Creation of a decision-support methodology for selecting more-electric aircraft subsystem technologies. - Jeremie Craisse; Simon Kruger; Young Jin Kim 006; Imon Chakraborty; Simon Briceno; Yongchang Li; Elena Garcia; Dimitri N. Mavris (SysCon, 2016)

Time-parallel simulation of air traffic networks. - Young Jin Kim 006; Dimitri Mavris; Richard Fujimoto (WSC, 2017)

A deep learning and parallel simulation methodology for air traffic management. - Young Jin Kim 006 (2018)

From Research to Production and Back: Ludicrously Fast Neural Machine Translation. - Young Jin Kim 006; Marcin Junczys-Dowmunt; Hany Hassan; Alham Fikri Aji; Kenneth Heafield; Roman Grundkiewicz; Nikolay Bogoychev (NGT@EMNLP-IJCNLP, 2019)

Time- and space-parallel simulation of air traffic networks. - Young Jin Kim 006; Dimitri Mavris; Richard Fujimoto (Simul., 2019)

FastFormers: Highly Efficient Transformer Models for Natural Language Understanding. - Young Jin Kim 006; Hany Hassan Awadalla (CoRR, 2020)

Scalable and Efficient MoE Training for Multitask Multilingual Models. - Young Jin Kim 006; Ammar Ahmad Awan; Alexandre Muzio; Andrés Felipe Cruz-Salinas; Liyang Lu; Amr Hendy; Samyam Rajbhandari; Yuxiong He; Hany Hassan Awadalla (CoRR, 2021)

Fast Vocabulary Projection Method via Clustering for Multilingual Machine Translation on GPU. - Hossam Amer; Mohamed Afify; Young Jin Kim 006; Hitokazu Matsushita; Hany Hassan (AMTA, 2022)

Taming Sparsely Activated Transformer with Stochastic Experts. - Simiao Zuo; Xiaodong Liu 003; Jian Jiao 007; Young Jin Kim 006; Hany Hassan; Ruofei Zhang; Jianfeng Gao 001; Tuo Zhao (ICLR, 2022)

Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers. - Rui Liu 013; Young Jin Kim 006; Alexandre Muzio; Hany Hassan (ICML, 2022)

Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers. - Rui Liu 013; Young Jin Kim 006; Alexandre Muzio; Barzan Mozafari; Hany Hassan Awadalla (CoRR, 2022)

AutoMoE: Neural Architecture Search for Efficient Sparsely Activated Transformers. - Ganesh Jawahar; Subhabrata Mukherjee; Xiaodong Liu 003; Young Jin Kim 006; Muhammad Abdul-Mageed; Laks V. S. Lakshmanan; Ahmed Hassan Awadallah; Sébastien Bubeck; Jianfeng Gao 001 (CoRR, 2022)

Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production. - Young Jin Kim 006; Rawn Henry; Raffy Fahim; Hany Hassan Awadalla (CoRR, 2022)

AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation. - Ganesh Jawahar; Subhabrata Mukherjee; Xiaodong Liu 003; Young Jin Kim 006; Muhammad Abdul-Mageed; Laks V. S. Lakshmanan; Ahmed Hassan Awadallah; Sébastien Bubeck; Jianfeng Gao 001 (ACL (Findings), 2023)

How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation. - Amr Hendy; Mohamed Abdelrehim; Amr Sharaf; Vikas Raunak; Mohamed Gabr; Hitokazu Matsushita; Young Jin Kim 006; Mohamed Afify; Hany Hassan Awadalla (CoRR, 2023)

FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs. - Young Jin Kim 006; Rawn Henry; Raffy Fahim; Hany Hassan Awadalla (CoRR, 2023)

Task-Based MoE for Multitask Multilingual Machine Translation. - Hai Pham; Young Jin Kim 006; Subhabrata Mukherjee; David P. Woodruff; Barnabás Póczos; Hany Hassan Awadalla (CoRR, 2023)

Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness. - Young Jin Kim 006; Raffy Fahim; Hany Hassan Awadalla (CoRR, 2023)

A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models. - Haoran Xu; Young Jin Kim 006; Amr Sharaf; Hany Hassan Awadalla (ICLR, 2024)

Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation. - Haoran Xu; Amr Sharaf; Yunmo Chen; Weiting Tan; Lingfeng Shen; Benjamin Van Durme; Kenton Murray; Young Jin Kim 006 (ICML, 2024)

GRIN: GRadient-INformed MoE. - Liyuan Liu; Young Jin Kim 006; Shuohang Wang; Chen Liang 006; Yelong Shen; Hao Cheng 002; Xiaodong Liu 003; Masahiro Tanaka; Xiaoxia Wu; Wenxiang Hu; Vishrav Chaudhary; Zeqi Lin; et al. (CoRR, 2024)

Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs. - Abdelrahman Abouelenin; Atabak Ashfaq; Adam Atkinson; Hany Awadalla; Nguyen Bach; Jianmin Bao; Alon Benhaim; Martin Cai; Vishrav Chaudhary; Congcong Chen; Dong Chen 003; Dongdong Chen 001; et al. (CoRR, 2025)

Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math. - Haoran Xu; Baolin Peng; Hany Awadalla; Dongdong Chen 001; Yen-Chun Chen 001; Mei Gao; Young Jin Kim 006; Yunsheng Li; Liliang Ren; Yelong Shen; Shuohang Wang; Weijian Xu; et al. (CoRR, 2025)

SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation. - Zichong Li; Chen Liang 006; Zixuan Zhang; Ilgee Hong; Young Jin Kim 006; Weizhu Chen; Tuo Zhao (CoRR, 2025)

Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation. - Liliang Ren; Congcong Chen; Haoran Xu; Young Jin Kim 006; Adam Atkinson; Zheng Zhan 001; Jiankai Sun; Baolin Peng; Liyuan Liu; Shuohang Wang; Hao Cheng 002; Jianfeng Gao 001; et al. (CoRR, 2025)