KIWAN MAENG

Charles K. Etner Early Career Assistant Professor · Computer Science and Engineering · Pennsylvania State University (2022.8 --)
Postdoc Researcher · SysML Team · Meta AI Research (2021.8 -- 2022.8)
I am a tenure-track assistant professor in the Computer Science and Engineering Department at Pennsylvania State University. Prior to joining PSU, I did a one-year postdoc at Meta AI Research's SysML Team. I did my Ph.D. at Carnegie Mellon University, under the advisement of Professor Brandon Lucia. My research interest lies in privacy-preserving machine learning in the broadest sense. I am interested in studying the privacy implications of modern ML systems and developing algorithm/system/hardware solutions for privacy-preserving ML training and inference. In the past, I've also worked on systems for ML, ML and sustainability, and batteryless energy-harvesting devices.

I am actively looking for motivated undergraduate/master/Ph.D. students. If you are interested in working with me, please send me an email elaborating the area of research you are interested in with respect to my research interests/recent publications. Please include your relevant background experiences (e.g., courses, projects, publication experiences) as much as possible.

I am also an amateur webtoonist. Check out my work if you know Korean. Otherwise, do not bother. My pronouns are: he/his/him.

Email: kwmaeng91 [at] gmail [dot] com



Recent News

Feb 2024: Our work "Accelerating ReLU for MPC-Based Private Inference with a Communication-Efficient Sign Estimation" was accepted to MLSys 2024!
Dec 2023: Our work "Compiler-based Memory Encryption for Machine Learning on Commodity Low-power Devices" was accepted to CC 2024!
Nov 2023: Our work "LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models" was accepted to ASPLOS 2024!
Sep 2023: Our work "Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information" was accepted to NeurIPS 2023!
Jul 2023: Our work "GPU-based Private Information Retrieval for On-Device Machine Learning Inference" was accepted to ASPLOS 2024!
Apr 2023: Our work "Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis" was accepted to ICML 2023!

Publications

Private ML Offloading Across the Edge-Cloud

  • Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
    NeurIPS 2023 [PDF]
    Kiwan Maeng*, Chuan Guo*, Sanjay Kariyappa, and G. Edward Suh
  • Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information
    FL-NeurIPS 2022 Workshop [PDF]
    Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, and G. Edward Suh
  • Information Flow Control in Machine Learning through Modular Model Architecture
    Under Submission [PDF]
    Trishita Tiwari, Suchin Gururangan, Chuan Guo, Weizhe Hua, Sanjay Kariyappa, Udit Gupta, Wenjie Xiong, Kiwan Maeng, Hsien-Hsin S. Lee, G. Edward Suh

Multi-party Computation (MPC)

  • Accelerating ReLU for MPC-Based Private Inference with a Communication-Efficient Sign Estimation
    MLSys 2024 [PDF]
    Kiwan Maeng and G. Edward Suh
  • GPU-based Private Information Retrieval for On-Device Machine Learning Inference
    ASPLOS 2024 [PDF]
    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

Federated Learning and Differential Privacy

  • LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
    ASPLOS 2024 [PDF]
    Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng, Edward Suh, and Minsoo Rhu
  • Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
    ICML 2023 [PDF]
    Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K Qureshi, and Hsien-Hsin S. Lee
  • Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
    RecSys 2022 (Best Paper Finalist) [PDF]
    Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, and Carole-Jean Wu
  • Sustainable AI: Environmental Implications, Challenges and Opportunities
    MLSys 2022 [PDF]
    Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James 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, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, and Kim Hazelwood
  • Green Federated Learning
    FL-ICML 2023 Workshop [PDF]
    Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael Rabbat, Carole-Jean Wu, and Ilya Mironov
  • FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
    IEEE Data Eng. Bull 2023 [PDF]
    Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, and Carole-Jean Wu

Systems for ML

  • Compiler-based Memory Encryption for Machine Learning on Commodity Low-power Devices
    CC 2024 [PDF]
    Kiwan Maeng and Brandon Lucia
  • Optimizing CPU Performance for Recommendation Systems At-Scale
    ISCA 2023 [PDF]
    Rishabh Jain, Scott Cheng, Vishwas Kalagi, Vrushabh Sanghavi, Samvit Kaul, Meena Arunachalam, Kiwan Maeng, Adwait Jog, Anand Sivasubramaniam, Mahmut Taylan Kandemir, and Chita Das
  • Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters
    ASPLOS 2023 [PDF]
    Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, and Carole-Jean Wu
  • Carbon Dependencies in Datacenter Design and Management
    HotCarbon 2022 [PDF]
    Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Aditya Sundarrajan, Manoj Chakkaravarthy, Kiwan Maeng, David Brooks, and Carole-Jean Wu
  • CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
    MLSys 2021 [PDF]
    Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, and Carole-Jean Wu

Batteryless Energy-harvesting Devices

  • An Architectural Charge Management Interface for Energy-Harvesting Systems
    MICRO 2022 [PDF]
    Emily Ruppel, Milijana Surbatovich, Harsh Desai, Kiwan Maeng, and Brandon Lucia
  • Adaptive Low-overhead Scheduling for Periodic and Reactive Intermittent Execution
    PLDI 2020 [PDF]
    Kiwan Maeng and Brandon Lucia
  • Enhancing Stratospheric Weather Analysis and Forecasts by Deploying Sensors from a Weather Balloon
    NeurIPS 2019 Workshop (Spotlight Talk) [PDF]
    Kiwan Maeng, Iskender Kushan, Brandon Lucia, and Ashish Kapoor
  • Supporting Peripherals in Intermittent Systems with Just-In-Time Checkpoints
    PLDI 2019 [PDF]
    Kiwan Maeng and Brandon Lucia
  • Adaptive Dynamic Checkpointing for Safe Efficient Intermittent Computing
    OSDI 2018 [PDF]
    Kiwan Maeng and Brandon Lucia
  • Alpaca: Intermittent Execution Without Checkpoints
    Kiwan Maeng, Alexei Colin, and Brandon Lucia
  • Intermittent Computing: Challenges and Opportunities
    SNAPL 2017 [PDF]
    Brandon Lucia, Vignesh Balaji, Alexei Colin, Kiwan Maeng, and Emily Ruppel

Services

  • Conference Program Committee: ASPLOS (2023-2025), MLSys (2022-2024), ISCA (2024), MICRO (2023), HPCA (2024), EuroSys (2022), IISWC (2022)
  • Journal Reviewer: IEEE Micro (2022), ACM TACO (2022)
  • Workshop Program Committee: ASPLOS YArch (2024), PACT SRC (2022), SenSys USN (2022)

Awards & Honors

  • Charles K. Etner Early Career Professorship (2022 -- 2025)
  • KFAS Scholarship (2016 -- 2021)
  • Summa Cum Laude (B.S., Seoul National University)
  • National Scholarship for Science and Engineering (2010 -- 2016)

Industry Experiences

  • Postdoc Researcher. Facebook AI Research (FAIR; 2021 -- 2022)
  • Research Intern. Facebook AI Research (FAIR; 2020.5 -- 2020.12)
  • Research Intern. Microsoft Research (2019.5 -- 2019.8)

Miscellaneous

I used to be an amateur webtoon--(web + cartoon)--ist. I used to draw cartoons about scientists and engineers on Facebook, and they were popular enough to become a real book! Do not bother clicking the link if you cannot read Korean :)
I haven't drawn any new episodes in years, but still I have things to boast: