Hao Geng Ph.D. PMICC School of Information Science and Technology ShanghaiTech University Office: Rm 332, SIST Email: genghao@shanghaitech.edu.cn

Biography

Currently, I'm an assistant professor at ShanghaiTech University (ShanghaiTech). Prior to that, I got Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong (CUHK) in 2021. Previously, I received M.Sc. with Merit from Department of Computing, Imperial College London in 2016, and M.Eng. from USTC in 2015.

My research interests include machine learning, deep learning, and optimization methods with applications in EDA, especially design space exploration and computational lithography.

Publications

Journal Papers

• [J18] Tinghuan Chen, Hao Geng, Qi Sun, Sanping Wan, Yongsheng Sun, Huatao Yu, Bei Yu, “Wages: The Worst Transistor Aging Analysis for Large-scale Analog Integrated Circuits via Domain Generalization”, accepted by ACM Transactions on Design Automation of Electronic Systems (TODAES).

• [J17] Bo Yang, Qi Xu, Hao Geng, Song Chen, Bei Yu, Yi Kang, “Floorplanning with Edge-Aware Graph Attention Network and Hindsight Experience Replay”, accepted by ACM Transactions on Design Automation of Electronic Systems (TODAES).

• [J16] Su Zheng, Hao Geng#, Chen Bai, Bei Yu#, Martin D.F. Wong, “Boosting VLSI Design Flow Parameter Tuning with Random Embedding and Multi-objective Trust-region Bayesian Optimization”, ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 28, no. 5, pp. 1–23, 2023.

• [J15] Yongtian Bi, Qi Xu, Hao Geng, Song Chen, Yi Kang, “AD²VNCS: Adversarial Defense and Device Variation-Tolerance in Memristive Crossbar-Based Neuromorphic Computing Systems”, ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 29, no. 1, pp. 8:1-8:19, 2023.

• [J14] Yongtian Bi, Qi Xu, Hao Geng, Song Chen, Yi Kang, “Resist: Robust Network Training for Memristive Crossbar-Based Neuromorphic Computing Systems”, IEEE Transactions on Circuits and Systems II: Express Briefs (TCAS-II), vol. 70, no. 6, pp. 2221-2225, 2023.

• [J13] Hao Geng, Tinghuan Chen, Yuzhe Ma, Binwu Zhu, Bei Yu, “PTPT: Physical Design Tool Parameter Tuning via Multi-Objective Bayesian Optimization”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 42, no. 01, pp. 178–189, 2023.

• [J12] Hao Geng, Yuzhe Ma, Qi Xu, Jin Miao, Subhendu Roy, Bei Yu, “High-Speed Adder Design Space Exploration via Graph Neural Processes”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 41, no. 8, pp. 2657–2670, 2022.

• [J11] Hao Geng, Haoyu Yang, Lu Zhang, Jin Miao, Fan Yang, Xuan Zeng, Bei Yu, “Hotspot Detection via Attention-based Deep Layout Metric Learning”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 41, no. 8, pp. 2685–2698, 2022.

• [J10] Hao Geng, Wei Zhong, Haoyu Yang, Yuzhe Ma, Joydeep Mitra, Bei Yu, “SRAF Insertion via Supervised Dictionary Learning”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 39, no. 10, pp. 2849-2859, 2020.

• [J9] Qi Xu*, Hao Geng*, Tianming Ni, Song Chen, Bei Yu, Xiaoqing Wen, “Fortune: A New Fault-Tolerance TSV Configuration in Router-based Redundancy Structure”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 41, no. 10, pp. 3182–3187, 2022.

• [J8] Qi Xu, Hao Geng#, Song Chen, Bo Yuan, Cheng Zhuo, Yi Kang, Xiaoqing Wen, “GoodFloorplan: Graph Convolutional Network and Reinforcement Learning Based Floorplanning”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 41, no. 10, pp. 3492–3502, 2022.

• [J7] Tinghuan Chen, Bin Duan, Qi Sun, Meng Zhang, Guoqing Li, Hao Geng, Qianru Zhang, Bei Yu, “An Efficient Sharing Grouped Convolution via Bayesian Learning”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 33, no. 12, pp. 7367–7379, 2022.

• [J6] Haoyu Yang, Wei Zhong, Yuzhe Ma, Hao Geng, Ran Chen, Wanli Chen, Bei Yu, “VLSI Mask Optimization: From Shallow To Deep Learning”, Integration, the VLSI Journal, vol. 77, pp. 96-103, Mar, 2021.

• [J5] Ran Chen, Wei Zhong, Haoyu Yang, Hao Geng, Fan Yang, Xuan Zeng, Bei Yu, “Faster Region-based Hotspot Detection”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 41, no. 3, pp. 669–680, 2022.

• [J4] Tinghuan Chen, Bingqing Lin, Hao Geng, Shiyan Hu, Bei Yu, “Leveraging Spatial Correlation for Sensor Drift Calibration in Smart Building”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 40, no. 7, pp. 1273-1286, 2021.

• [J3] Qi Xu, Song Chen, Hao Geng, Bo Yuan, Bei Yu, Feng Wu, Zhengfeng Huang, “Fault Tolerance in Memristive Crossbar-Based Neuromorphic Computing Systems”, Integration, the VLSI Journal, vol. 70, Jan., pp. 70-79, 2020.

• [J2] Qi Xu, Hao Geng, Song Chen, Bei Yu, Feng Wu, “Memristive Crossbar Mapping for Neuromorphic Computing Systems on 3D IC”, ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 25, no. 1, pp. 8:1-8:19, 2019.

• [J1] Lizhe Wang, Hao Geng, Peng Liu, Ke Lu, Joanna Kolodziej, Rajiv Ranjan, Albert Zomaya, “Particle Swarm Optimization based Dictionary Learning for Remote Sensing Big Data”, Knowledge-Based System, vol. 79, pp. 43-50, Elsevier, May, 2015.

Conference Papers

• [C25] Yuanhang Gao*, Donger Luo*, Chen Bai, Bei Yu, Hao Geng, Qi Sun, Cheng Zhuo “Is Vanilla Bayesian Optimization Enough for High-Dimensional Architecture Design Optimization?”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024.

• [C24] Peng Xu, Su Zheng, Yuyang Ye, Chen Bai, Siyuan Xu, Hao Geng, Tsung-Yi Ho, Bei Yu, “RankTuner: When Design Tool Parameter Tuning Meets Preference Bayesian Optimization”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024.

• [C23] Yuyang Chen*, Yiwen Wu*, Jingya Wang, Tao Wu, Xuming He, Jingyi Yu#, Hao Geng#, “LLM-HD: Layout Language Model for Hotspot Detection with GDS Semantic Encoding”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 23–27, 2024. (Best Paper Award Nomination)

• [C22] Donger Luo*, Qi Sun*, Xinheng Li, Chen Bai, Bei Yu, Hao Geng#, “Knowing The Spec to Explore The Design via Transformed Bayesian Optimization”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 23–27, 2024.

• [C21] Guojin Chen, Hongquan He, Peng Xu, Hao Geng, Bei Yu, “Efficient Bilevel Source Mask Optimization”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 23–27, 2024.

• [C20] Guowen Kuang, Xin Lu, Jingran Xia, Hao Geng, Xu Wang, Jinfeng Yang, “Real-Oriented Object Detection Driven by Intelligent Stockbreeding”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, April 14-19, 2024.

• [C19] Donger Luo, Qi Sun, Qi Xu, Tinghuan Chen, Hao Geng#, “Attention-Based EDA Tool Parameter Explorer: From Hybrid Parameters to Multi-QoR metrics”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Valencia, Spain, Mar. 25–27, 2024.

• [C18] Hongquan He, Guowen Kuang, Qi Sun, Hao Geng#, “PoLM: Point Cloud and Large Pre-trained Model Catch Mixed-type Wafer Defect Pattern Recognition”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Valencia, Spain, Mar. 25–27, 2024.

• [C17] Bo Yang, Qi Xu, Hao Geng, Song Chen, Yi Kang, “Miracle: Multi-Action Reinforcement Learning-Based Chip Floorplanning Reasoner”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Valencia, Spain, Mar. 25–27, 2024.

• [C16] Guojin Chen, Hao Geng, Bei Yu, David Z. Pan, “Open-Source Differentiable Lithography Imaging Framework”, SPIE Advanced Lithography + Patterning, San Jose, Feb. 25–29, 2024.

• [C15] Hao Geng, Qi Sun, Tinghuan Chen, Qi Xu, Tsung-Yi Ho, Bei Yu, “Mixed-type Wafer Failure Pattern Recognition”, IEEE/ACM Asian and South Pacific Design Automation Conference (ASPDAC), Tokyo Odaiba Miraikan, Jan. 16–19, 2023. (Invited Paper)

• [C14] Qi Sun, Xinyun Zhang, Hao Geng, Yuxuan Zhao, Yang Bai, Haisheng Zheng, Bei Yu, “GTuner: Tuning DNN Computations on GPU via Graph Attention Network”, ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jul. 10–14, 2022.

• [C13] Hao Geng, Qi Xu, Tsung-Yi Ho, Bei Yu, “PPATuner: Pareto-driven Tool Parameter Auto-tuning in Physical Design via Gaussian Process Transfer Learning”, ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jul. 10–14, 2022. (Best-in-Track Paper)

• [C12] Hao Geng, Tinghuan Chen, Qi Sun, Bei Yu, “Techniques for CAD Tool Parameter Auto-tuning in Physical Synthesis: A Survey”, IEEE/ACM Asian and South Pacific Design Automation Conference (ASPDAC), Jan. 17-20, 2022. (Invited Paper)

• [C11] Hao Geng, Fan Yang, Xuan Zeng, Bei Yu, “When Wafer Failure Pattern Classification Meets Few-shot Learning and Self-Supervised Learning”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov. 01-04, 2021.

• [C10] Qi Sun, Chen Bai, Tinghuan Chen, Hao Geng, Xinyun Zhang, Yang Bai, Bei Yu, “Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning”, IEEE International Conference on Computer Vision (ICCV), Oct. 11-17, 2021.

• [C9] Qi Sun, Chen Bai, Hao Geng, Bei Yu, “Deep Neural Network Hardware Deployment Optimization via Advanced Active Learning”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Feb. 01-05, 2021.

• [C8] Qi Xu, Junpeng Wang, Hao Geng, Song Chen and Xiaoqing Wen, “Reliability-Driven Neuromorphic Computing Systems Design”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Feb. 01-05, 2021.

• [C7] Hao Geng, Haoyu Yang, Lu Zhang, Jin Miao, Fan Yang, Xuan Zeng, Bei Yu, “Hotspot Detection via Attention-based Deep Layout Metric Learning”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov. 02-05, 2020. (Best-in-Track Paper)

• [C6] Haoyu Yang, Wei Zhong, Yuzhe Ma, Hao Geng, Ran Chen, Wanli Chen, Bei Yu, “VLSI Mask Optimization: From Shallow To Deep Learning”, IEEE/ACM Asian and South Pacific Design Automation Conference (ASPDAC), pp. 434-439, Beijing, Jan. 13-16, 2020. (Invited Paper)

• [C5] Tinghuan Chen, Bingqing Lin, Hao Geng, Bei Yu, “Sensor Drift Calibration via Spatial Correlation Model in Smart Building”, ACM/IEEE Design Automation Conference (DAC), Las Vegas, NV, June 02-06, 2019.

• [C4] Ran Chen, Wei Zhong, Haoyu Yang, Hao Geng, Xuan Zeng, Bei Yu, “Faster Region-based Hotspot Detection”, ACM/IEEE Design Automation Conference (DAC), Las Vegas, NV, June 02-06, 2019.

• [C3] Hao Geng, Haoyu Yang, Yuzhe Ma, Joydeep Mitra, Bei Yu, “SRAF Insertion via Supervised Dictionary Learning”, IEEE/ACM Asian and South Pacific Design Automation Conference (ASPDAC), Tokyo, Jan. 21-24, 2019. (Best Paper Award Nomination)

• [C2] Hao Geng, Haoyu Yang, Bei Yu, Xingquan Li, Xuan Zeng, “Sparse VLSI Layout Feature Extraction: A Dictionary Learning Approach”, IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Hong Kong, July 09-11, 2018. (Invited Paper)

• [C1] Hao Geng, Lizhe Wang, Peng Liu, Lajiao Chen, “Compressed Sensing Based Remote Sensing Image Reconstruction Using an Auxiliary Image as Priors”, in IEEE Geoscience and Remote Sensing Symposium (IGARSS), pp. 2499-2502, IEEE, Quebec, Canada, 2014.

Book Chapters

• [B1] Tinghuan Chen, Bingqing Lin, Hao Geng, Bei Yu, “Smart Building Sensor Drift Calibration”, Big Data Analytics for Cyber-Physical Systems, Springer, 2020: 187-202.

Selected Awards

Best Paper Award Nomination	DAC	2024
Best Paper Award Nomination	ASPDAC	2019
Postgraduate Studentship	CUHK	2017-2021
Suzhou Industrial Park Scholarship	USTC	2014

Teaching

• 2023 Fall: CS101 – Algorithms and Data Structures – ShanghaiTech
  

• 2023 Spring: EE215A – VLSI Design Automation – ShanghaiTech
  

• 2022 Fall: CS101 – Algorithms and Data Structures – ShanghaiTech