J.P. Morgan

FACULTY AWARD RECIPIENTS 2019

LEARNING FROM EXPERIENCE

Robo-Advising as a Symbiotic Human- Machine System

Agostino Capponi
Industrial Engineering and Operations Research Department and Data Science Institute

J.P. Morgan Contact:
Tucker Balch, Naren Chittar

Agents supporting large-scale environments of teams of people and computer systems

Sarit Kraus
Department of Computer Science

J.P. Morgan Contact:
Manuela Veloso, Andy Alexander

Learning and Explaining the Differences Between Novice and Expert Data Analysts

Stephanie Rosenthal
Courtesy Faculty, Robotics Institute
Reid Simmons
Robotics Institute

J.P. Morgan Contact:
Prashant Reddy

A Unified Library for Distributed Reinforcement Learning

Ion Stoica
EECS Department

J.P. Morgan Contact:
Prashant Reddy, Oleg Rasskazov

Scalable Language-Guided Deep Reinforcement Learning

Sergey Levine
Department of Electrical Engineering and Computer Science

J.P. Morgan Contact:
Sumitra Ganesh, Reza Amini

Machine Learning from Human Instruction: Every Person a Programmer

Tom M. Mitchell
Machine Learning Department, School of Computer Science
Brad Myers
Institute for Software Research, School of Computer Science

J.P. Morgan Contact:
Sumitra Ganesh, Denis Kochedykov

Risk Management of Investment Strategies: An Agent-based Approach

Anisoara Calinescu
Department of Computer Science
Doyne Farmer
Mathematical Sciences Institute
Michael Wooldridge
Department of Computer Science

J.P. Morgan Contact:
Manuela Veloso, Naren Chittar

Cooperative Multi-Agent Reinforcement Learning

Shimon Whiteson
Department of Computer Science

J.P. Morgan Contact:
Prashant Reddy, Vacslav Glukhov

Automated Machine Learning for Time Series Analysis

Yolanda Gil
Information Sciences Institute
Deborah Khider
Information Sciences Institute

J.P. Morgan Contact:
Sumitra Ganesh

EXPLAINABILITY & INTERPRETABILITY

Explaining Decisions of Machine Learning Systems

Adnan Darwiche
Computer Science Department

J.P. Morgan Contact:
Jiahao Chen, Virgile Mison

Prediction semantics and interpretations that are grounded in real data

Daniel Hsu
Computer Science Department & Data Science Institute

J.P. Morgan Contact:
Sumitra Ganesh

Human-Aware AI Assistants for Interactive Decision Support in Finance

Subbarao Kambhampati
School of Computing, Informatics, and Decision Systems Engineering

J.P. Morgan Contact:
Tucker Balch, Andy Alexander

Efficient Formal Safety Analysis of Neural Networks

Suman Jana
Department of Computer Science
Jeannette M. Wing
Data Science Institute, Department of Computer Science
Junfeng Yang
Department of Computer Science

J.P. Morgan Contact:
Sumitra Ganesh, Karthik Krishnaiyengar

Illuminating Black-Box Models in Machine Learning

Ameet Talwalkar
Machine Learning Department, School of Computer Science

J.P. Morgan Contact:
Tucker Balch, Baranidharan Mohan

Compression Algorithms for Resilient AI

J.P. Morgan Contact:
Tucker Balch, Baranidharan Mohan

Summarizing Agent Behavior to People

Ofra Amir
Faculty of Industrial Engineering & Management
David Sarne
Department of Computer Science and Technology
Finale Doshi-Velez
John Paulson School of Engineering and Applied Sciences

J.P. Morgan Contact:
Gilad Asharov, Naftali Cohen, Hua Zheng

Interpretable Knowledge Reasoning and Extraction for Equity Investment

Xiang Ren
Department of Computer Science

J.P. Morgan Contact:
Sumitra Ganesh

Interpretable machine learning by incorporating human knowledge; Adversarial attack and defense on image classification; Adversarial attacks and defense on graph data

Ning Chen
Beijing National Research Center for Information Science and Technology
Hang Su
Department of Computer Science and Technology
Jun Zhu
Department of Computer Science and Technology

J.P. Morgan Contact:
Manuela Veloso, Lin Zhu

“Optimizer, explain thyself!”: Making Bayesian Optimization Interpretable

Ryan P. Adams
The Computer Science Department

J.P. Morgan Contact:
Gilad Asharov, Naftali Cohen, Hua Zheng

Post-hoc Prediction Interpretation of Deep Learning in Finance Applications

Xia “Ben” Hu
Department of Computer Science & Engineering, College of Engineering

J.P. Morgan Contact:
Tucker Balch

Contextual Model Interpretation

Xu Chu
College of Computing

J.P. Morgan Contact:
Prashant Reddy

DATA & CRYPTOGRAPHY

Teaching the Machine to Solve Matching Problems

Avigdor Gal
Faculty of Industrial Engineering & Management

J.P. Morgan Contact:
Gilad Asharov, Naftali Cohen, Jeff Kessler

Exploring Use Cases for JP Morgan ROAR in Academic Teaching and Research

Kay Giesecke
Advanced Financial Technologies Laboratory
Department of Management Science and Engineering

J.P. Morgan Contact:
Antigoni Polychroniadou, Peter Cotton

Energy-Efficient Algorithms for AI and Data Science

J.P. Morgan Contact:
Prashant Reddy

SIENA: Securing AI Computing Environment for AWS

Rafail Ostrovsky
Computer Science Department

J.P. Morgan Contact:
Antigoni Polychroniadou, Rusty Conover

Generation X: Learning and Inversion of Generative Networks for Tabular Data

John Lafferty
Department of Statistics and Data Science

J.P. Morgan Contact:
Jiahao Chen, Lin Zhu

Secure Multi-Party Computation for Privacy Preserving Data Mining

Vipul Goyal
Computer Science Department and Cylab

J.P. Morgan Contact:
Gilad Asharov, Rusty Conover

 

ETHICS & FAIRNESS

Voting-Based Methods for Explainable AI  

Ariel D. Procaccia
Computer Science Department

J.P. Morgan Contact:
Jiahao Chen

Robustness and Fairness in Policy Learning from Observational Data

Nathan Kallus
Cornell Tech

J.P. Morgan Contact:
Jiahao Chen

Preventing Unfair Discrimination in Interactive Learning

Zhiwei Steven Wu
Department of Computer Science & Engineering

J.P. Morgan Contact:
Jiahao Chen

OTHER

A Scalable Approach to Correcting Failures of AI Systems in the Real World

Joydeep Biswas
College of Information and Computer Sciences

J.P. Morgan Contact:
Tucker Balch, Jeff Kessler

Methods to Identify Communities and Trading Behavior Over Financial Data Streams

Furong Huang
Department of Computer Science and UMIACS
Louiqa Raschid
Department of Management Science and Engineering
Alberto Rossi
Smith School of Business

J.P. Morgan Contact:
Prashant Reddy

Dynamics, Control and Uncertainty Quantification for Stable Machine Learning Algorithms

Nikolas Kantas
Statistics Section, Department of Mathematics
Panos Parpas
Computational Optimization Group, Department of Computing
Grigorios A. Pavliotis
Applied Mathematics and Mathematical Physics Section, Department of Mathematics

J.P. Morgan Contact:
Manuela Veloso, Lin Zhu

 

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