Conference Proceedings
- Kiwan Maeng, Jiamu Bai, Xin Yu, Meilong Xu, Weitao Lu, Xin Pan, Daniel Kifer, Jian Wang and Yu Wang, 2026, "Towards Better Optimization for Listwise Preference in Diffusion Models", International Conference on Learning Representations (ICLR)
- Kiwan Maeng, Michael Shen, Muhammad Umar, G. Edward Suh and Udit Gupta, 2025, "Hermes: Algorithm-System Co-design for Efficient Retrieval Augmented Generation At-Scale", The International Symposium on Computer Architecture (ISCA)
- Kiwan Maeng, Jinyu Liu, Wenjie Xiong and G. Edward Suh, 2025, "Practical Federated Recommendation Model Learning Using ORAM with Controlled Privacy", ASPLOS, 2, pp. 913-932
- Kiwan Maeng, Yingtian Zhang, Yan Kang, Ziyu Ying, Wanhang Lu, Sijie Lan, Huijuan Xu, Anand Sivasubramaniam, Mahmut T Kandemir and Chitaranjan Das, 2025, "Pirate: No Compromise Low-Bandwidth VR Streaming for Edge Devices", ASPLOS, 2, pp. 882-896
- Kiwan Maeng, G. Edward Suh, Trishita Tiwari, Suchin Gururangan, Chuan Guo, Weizhe Hua, Sanjay Kariyappa, Udit Gupta, Wenjie Xiong and Hsien-Hsin S. Lee, 2024, "Information Flow Control in Machine Learning through Modular Model Architecture", USENIX Security Symposium
- Kiwan Maeng and G. Edward Suh, 2024, "Accelerating ReLU for MPC-Based Private Inference with a Communication-Efficient Sign Estimation", Proceedings of Machine Learning and Systems (MLSys)
- Kiwan Maeng, Juntaek Lim, Younguen Kwon, Ranggi Hwang, G. Edward Suh and Minsoo Rhu, 2024, "LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models", International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)
- Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Minsoo Rhu, Hsien-Hsin S Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks and G. Edward Suh, 2024, "GPU-based Private Information Retrieval for On-Device Machine Learning Inference", International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)
- Kiwan Maeng and Brandon Lucia, 2024, "Compiler-based Memory Encryption for Machine Learning on Commodity Low-power Devices", International Conference on Compiler Construction (CC)
- Kiwan Maeng, Chuan Guo, Sanjay Kariyappa and G. Edward Suh, 2023, "Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information", Conference on Neural Information Processing Systems (NeurIPS)
- Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G Edward Suh, Moinuddin K Qureshi and Hsien-Hsin S Lee, 2023, "Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis", International Conference on Machine Learning (ICML)
- Rishabh Jain, Scott Cheng, Vishwas Kalagi, Vrushabh Sanghavi, Samvit Kaul, Meena Arunachalam, Kiwan Maeng, Adwait Jog, Anand Sivasubramaniam, Mahmut T Kandemir and Chitaranjan Das, 2023, "Optimizing CPU Performance for Recommendation Systems At-Scale", Proceedings of the 46th International Symposium on Computer Architecture (ISCA)
- Kiwan Maeng, Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang and Carole-Jean Wu, 2023, "FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning", IEEE Data Engineering Bulletin
- Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Udit Gupta, Manoj Chakkaravarthy, David Brooks and Carole-Jean Wu, 2023, "Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters", Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp. 118--132
- Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat and Carole-Jean Wu, 2022, "Towards fair federated recommendation learning: Characterizing the inter-dependence of system and data heterogeneity", Proceedings of the 16th ACM Conference on Recommender Systems (RecSys), pp. 156--167
- Emily Ruppel, Milijana Surbatovich, Harsh Desai, Kiwan Maeng and Brandon Lucia, 2022, "An Architectural Charge Management Interface for Energy-Harvesting Systems", 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 318--335
- Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S Lee, Bugra Akyildiz, Max Balandat, Joe Spisak, Ravi Jain, Mike Rabbat and Kim Hazelwood, 2022, "Sustainable ai: Environmental implications, challenges and opportunities", Proceedings of Machine Learning and Systems (MLSys), 4, pp. 795--813
- Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Carole-Jean Wu and Brandon Lucia, 2021, "CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery", Proceedings of Machine Learning and Systems (MLSys)
- Kiwan Maeng and Brandon Lucia, 2020, "Adaptive Low-overhead Scheduling for Periodic and Reactive Intermittent Execution", ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)
- Kiwan Maeng and Brandon Lucia, 2019, "Supporting Peripherals in Intermittent Systems with Just-In-Time Checkpoints", ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)
- Kiwan Maeng and Brandon Lucia, 2018, "Adaptive Dynamic Checkpointing for Safe Efficient Intermittent Computing", The Symposium on Operating Systems Design and Implementation (OSDI)
- Kiwan Maeng, Brandon Lucia and Alexei Colin, 2017, "Alpaca: Intermittent Execution Without Checkpoints", Object-Oriented Programming, Systems, Languages & Applications (OOPSLA)
Manuscripts
- Hanieh Hashemi, Wenjie Xiong, Liu Ke, Kiwan Maeng, Murali Annavaram, G. Edward Suh and Hsien-Hsin S Lee, 2022, "Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems", arXiv preprint arXiv:2212.06264
- Kiwan Maeng, Chuan Guo, Sanjay Kariyappa and Edward Suh, 2022, "Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information", NeurIPS 2022 Federated Learning Workshop
