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 system, energy systems, robotics, and management.
T. Altun, R. Madani, A. Atamtürk, R.Baldick, A. Davoudi. Enhanced Modeling of Contingency Response in Security-constrained Optimal Power Flow. BCOL Research Report 21.01, IEOR, University of California-Berkeley.
Deep Learning Can Significantly Accelerate Grasp-Optimized Motion Planning. Jeffrey Ichnowski, Yahav Avigal, Vishal Satish, Ken Goldberg. Science Robotics Journal. V5(48), 18 Nov 2020. [Paper] [Video].
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects Mike Danielczuk*, Ashwin Balakrishna*, Daniel Brown, Shivin Devgon, Ken Goldberg. Conference on Robot Learning (CoRL), Online MIT, Cambridge, MA, Nov 2020. [Paper] [5min Video] [Website].
Untangling Dense Knots by Learning Task-Relevant Keypoints. Jennifer Grannen*, Priya Sundaresan*, Brijen Thananjeyan, Jeff Ichnowski, Ashwin Balakrishna, Vainavi Viswanath, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg. Conference on Robot Learning (CoRL), Online MIT, Cambridge, MA, Nov 2020. [Paper] [5min Video] [Website]
Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow
Julie Mulvaney-Kemp, Salar Fattahi, and Javad Lavaei, Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow, conditionally accepted for IEEE Transactions on Control of Network Systems, 2020.
Molybog, Ramtin Madani, and Javad Lavaei. Conic Optimization for Quadratic Regression Under Sparse Noise. Journal of Machine Learning Research. https://www.jmlr.org/papers/v21/18-881.html.