Zixiao Wang(王子啸), Ph.D. Assistant Professor Microelectronics Thrust, Function Hub The Hong Kong University of Science and Technology (Guangzhou) Resume/CV (as of September, 2026)

Biography

I received my Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong in 2026, under the supervision of Prof. Bei Yu and Prof. Farzan Farnia. Previously, I received my M.Sc. in Computer Science and Technology and my B.Eng. in Automation with a second major in Economics from Tsinghua University.

I will join the Microelectronics Thrust, Function Hub, The Hong Kong University of Science and Technology (Guangzhou) in January 2027. I am recruiting highly self-motivated students for the Spring/Fall 2027 intake. Prospective students with interests in Design for Manufacturability, multi-physics simulation, and related EDA/AI topics are encouraged to send their CV to me by email.

Contact

Email: zixiaowang97@gmail.com

Research Interest

• Design for Manufacturability

• Simulation within EDA

• Hardware-efficient Machine Learning

Education

• Ph.D. Computer Science and Engineering, The Chinese University of Hong Kong, Aug 2022 - June 2026

• M.Sc. Computer Science and Technology, Tsinghua University, Sept 2019 - June 2022

• B.Eng. Automation, Tsinghua University, Sept 2015 - June 2019

• B.A. Economics (Second Major), Tsinghua University, Sept 2016 - June 2019

Selected Awards

Best Ph.D. Dissertation Award (Wafer Manufacturing)	EDA²	2026
Full Postgraduate Studentship	CUHK	2022-2026
Outstanding Graduate of Computer Science and Technology	Tsinghua University	2022
Qi Hang Scholarship	Tsinghua University	2019
Academician Ni Weidou Scholarship Endowment Fund	Tsinghua University	2018
Zheng Geru Scholarship	Tsinghua University	2017
1st runner-up in MLCAD Contest	MLCAD	2026
2nd Place Award in Technical Challenge (Team Vulcan)	RoboCup	2018
3rd Place Award in 1v1 Contest (Team Vulcan)	RoboCup	2018

Publication

* denotes equal contribution.

Selected preprints

• [P1] Zixiao Wang, Tianshu Hou, Chenghan Wang, Zhen Zhuang, Leilei Jin, Tsung-Yi Ho, Farzan Farnia, Bei Yu, “FasterRW: An Efficient Prior-Assisted Random Walk Solver for Thermal Analysis”, 2026. (pdf) (code)

Conference papers

• [C18] Zixiao Wang, Leilei Jin, Zhen Zhuang, Rongmei Chen, Bei Yu, “CSCO: A Backside-PDN-Aware Clock–Signal Co-Optimization Framework for Improved PPA”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Jose, Nov. 08–12, 2026. (paper)

• [C17] Chaofan Wang, Ziyang Yu, Su Zheng, Zixiao Wang, Yifan Shi, Bei Yu, “Moreau-ILT: Efficient Inverse Lithography Exploiting Weak Convexity”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Jose, Nov. 08–12, 2026. (paper)

• [C16] Wuqian Tang*, Zixiao Wang*, Che-Chien Lin, Fong-Hua Fu, Laura Angélica Shion-Korsak, Xinyun Zhang, Bei Yu, Chun-Yao Wang, “TAPCO: LLM-Guided Design-Adaptive Timing and Power Co-Optimization”, ACM/IEEE International Symposium on Machine Learning for CAD (MLCAD), Jeju, Sep. 07–09, 2026. (paper) (Invited Paper)

• [C15] Leilei Jin, Haoyang Xu, Siyuan Liang, Zhen Zhuang, Zhou Hu, Zixiao Wang, Bei Yu, Rongmei Chen, Tsung-Yi Ho, “BSPDN-Elite: A Comprehensive Framework for Optimizing Timing, Power and Routing Resources in BSPDN Designs”, ACM/IEEE Design Automation Conference (DAC), Long Beach, Jul. 26–29, 2026. (paper)

• [C14] Zixiao Wang, Jieya Zhou, Xinyun Zhang, Shoubo Hu, Farzan Farnia, Bei Yu, “DiffResist: Physics-Constrained Diffusion for Photoresist Modeling”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Verona, Italy, Apr. 20–22, 2026. (paper) (poster) (slides)

• [C13] Zixiao Wang, Tianshu Hou, Chenghan Wang, Zhen Zhuang, Tsung-Yi Ho, Farzan Farnia, Bei Yu, “FastRW: An Efficient Random Walk Method for Steady-State Thermal Analysis”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Verona, Italy, Apr. 20–22, 2026. (paper) (poster) (slides) (code)

• [C12] Wenbo Xu, Silin Chen, Yibo Huang, Xinyun Zhang, Zixiao Wang, Bei Yu, Ningmu Zou, “Node2Node: Node Adaptation with Transformer for Cross-Node Hotspot Detection”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Verona, Italy, Apr. 20–22, 2026. (paper)

• [C11] Rongliang Fu, Libo Shen, Ziyi Wang, Zhengxing Lei, Zixiao Wang, Junying Huang, Bei Yu, Tsung-Yi Ho, “DCLOG: Don't Cares-based Logic Optimization using Pre-training Graph Neural Networks”, IEEE/ACM Asian and South Pacific Design Automation Conference (ASPDAC), Hong Kong, Jan. 19–22, 2026. (paper)

• [C10] Xinyun Zhang*, Zixiao Wang*, Yurui Kuang, Bei Yu, “Video-based Visible-Event Cross-modal Person Re-identification for Edge AI Surveillance Systems”, IEEE/ACM Asian and South Pacific Design Automation Conference (ASPDAC), Hong Kong, Jan. 19–22, 2026. (paper) (slides)

• [C9] Zixiao Wang, Farzan Farnia, Zhenghao Lin, Yunheng Shen, Bei Yu, “On the Distributed Evaluation of Generative Models”, IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Honolulu, Oct. 19–20, 2025, pp. 7703–7712. (paper) (arXiv)

• [C8] Ziyang Yu, Peng Xu, Zixiao Wang, Binwu Zhu, Qipan Wang, Yibo Lin, Runsheng Wang, Bei Yu, Martin Wong, “SDM-PEB: Spatial-Depthwise Mamba for Enhanced Post-Exposure Bake Simulation”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 22–25, 2025. (paper) (poster) (slides)

• [C7] Zixiao Wang, Junwu Weng, Mengyuan Liu, Bei Yu, “FlexPose: Pose Distribution Adaptation with Limited Guidance”, AAAI Conference on Artificial Intelligence (AAAI), Philadelphia, Feb. 25–Mar. 04, 2025. (paper) (poster)

• [C6] Zixiao Wang, Jieya Zhou, Su Zheng, Shuo Yin, Kaichao Liang, Shoubo Hu, Xiao Chen, Bei Yu, “TorchResist: Open-source differentiable resist simulator”, SPIE Advanced Lithography + Patterning, San Jose, Feb. 23–27, 2025. (paper) (poster) (code)

• [C5] Zixiao Wang*, Yunheng Shen*, Xufeng Yao, Wenqian Zhao, Yang Bai, Farzan Farnia, Bei Yu, “ChatPattern: Layout Pattern Customization via Natural Language”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 23–27, 2024. (paper) (poster) (slides)

• [C4] Yang Bai, Wenqian Zhao, Shuo Yin, Zixiao Wang, Bei Yu, “ATFormer: A Learned Performance Model with Transfer Learning Across Devices for Large-Scale Transformers”, Empirical Methods in Natural Language Processing (EMNLP), Singapore, Dec. 06–10, 2023. (paper) (poster) (slides)

• [C3] Zixiao Wang*, Yunheng Shen*, Wenqian Zhao, Yang Bai, Guojin Chen, Farzan Farnia, Bei Yu, “DiffPattern: Layout Pattern Generation via Discrete Diffusion”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jul. 09–13, 2023. (paper) (poster) (slides) (code)

• [C2] Zixiao Wang, Junwu Weng, Chun Yuan, Jue Wang, “Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos”, AAAI Conference on Artificial Intelligence (AAAI, Oral), Washington, Feb. 7–14, 2023. (paper) (poster) (slides) (code)

• [C1] Haijin Ding, Zixiao Wang, Rebing Wu, “Enhancing the security of multi-agent networked control systems using QKD based homomorphic encryption”, IEEE Conference on Decision and Control (CDC), Miami Beach, Dec. 17-19, 2018. (paper)

Journal papers

• [J5] Zixiao Wang, Wenqian Zhao, Yunheng Shen, Yang Bai, Guojin Chen, Farzan Farnia, Bei Yu, “DiffPattern-Flex: Efficient Layout Pattern Generation via Discrete Diffusion”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 44, no. 12, pp. 4679–4690, 2025. (paper)

• [J4] Wenqian Zhao, Lancheng Zou, Zixiao Wang, Xufeng Yao, Bei Yu “HAPE: Hardware-Aware LLM Pruning For Efficient On-Device Inference Optimization”, ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 30, no. 04, pp. 61:1–61:18, 2025. (paper) (Editor's Pick)

• [J3] Wenqian Zhao, Shuo Yin, Chen Bai, Zixiao Wang, Bei Yu, “BAQE: Backend-Adaptive DNN Deployment via Synchronous Bayesian Quantization and Hardware Configuration Exploration”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 44, no. 04, pp. 1394–1405, 2025. (paper)

• [J2] Guojin Chen, Zixiao Wang, Bei Yu, David Z. Pan, Martin D.F. Wong, “Ultrafast Source Mask Optimization via Conditional Discrete Diffusion”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 43, no. 07, pp. 2140–2150, 2024. (paper)

• [J1] Yang Bai, Xufeng Yao, Qi Sun, Wenqian Zhao, Shixin Chen, Zixiao Wang, Bei Yu, “GTCO: Graph and Tensor Co-Design for Transformer-based Image Recognition on Tensor Cores”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 43, no. 02, pp. 586–599, 2024. (paper)

Dissertation

• [PHD] Zixiao Wang, “Physics-Driven Learning for Manufacturability”, Ph.D. Dissertation at The Chinese University of Hong Kong, July 2026. (thesis) (EDA² Best Ph.D. Dissertation Award, Wafer Manufacturing)

Softwares

• FastRW: GPU random-walk thermal solver and experiment harness for the FastRW / FasterRW algorithms (Apple Metal + C++17).

• TorchResist: An open-source differentiable photoresist simulator that is implemented with Pytorch.

• MoreauPruner: Robust pruning framework for large language models against weight perturbations. (paper)

• DiffPattern: Discrete diffusion framework for layout topology generation and constrained geometry assignment.

Experience

• Research Intern, Primarius Technologies, Shanghai, China, 2026.01-2026.06

• Research Intern, Huawei Noah's Ark Lab, HongKong, China, 2024.08-2025.05

• Research Intern, SenseTime, HongKong, China, 2023.04-2023.12

• Research Intern, Tencent AI Lab, Shenzhen, China, 2020.10-2022.08

• Research Intern, UBTECH Robotics, Beijing, China, 2017.09-2018.08

Professional Service

• Technical Program Committee Member, IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC).

• Program Committee Member, AAAI Conference on Artificial Intelligence (AAAI).

• Journal Reviewer, Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Consumer Electronics (TCE).

• Conference Reviewer, CVPR, ICCV, ECCV, NeurIPS, ICML, and ICLR.

Teaching

• CSCI3320, 2023-R2, Fundamentals of Machine Learning

• ENGG2760A, 2023-R1, Probability for Engineers

• CSCI5030, 2022-R2, Machine Learning Theory

• ENGG2020, 2022-R1, Digital Logic and Systems