Advanced Hybrid Interpolation Based on Edge Direction Extracted from the DCT Coefficient Distribution. - Jae-Hun Kim; Kibaek Kim; Janghyeok Yun; Jechang Jeong; Gwanggil Jeon (SITIS, 2012)

Improved Residual DPCM for HEVC Lossless Coding. - Gwanggil Jeon; Kibaek Kim; Jechang Jeong (SIBGRAPI, 2014)

Improvement of Implicit Residual DPCM for HEVC. - Kibaek Kim; Gwanggil Jeon; Jechang Jeong (SITIS, 2014)

Adaptive Intra Prediction Mode Signaling Approach in H.264/AVC. - Kibaek Kim; Gwanggil Jeon; Jechang Jeong (IEICE Trans. Commun., 2014)

Piecewise DC prediction in HEVC. - Kibaek Kim; Gwanggil Jeon; Jechang Jeong (Signal Process. Image Commun., 2014)

A Two-Stage Stochastic Integer Programming Approach to Integrated Staffing and Scheduling with Application to Nurse Management. - Kibaek Kim; Sanjay Mehrotra (Oper. Res., 2015)

Data Centers as Dispatchable Loads to Harness Stranded Power. - Kibaek Kim; Fan Yang 015; Victor M. Zavala; Andrew A. Chien (CoRR, 2016)

A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties. - Victor M. Zavala; Kibaek Kim; Mihai Anitescu; John R. Birge (Oper. Res., 2017)

Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs. - Kibaek Kim; Victor M. Zavala (Math. Program. Comput., 2018)

An Asynchronous Bundle-Trust-Region Method for Dual Decomposition of Stochastic Mixed-Integer Programming. - Kibaek Kim; Cosmin G. Petra; Victor M. Zavala (SIAM J. Optim., 2019)

Differentially Private Federated Learning via Inexact ADMM. - Minseok Ryu; Kibaek Kim (CoRR, 2021)

Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition. - Youngdae Kim; François Pacaud; Kibaek Kim; Mihai Anitescu (CoRR, 2021)

A Reinforcement Learning Approach to Parameter Selection for Distributed Optimization in Power Systems. - Sihan Zeng; Alyssa Kody; Youngdae Kim; Kibaek Kim; Daniel K. Molzahn (CoRR, 2021)

A Bilevel Approach for Identifying the Worst Contingencies for Nonconvex Alternating Current Power Systems. - Brian C. Dandurand; Kibaek Kim; Sven Leyffer (SIAM J. Optim., 2021)

Accelerated Computation and Tracking of AC Optimal Power Flow Solutions Using GPUs. - Youngdae Kim; Kibaek Kim (ICPP Workshops, 2022)

APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning. - Minseok Ryu; Youngdae Kim; Kibaek Kim; Ravi K. Madduri (IPDPS Workshops, 2022)

Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates. - Minseok Ryu; Kibaek Kim (CoRR, 2022)

Scalable branching on dual decomposition of stochastic mixed-integer programming problems. - Kibaek Kim; Brian C. Dandurand (Math. Program. Comput., 2022)

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service. - Zilinghan Li; Shilan He; Pranshu Chaturvedi; Trung-Hieu Hoang; Minseok Ryu; Eliu A. Huerta; Volodymyr V. Kindratenko; Jordan D. Fuhrman; Maryellen L. Giger; Ryan Chard; Kibaek Kim; Ravi K. Madduri (e-Science, 2023)

Differentially Private Distributed Convex Optimization. - Minseok Ryu; Kibaek Kim (CoRR, 2023)

Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients. - Shourya Bose; Kibaek Kim (CoRR, 2023)

Privacy-Preserving Load Forecasting via Personalized Model Obfuscation. - Shourya Bose; Yu Zhang 005; Kibaek Kim (CoRR, 2023)

Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx. - Trung-Hieu Hoang; Jordan D. Fuhrman; Ravi K. Madduri; Miao Li; Pranshu Chaturvedi; Zilinghan Li; Kibaek Kim; Minseok Ryu; Ryan Chard; Eliu A. Huerta; Maryellen L. Giger (CoRR, 2023)

Efficient Communication in Federated Learning Using Floating-Point Lossy Compression. - Grant Wilkins; Sheng Di; Jon C. Calhoun; Kibaek Kim; Robert Underwood; Richard Mortier; Franck Cappello (CoRR, 2023)

Privacy-Preserving Federated Learning for Science: Challenges and Research Directions. - Kibaek Kim; Krishnan Raghavan; Olivera Kotevska; Matthieu Dorier; Ravi K. Madduri; Minseok Ryu; Todd S. Munson; Robert Ross; Thomas Flynn 001; Ai Kagawa; Byung-Jun Yoon; Christian Engelmann; et al. (IEEE Big Data, 2024)

GPU-Accelerated Sequential Quadratic Programming Algorithm for Solving ACOPF. - Bowen Li; Kibaek Kim (CDC, 2024)

FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications. - Grant Wilkins; Sheng Di; Jon C. Calhoun; Zilinghan Li; Kibaek Kim; Robert Underwood; Richard Mortier; Franck Cappello (ICDCS, 2024)

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler. - Zilinghan Li; Pranshu Chaturvedi; Shilan He; Han Chen; Gagandeep Singh; Volodymyr V. Kindratenko; Eliu A. Huerta; Kibaek Kim; Ravi K. Madduri (ICLR, 2024)

QCQP-Net: Reliably learning feasible alternating current optimal power flow solutions under constraints. - Sihan Zeng; Youngdae Kim; Yuxuan Ren; Kibaek Kim (L4DC, 2024)

SparseLLM: Towards Global Pruning of Pre-trained Language Models. - Guangji Bai; Yijiang Li; Chen Ling 003; Kibaek Kim; Liang Zhao 002 (NeurIPS, 2024)

Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System. - Ravi K. Madduri; Zilinghan Li; Tarak Nandi; Kibaek Kim; Minseok Ryu; Alex Rodriguez (TPS-ISA, 2024)

Gradient-Free Adaptive Global Pruning for Pre-trained Language Models. - Guangji Bai; Yijiang Li; Chen Ling 003; Kibaek Kim; Liang Zhao 002 (CoRR, 2024)

Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers. - Shourya Bose; Yu Zhang 005; Kibaek Kim (CoRR, 2024)

Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays. - Charikleia Iakovidou; Kibaek Kim (CoRR, 2024)

A Perspective on Foundation Models for the Electric Power Grid. - Hendrik F. Hamann; Thomas Brunschwiler; Blazhe Gjorgiev; Leonardo S. A. Martins; Alban Puech; Anna Varbella; Jonas Weiss; Juan Bernabé-Moreno; Alexandre Blondin Massé; Seong Choi; Ian T. Foster; Bri-Mathias S. Hodge; et al. (CoRR, 2024)

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework. - Zilinghan Li; Shilan He; Ze Yang; Minseok Ryu; Kibaek Kim; Ravi K. Madduri (CoRR, 2024)

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption. - Shourya Bose; Yijiang Li; Amy Van Sant; Yu Zhang; Kibaek Kim (CoRR, 2024)

Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources: A Case Study on Federated Fine-Tuning of LLaMA 2. - Zilinghan Li; Shilan He; Pranshu Chaturvedi; Volodymyr V. Kindratenko; Eliu A. Huerta; Kibaek Kim; Ravi K. Madduri (Comput. Sci. Eng., 2024)

Distributionally Robust Decentralized Volt-Var Control With Network Reconfiguration. - Geunyeong Byeon; Kibaek Kim (IEEE Trans. Smart Grid, 2024)

A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems. - Minseok Ryu; Geunyeong Byeon; Kibaek Kim (IPDPS, 2025)

FedSpaLLM: Federated Pruning of Large Language Models. - Guangji Bai; Yijiang Li; Zilinghan Li; Liang Zhao 002; Kibaek Kim (NAACL (Long Papers), 2025)

Two-Stage Distributionally Robust Conic Linear Programming over 1-Wasserstein Balls. - Geunyeong Byeon; Kaiwen Fang; Kibaek Kim (SIAM J. Optim., 2025)

Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction. - Anh Van Nguyen; Diego Klabjan; Minseok Ryu; Kibaek Kim; Zichao Di (CoRR, 2025)

A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning. - Yuyang Qiu; Kibaek Kim; Farzad Yousefian (CoRR, 2025)

FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud. - Aditya Sinha; Zilinghan Li; Tingkai Liu; Volodymyr V. Kindratenko; Kibaek Kim; Ravi K. Madduri (CoRR, 2025)