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.
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.
M. Olfat and A. Aswani (2018), Spectral algorithms for computing fair support vector machines, In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS): 1933-1942.
Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg. “DART: Noise Injection for Robust Imitation Learning“. 1st Conference on Robot Learning (CoRL). Mt. View, CA. Nov 2017. Proceedings of Machine Learning Research volume 78