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
systems for 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.
If you are interested in working with me, please send me an email with:
(1) your interested research area (with respect to my research interest), (2) at least one of my publication you read and want to build upon, and (3) your relevant background experiences (e.g., courses, projects, publication experiences). If these are not provided explicitly, I will not respond to your email.
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
• May 2024: Our work "
Information Flow Control in Machine Learning through Modular Model Architecture" was accepted to
Usenix Security 2024!
• 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!