Shaoyi Huang
Openings: I am looking for 1~2 highly motivated PhD students (will be fully-funded) for Fall 2025. If you are interested, please send your CV, trasncripts, research interests to shaoyi.hiring@gmail.com
Application link: https://www.stevens.edu/program/computer-science-doctoral-program
Shaoyi is a tenure-track assistant professor in CS department at Stevens Institute of Technology.
She received her PhD degree from
the School of Computing at University of Connecticut
under the supervision of Prof. Caiwen Ding and Prof. Omer Khan.
Her research agenda is grounded in advancing AI systems, including algorithm-system co-design for AI acceleration, emerging deep learning models inference acceleration (i.e., Transformer and LLM), privacy preserving machine learning, and machine learning for EDA.
Shaoyi’s work has been published in high-impact conferences such as HPCA, ASPLOS, SC, DAC, ICCAD, ACL, ICCV, NeurIPS, IJCAI, etc.
She was selected as a Machine Learning and Systems Rising Star 2024.
She was awarded Marion and Frederick Buckman Engineering Fellowship for outstanding academic achievement in 2024 and both Predoctoral Prize for Research Excellence, GE Fellowship for Excellence from UConn in 2023 and 2022.
She was the recipient of Eversource Energy Graduate Fellowship in 2022, Synchrony Fellowship in 2022, and Cigna Graduate Fellowship in 2021.
Her work on FPGA-based language models acceleration through sparse attention and dynamic pipelining won DAC'22 Publicity Paper Award.
She was invited as a student panelist to share experience with the female researchers and K-12 students at the 6th Workshop for Women in Hardware and Systems Security (WISE).
Email  / 
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Research Interests
Efficient machine learning algorithm
Algorithm-system co-design for AI acceleration (including emerging deep learning models, such as GNNs, LLMs, Diffusion models, etc.)
Large-scale machine learning for chip design
Energy efficient privacy preserving machine learning
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News
- 12/2024: Invited to be a speaker of HackMHS at Millburn High School in Millburn, New Jersey. Looking forward to discussing with the hackers!
- 11/2024: One proposal is funded by Stevens Bridging Program as lead-PI. Thanks for the generous support from Stevens!
- 11/2024: Glad to be invited as a speaker at The 3rd Women in Quantitative Finance Americas Conference. Looking forward to connecting with people in Quantitative Finance! See you in New York City!
- 11/2024: One paper is accepted to NSF AI for EDA Workshop at NeurIPS 2024, congratulations to my students and interns, Zhendong, Renming and Haowen!
- 11/2024: I will give a mini-TED talk at WISE 2024.
- 11/2024: Invited to serve as Program Committee Track Chair for IEEE Cloud Summit 2025.
- 11/2024: Invited to serve as Program Committee for ISQED 2025.
- 11/2024: Invited to serve as a Reviewer for ACM Transactions on Architecture and Code Optimization.
- 10/2024: I will serve as a session chair for ICCAD 2024.
- 10/2024: Invited to serve as a TPC member for DAC 2025.
- 09/2024: Invited to serve as a Guest Editor of Special Issue, High-Performance Computing for AI: Architecture, Systems, and Algorithm.
- 09/2024: Invited to serve on the 2024 WISE organizing committee. See u in IBM T.J. Watson Research Center, New York.
- 09/2024: Invited to serve as a Reviewer for ICLR 2025.
- 09/2024: Shaoyi joined Stevens CS department.
- 08/2024: Invited to join the Program Committee of IPDPS’25 - the 39th IEEE International Parallel & Distributed Processing Symposium.
- 05/2024: Honored to be selected as a Machine Learning and Systems Rising Star 2024, see u in NVIDIA, Santa Clara, CA.
- 05/2024: Glad to volunteer at NSF IUCRC Center for Hardware and Embedded Systems Security and Trust (CHEST).
- 05/2024: Honored to receive Marion and Frederick Buckman Engineering Fellowship for outstanding academic achievement from the School of Computing.
- 04/2024: Invited to write an article for ACM Sigda newsletter "what is column".
- 10/2023: One paper was accepted to ASPLOS 2024.
- 10/2023: One paper was accepted to HPCA 2024.
- 10/2023: Received travel grant from CACC. Thanks, CACC!
- 09/2023: Honored to be invited to be the student panelist and to receive the student travel grant from WISE 2023.
- 09/2023: One proposal on Efficient Multi-party Computation-based Private Inference was accepted to WISE 2023 (The 6th Workshop for Women in Hardware and Systems Security).
- 09/2023: One paper was accepted to NeurIPS 2023.
- 09/2023: Honored to be invited to give a guest lecture at the University of Rochester.
- 09/2023: Invited to serve as a PC member for SDM 2024.
- 09/2023: Honored to present at TechCon organized by Semiconductor Research Corporation (SRC). See u in Austin.
- 07/2023: One paper was accepted to ICCAD 2023.
- 07/2023: One paper was accepted to ICCV 2023.
- 07/2023: Invited to serve as a PC member for AAAI 2024.
- 06/2023: Received the GE Fellowship for Excellence from UConn School of Engineering.
- 05/2023: Honored to give a talk and a poster presentation at Semiconductor Research Corporation (SRC) AIHW & CADT Annual Review in IBM Research. See u in San Jose.
- 05/2023: Received the Predoctoral Prize for Research Excellence from UConn CSE.
- 03/2023: Honored to be invited to give a talk at MLNLP.
- 03/2023: One paper was accepted to IJCAI 2023.
- 03/2023: Two papers on sparse training were accepted to DAC 2023. See u in San Francisco.
- 01/2023: Invited to serve as a reviewer for the First Workshop on DL-Hardware Co-Design for AI Acceleration at AAAI 2023.
- 12/2022: Invited to serve as a PC member for KDD 2023.
- 08/2022: Received the GE Fellowship for Excellence from the School of Engineering. Thanks for the support!
- 07/2022: Invited to serve as a PC member for AAAI 2023.
- 07/2022: Received the Synchrony Fellowship from Connecticut Advanced Computing Center. Thanks, CACC!
- 05/2022: Started to work at TikTok (ByteDance) as a research intern.
- 05/2022: Received the Predoctoral Prize for Research Excellence from UConn CSE.
- 05/2022: Received the Eversource Energy Graduate Fellowship from Eversource Energy Center. Thanks, Eversource!
- 04/2022: Our DAC 22 paper “A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining” was recognized as a Publicity Paper.
- 02/2022: One paper was accepted to ACL 2022.
- 02/2022: One paper was accepted to DAC 2022.
- 02/2022: One paper was accepted to ISQED 2022.
- 08/2021: Received the Cigna Graduate Fellowship from UConn CSE. Thanks, Cigna!
- 07/2021: One paper was accepted to ICCAD 2021.
- 06/2021: One paper was accepted to SC 2021.
- 04/2021: Three papers were accepted to GLSVLSI 2021.
- 02/2021: One paper was accepted to ISQED 2021.
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MaxK-GNN: Towards Theoretical Speed Limits for Accelerating Graph Neural Networks Training
H. Peng, X. Xie, K. Shivdikar, M. A. Hasan, J. Zhao, S. Huang, O. Khan, D. Kaeli, C. Ding
[ASPLOS 2024]
Paper
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PruneGNN: An Optimized Algorithm-Hardware Framework for Graph Neural Network Pruning
D. Gurevin, M. Shan, S. Huang, M. A. Hasan, C. Ding, O. Khan
[HPCA 2024]
Paper
(Acceptance rate: 75/410=18.3%)
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Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
S. Huang, B. Lei, D. Xu, H. Peng, Y. Sun, M. Xie, C. Ding
[DAC 2023]
Paper
(Acceptance rate: 263/1156=22.7%)
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Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
S. Huang, H. Fang, K. Mahmood, B. Lei, N. Xu, B. Lei, Y. Sun, D. Xu, W. Wen, C. Ding
[DAC 2023]
Paper
(Acceptance rate: 263/1156=22.7%)
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AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
S. Huang*, H. Peng*, T. Zhou*, W. Wen, X. Xu, C. Ding, et al.
[ICCV 2023]
Paper / Code
(Acceptance rate: 26.8%)
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Towards Lossless Head Pruning through Automatic Peer Distillation for Large Language Models
B. Li, Z. Wang, S. Huang, M. Bragin, J. Li, C. Ding
[IJCAI 2023]
Paper
(Acceptance rate: 15%)
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Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
X. Xie, H. Peng, A. Hasan, S. Huang, J. Zhao, H. Fang, W. Zhang, T. Geng, O. Khan, C. Ding
[ICCAD 2023]
Paper / Code
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LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
H. Peng, R. Ran, Y. Luo, J. Zhao, S. Huang, K. Thorat, T. Geng, C. Wang, X.lin Xu, W. Wen, C. Ding
[NeurIPS 2023]
Paper / Code
(Acceptance rate: 26.1%)
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Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
S. Huang, D. Xu, I. E. Yen, S. Chang, B. Li, C. Ding, et al.
[ACL 2022]
Paper / Code
(Acceptance rate: 714/3350=21.3%)
In collaboration with Moffett AI
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A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
S. Huang*, H. Peng*, S. Chen, B. Li, T. Geng, A, Li, W. Jiang, W, Wen, J, Bi, H. Liu, C. Ding
[DAC 2022]
Paper
Publicity paper award
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Towards Sparsification of Graph Neural Networks
H. Peng, D. Gurevin, S. Huang, T. Geng, W. Jiang, O. Khan, C. Ding
[ICCD 2022]
Paper / Code
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Analyzing and Defending against Membership Inference Attacks in Natural Language Processing Classification
Y. Wang, N. Xu, S. Huang, K. Mahmood, D. Guo, C. Ding, W. Wen, S. Rajasekaran
[IEEE BigData 2022]
Paper
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CoDG-ReRAM: An Algorithm-Hardware Co-design to Accelerate Semi-Structured GNNs on ReRAM
Y. Luo, P. Behnam, K. Thorat, Z. Liu, H. Peng, S. Huang, S. Zhou, O. Khan, A. Tumanov, C. Ding, T. Geng
[ICCD 2022]
Paper
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E.T.: Re-Thinking Self-Attention for Transformer Models on GPU
S. Huang*, S. Chen*, S. Pandey, B. Li, G. Gao, C. Ding, H. Liu
[SC 2021]
Paper / Code
(Acceptance rate: 86/365=23.6%)
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Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization
P. Qi, E. Sha, Q. Zhuge, H. Peng, S. Huang, Z. Kong, Y. Song, B. Li
[ICCAD 2021]
Paper
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HMC-Tran: A Tensor-core Inspired Hierarchical Model Compression for Transformer-based DNNs on GPU
S. Huang, S. Chen, H. Peng, D. Manu, Z. Kong, G. Yuan, L. Yang, S. Wang, H. Liu, C. Ding
[GLSVLSI 2021]
Paper
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Accelerating Transformer-based Deep Learning Models on FPGAs Using Column Balanced Block Pruning
H. Peng, S. Huang, T. Geng, A. Li, W. Jiang, H. Liu, W. Wang, C. Ding
[ISQED 2021]
Paper
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Teaching
- Guest Lecturer
- Teaching Assistant at University of Connecticut
CSE 4502/5717 - BigData Analytics, Spring 2023
Instructor: Prof. Suining He   
CSE5819 - Introduction to Machine Learning, Fall 2022
Instructor: Prof. Fei Miao   
As a co-instructor, I designed final project, developed and delivered coding tutorials, led final project presentation courses, supervised students on course projects, held office hours and graded assignments.
- Teaching Assistant at University of Rochester
Microcontroller, Spring 2018
Instructor: Prof. Qiang Lin   
Circuits & Signals LAB, Fall 2017
Instructor: Prof. Jack G. Mottley   
I led the laboratory sessions and provided hands-on instruction to around 100 undergraduate students.
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Internship
May 2022 - Aug 2022, Research Intern, ByteDance, Austin, TX
May 2021 - Aug 2021, Research Intern, Moffett AI, Los Altos, CA
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Talks
Student panelist - The 6th Workshop for Women in Hardware and Systems Security
Oct 2023, California State University, Fullerton, Fullerton, CA
Towards Efficient Model Inference and Training
Sep 2023, University of Rochester
Guest Lecture of ECE 403-1: Advanced Computer Architecture for Machine Learning.
Towards Efficient Training and Inference Under Pretrain-and-Finetune Paradigm
Sep 2023, TechCon - Semiconductor Research Corporation (SRC), Austin, TX
Exploring Extreme Sparsity in Training and Inference for Graph Neural Networks to Achieve High Performance
Scaling on Large Core Count Machines
May 2023, Semiconductor Research Corporation (SRC) AIHW \& CADT Annual Review, IBM Research, San Jose, CA
Towards Efficient Model Inference and Training
Apr 2023, Machine Learning and Natural Language Processing Community (MLNLP)
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Honors and Awards
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Machine Learning and Systems Rising Star, 2024
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Marion and Frederick Buckman Engineering Fellowship, 2024
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NeurIPS Travel Grant from CACC, 2023
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WISE Student Travel Award, 2023
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GE Fellowship of Excellence, 2023
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Predoctoral Prize for Research Excellence, 2023
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GE Fellowship for Excellence, 2022
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Synchrony Fellowship, 2022
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DAC Publicity Paper Award, 2022
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Predoctoral Prize for Research Excellence, 2022
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Eversource Energy Graduate Fellowship, 2022
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DAC Young Fellow, 2021
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Cigna Graduate Fellowship, 2021
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Professional Services
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Panelist
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The 6th Workshop for Women in Hardware and Systems Security (WISE)
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Organizing Committee
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The 7th Workshop for Women in Hardware and Systems Security (WISE)
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Guest Editor
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Special Issue, High-Performance Computing for AI: Architecture, Systems, and Algorithm
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Program Committee Member
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DAC’25
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IPDPS’25
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ICLR'25
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ACL'24
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AAAI'25, 24, 23
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NeurIPS'23 Datasets and Benchmarks
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KDD'24, 23
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SDM'24
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DCAA'23
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Conference External Reviewer
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NeurIPS'23
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DAC'23, 22, 21
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ICCAD'23, 22, 21
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AAAI'23, 22, 21
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EMNLP'23, 22
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IJCAI'23, 22, 21
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ICCD'23, 22
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EACL'22
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FPGA'21
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NAACL'21
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Journal Reviewer
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ACM Transactions on Architecture and Code Optimization
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Neurocomputing
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Pattern Recognition
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Engineering Applications of Artificial Intelligence
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Neural Networks
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Journal of Systems Architecture
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IEEE Transactions on Neural Networks and Learning Systems
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ACM Transactions on Intelligent Systems and Technology
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ACM Transactions on Design Automation of Electronic Systems
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Journal External Reviewer
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ACM Transactions on Architecture and Code Optimization
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IEEE Transactions on Neural Networks and Learning Systems
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IEEE Network Magazine
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Journal on Emerging Technologies in Computing Systems
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