellon University, Timely Suggestions for Improving Data Analyst Cognitive Workflows