CV

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Education

• Ph.D. candidate in Computer Engineering, University of Southern California
• M.S. in Computer Engineering, University of Southern California, 2024
• B.S. in Electrical Engineering, University of Washington, 2019

Work experience

• Summer & Fall 2021: Research Intern
  • Meta AI
  • Supervisor: Hsien-Hsin S. Lee

Publications

PIGEON: A High Throughput Framework for Private Inference of Neural Networks using Secure Multiparty Computation

Christopher Harth-Kitzerow, Yongqin Wang, Rachit Rajat, Georg Carle, Murali Annavaram, "PIGEON: A High Throughput Framework for Private Inference of Neural Networks using Secure Multiparty Computation," 2025 25th Privacy Enhancing Technologies Symposium (PETS).

High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings

Christopher Harth-Kitzerow, Ajith Suresh, Yongqin Wang, Hossein Yalame, Georg Carle, Murali Annavaram, "High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings," 2025 25th Privacy Enhancing Technologies Symposium (PETS).

MPC-Pipe: An Efficient Pipeline Scheme for Semi-honest MPC Machine Learning

Yongqin Wang, Rachit Rajat, Murali Annavaram, "MPC-Pipe: An Efficient Pipeline Scheme for Semi-honest MPC Machine Learning," 2024 ACM 29th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS).

LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables

Yongqin Wang*, Rachit Rajat*, Murali Annavaram, "LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables," 2023 IEEE/ACM 50th International Symposium on Computer Architecture (ISCA). [* Equal Contributions]

PageORAM: An Efficient DRAM Page Aware ORAM Strategy

Rachit Rajat, Yongqin Wang and Murali Annavaram, "PageORAM: An Efficient DRAM Page Aware ORAM Strategy," 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO).

Characterization of MPC-based Private Inference for Transformer-based Models

Yongqin Wang, G. Edward Suh, Wenjie Xiong, Benjamin Lefaudeux, Brian Knott, Murali Annavaram, Hsien-Hsin S. Lee, "Characterization of MPC-based Private Inference for Transformer-based Models," 2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

DarKnight: An accelerated framework for privacy and integrity preserving deep learning using trusted hardware

Hanieh Hashemi, Yongqin Wang, and Murali Annavaram. 2021. DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

Origami inference: Private inference using hardware enclaves

Krishna Giri Narra, Zhifeng Lin, Yongqin Wang, Keshav Balasubramanian and Murali Annavaram, "Origami Inference: Private Inference Using Hardware Enclaves," 2021 IEEE 14th International Conference on Cloud Computing (CLOUD)

Byzantine-Robust and Privacy-Preserving Framework for FedML

Hanieh Hashemi, Yongqin Wang, and Murali Annavaram. 2021. Byzantine-Robust and Privacy-Preserving Framework for FedML.

The BlackParrot Processor: An Open-Source Industrial-Strength RV64G Multicore Processor

Teaching

Co-instructor for EE 653 Advanced Topics in Microarchitecture

Teaching Assistant for EE 557 Computer Systems Architecture

Co-instructor for EE 508 Hardware Fundation of Machine Learning

Teaching Assistant for EE 653 Advanced Topics in Microarchitecture

Teaching Assistant for EE 450 Introduction to Computer Networks

Service and leadership

• Reviewer for HPCA 2025
• AE committee member for ASPLOS 2025
• AE committee member for ISCA 2024
• Student volunteer for ISCA 2023
• Student volunteer for HPCA 2021

Inter-discipline activities:

• Reviewer for Journal of Geoenergy Science and Engineering