Machine Learning Research at IEMS

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


AUGUST 21, 2020: David Morton

David MortonDistributionally Robust Two-Stage Stochastic Programming
Distributionally Robust Stochastic Dual Dynamic Programming








JULY 24, 2020: Simge Kucukyavuz

Simge Kucukyavuz

Integer Programming for Learning Directed Acyclic Graphs from Continuous Data
Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks
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




JUNE 12, 2020: Noshir Contractor

Noshir Contractor


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





May 22, 2020: Barry Nelson

Barry Nelson


April 17, 2020: Ed Malthouse and Diego Klabjan

 Ed MalthouseAn Algorithm for Allocating Sponsored Recommendations and
Content: Unifying Programmatic Advertising and Recommender
Multistakeholder Recommendation with Provider Constraints
A Multistakeholder Recommender Systems Algorithm for
Allocating Sponsored Recommendations






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







FebRUARY 19, 2020: Zhaoran Wang and Jorge Nocedal

 Zhaoran Wang

Jorge Nocedal