Faculty Research Awards

Makerere University, Generating Synthetic Datasets for Mobile Money Transactions for AI Research

Stanford University, ROLAND 2.0: Graph Representation Learning for Transaction Networks

Harvard University, Policies for Supervisory Audits: A Security Games and Bandit approach

Purdue University, Neural Modeling of Network Dynamics for Anomaly Detection and Recommendation in Financial Systems

Bar-Ilan University, Scaling Secure Computation for Data Centers

Carnegie Mellon University, Producing Privacy-Preserving, Synthetic Time Series Datasets with Generative Adversarial Networks

University of Southern California, Time Series Synthesization: Models, Evaluation Metrics and Benchmark Dataset

University of California, Berkeley, Interpretable machine learning methods for financial analytics

Hebrew University of Jerusalem, Scaling Secure Computation for Data Centers

Carnegie Mellon University, Distributed Data Structures for Federated Learning

University of California, Los Angeles, CEDRIC: seCurE anD pRIvate Computation

Massachusetts Institute of Technology, Auditable Debiased Decision Making

University of California, Berkeley, PrivShare: Privacy-preserving Data Analysis over Multiple Data Sources

Columbia University, Decoding Economic Trends with Human-in-the-loop Machine Perception

Sapienza University of Rome, Understanding interdependent market dynamics: vulnerabilities and opportunities

Universidad Carlos III de Madrid, Adversarial Reinforcement Learning: Avoiding Malicious Behaviours

Stanford University, Rapid and Robust Adaptation to Temporal Distribution Shift

Imperial College London, Secure and Self-Optimizing Distributed Inference

Cornell University, Offline Reinforcement Learning: Efficiency, Safety, Transparency, and Fairness

University of California, Berkeley, Offline Reinforcement Learning in Multi-Agent Networks: Smart Decisions from Logged Data

University of Liverpool, Robust Trading via Multi-Agent Adversarial Reinforcement Learning

University of Texas at Austin, Learning Optimizers Made Adaptable and Applicable to Multi-Agent Systems

Carnegie Mellon University, Diderot: Building the Next Generation Education Platform

Sapienza University of Rome, Resilience-based Generalized Planning and Strategic Reasoning

Arizona State University, Automated Extraction and Execution Support for Cognitive Workflows in Finance

University of Southern California, A Table Understanding Approach to Improving Quantitative Cognitive Workflows

Stanford University, Learning and Leveraging Representations in Repeated Multi-Agent Interactions

King’s College London, MIG: Migrating

University of California, Santa Barbara, OpenFinQA: Open Financial Question Answering via Tables and Text

Georgia Tech, Scalable Modeling of Financial Documents for Improved Decision-Making

Cornell University, Exploiting Low Rank Structure for Provably Efficient Reinforcement Learning

Columbia University, Causal Reinforcement Learning for Optimal and Personalized Decision-Making

Bar-Ilan University, Meta-agents for human-agent collaboration

University of Virginia, Usable, Interpretable, and Fair Causal Effects Learning for Financial Applications

University of Victoria, Attention pays: learning a structured model of client interest for improved financial product recommendations

Addis Ababa Institute of Technology, Exploring AI and Machine Learning Technologies to track Policy Compliance of Highly Trusted Parties (HTPs) and incidents of Fraud at selected banking Intuitions in Ethiopia

IIIT Hyderabad, Applied Semantics Extraction and Analytics over Banking Documents

University of Pennsylvania, Private Federated Analytics with Efficient Key Management

Cornell University, Improving Tech Abuse Interventions with IPV Survivors

University of Maryland, Joint Fairness and Privacy Design for Financial Machine Learning Algorithms

New York University, Nutritional Labels for Financial Products and Credit Decisions: Strengthening Accountability Through Public Disclosure

Rice University, Improving Fairness and Interpretability of AI Systems through Minipatch Learning

Rice University, Multi-Aspect Interpretation Framework for Understanding AI Models on Financial Adverse Actions

Purdue University, Testing and Improving the Fairness and Correctness of AI Systems: A Variance Perspective