Παρακολούθηση
Kibaek Kim
Kibaek Kim
Argonne National Laboratory
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα anl.gov - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
A Two-Stage Stochastic Integer Programming Approach to Integrated Staffing and Scheduling with Application to Nurse Management
K Kim, S Mehrotra
Operations Research 63 (6), 1431-1451, 2015
1812015
A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties
VM Zavala, K Kim, M Anitescu, J Birge
Operations Research 65 (3), 557-576, 2017
1062017
Data Centers as Dispatchable Loads to Harness Stranded Power
K Kim, F Yang, VM Zavala, AA Chien
IEEE Transactions on Sustainable Energy 8 (1), 208-218, 2017
902017
Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs
K Kim, VM Zavala
Mathematical Programming Computation 10 (2), 225-266, 2018
762018
Graph convolutional neural networks for optimal load shedding under line contingency
C Kim, K Kim, P Balaprakash, M Anitescu
2019 ieee power & energy society general meeting (pesgm), 1-5, 2019
682019
Temporal Decomposition for Improved Unit Commitment in Power System Production Cost Modeling
K Kim, A Botterud, F Qiu
IEEE Transactions on Power Systems 33 (5), 5276-5287, 2018
612018
A Graph-Based Computational Framework for Simulation and Optimization of Coupled Infrastructure Networks
J Jalving, S Abhyankar, K Kim, M Hereld, VM Zavala
IET Generation, Transmission & Distribution 11 (12), 3163-3176, 2017
412017
An Asynchronous Bundle-Trust-Region Method for Dual Decomposition of Stochastic Mixed-Integer Programming
K Kim, CG Petra, VM Zavala
SIAM Journal on Optimization 29 (1), 318-342, 2019
372019
Predicting patient volumes in hospital medicine: A comparative study of different time series forecasting methods
K Kim, C Lee, K O’Leary, S Rosenauer, S Mehrotra
Northwestern University, Illinois, USA, Scientific Report, 2014
37*2014
A reinforcement learning approach to parameter selection for distributed optimal power flow
S Zeng, A Kody, Y Kim, K Kim, DK Molzahn
Electric Power Systems Research 212, 108546, 2022
32*2022
APPFL: open-source software framework for privacy-preserving federated learning
M Ryu, Y Kim, K Kim, RK Madduri
2022 IEEE International Parallel and Distributed Processing Symposium …, 2022
312022
A privacy-preserving distributed control of optimal power flow
M Ryu, K Kim
IEEE Transactions on Power Systems 37 (3), 2042-2051, 2021
232021
Accelerated computation and tracking of AC optimal power flow solutions using GPUs
Y Kim, K Kim
Workshop Proceedings of the 51st International Conference on Parallel …, 2022
182022
Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition
Y Kim, F Pacaud, K Kim, M Anitescu
arXiv preprint arXiv:2106.14995, 2021
182021
Using optimization models to demonstrate the need for structural changes in training programs for surgical medical residents
J Turner, K Kim, S Mehrotra, DA DaRosa, MS Daskin, HE Rodriguez
Health care management science 16, 217-227, 2013
172013
Differentially private federated learning via inexact ADMM with multiple local updates
M Ryu, K Kim
arXiv preprint arXiv:2202.09409, 2022
152022
Scalable Branching on Dual Decomposition of Stochastic Mixed-Integer Programming Problems
K Kim, B Dandurand
Mathematical Programming Computation 14, 1-41, 2022
152022
Outcome based state budget allocation for diabetes prevention programs using multi-criteria optimization with robust weights
S Mehrotra, K Kim
Health care management science 14, 324-337, 2011
142011
Incorporating Prioritization in Critical Infrastructure Security and Resilience Programs
D Verner, F Petit, K Kim
The Journal of the NPS Center for Homeland Defense and Security, 2017
132017
APPFLx: Providing privacy-preserving cross-silo federated learning as a service
Z Li, S He, P Chaturvedi, TH Hoang, M Ryu, EA Huerta, V Kindratenko, ...
2023 IEEE 19th International Conference on e-Science (e-Science), 1-4, 2023
112023
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