IEOR researchers investigate the latest mathematical tools, approaches, and methodologies to make new theoretical discoveries and innovations that touch nearly every industry, making them more efficient and profitable in areas such as supply chain, logistics, manufacturing, data science, energy systems, robotics, and management.
Alper Atamturk, Andres Gomez and Shaoning Han. “Sparse and Smooth Signal Estimation: Convexification of L0 Formulations”. Journal of Machine Learning Research.
KAMINSKY AND S. LIU. 2018.A Two-Step Gradient Estimation Approach to for Setting Supply Chain Operating Parameters. Computers & Operations Research 92, pp. 98-110
Barna Saha, Aravind Srinivasan. “A new approximation technique for resource‐allocation problems“. Random Structure & Algorithms
Somayeh Sojoudi, Salar Fattahi and Javad Lavaei, Convexification of Generalized Network Flow Problem, to appear in Mathematical Programming, pp. 1-39, 2017.
Ming Jin, Javad Lavaei, and Karl Henrik Johansson, Power Grid AC-based State Estimation: Vulnerability Analysis Against Cyber Attacks, to appear in IEEE Transactions on Automatic Control, 2018.
Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial
M. Zhou, Y. Fukuoka, Y. Mintz, K. Goldberg, P. Kaminsky, E. Flowers, and A. Aswani (2018), Evaluating machine learning-based automated personalized daily step goals delivered through a mobile phone app: Randomized controlled trial, JMIR Mhealth & Uhealth, vol. 6, no. 1: e28.