• B.S., Electrical and Computer Engineering, Seoul National University, 2016
• Ph.D., Electrical and Computer Engineering, Carnegie Mellon University, 2021

Conference Proceedings

Jinyu Liu, Wenjie Xiong, G. Edward Suh and Kiwan Maeng, 2025, "Practical Federated Recommendation Model Learning Using ORAM with Controlled Privacy", ASPLOS

Yingtian Zhang, Yan Kang, Ziyu Ying, Wanhang Lu, Sijie Lan, Huijuan Xu, Kiwan Maeng, Anand Sivasubramaniam, Mahmut T Kandemir and Chitaranjan Das, 2025, "Pirate: No Compromise Low-Bandwidth VR Streaming for Edge Devices", ASPLOS

Trishita Tiwari, Suchin Gururangan, Chuan Guo, Weizhe Hua, Sanjay Kariyappa, Udit Gupta, Wenjie Xiong, Kiwan Maeng, Hsien-Hsin S. Lee and G. Edward Suh, 2024, "Information Flow Control in Machine Learning through Modular Model Architecture", Usenix Security

Kiwan Maeng and G. Edward Suh, 2024, "Accelerating ReLU for MPC-Based Private Inference with a Communication-Efficient Sign Estimation", MLSys

Juntaek Lim, Younguen Kwon, Ranggi Hwang, Kiwan Maeng, 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)

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

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

Service to Penn State:

Service to External Organizations:

• Organizing Conferences and Service on Conference Committees, Committee Member, April 2023

• Organizing Conferences and Service on Conference Committees, Committee Member, October 2022 - April 2023

• Organizing Conferences and Service on Conference Committees, Committee Member, November 2022 - February 2023