Machine Learning Research at IEMS

The IEMS department hosts a recurring meeting in which faculty present their work related to machine learning.


Brenna Argall and Michael Watson: April 25, 2024

Machine Learning in the Assistive/Rehab Field 

Brenna Argall 

How Operations and Supply Chain Executives Learn About AI/Machine Learning 

Michael Watson

Andreas Waechter: June 9, 2022

DNNs In Engineering Applications

Andreas Waechter

Julia Gaudio: March 31, 2022

Generalization Error

Julia Gaudio

Barry Nelson: February 3, 2022

Simulation Optimization and Bandits

Barry Nelson

David Morton: August 21, 2020

2020 Distributionally Robust Two-Stage Stochastic Programming

2019 Distributionally Robust Stochastic Dual Dynamic Programming

David Morton

Simge Kucukyavuz: July 24, 2020

Integer Programming for Learning Directed Acyclic Graphs from Continuous Data

Consistent Second-Order Conic Integer Programming for Learning Bayesian Network

On the Convexification of Constrained Quadratic Optimization Problems with Indicator Variables

Ideal Formulations for Constrained Convex Optimization Problems with Indicator Variables

A polyhedral approach to bisubmodular function minimization

Simge Kucukyavuz

Noshir Contractor: June 12, 2020

Moon 2024: Translating Research to Practice
A Successful Crew Composition Countermeasure Validated in HER

Noshir Contractor

Barry Nelson: May 22, 2020

Barry Nelson

Ed Malthouse and Diego Klabjan: April 17, 2020

An Algorithm for Allocating Sponsored Recommendations and Content: Unifying Programmatic Advertising and Recommender Systems

Multistakeholder Recommendation with Provider Constraints

Multistakeholder Recommender Systems Algorithm for Allocating Sponsored Recommendations

Ed Malthouse

Diego Klabjan: 2022

LUCK, MACHINE LEARNING AND IEMS: You do need all three of them

Diego Klabjan

Zhaoran Wang and Jorge Nocedal: February 19, 2020

Zhaoran Wang Jorge Nocedal