About

Yingjie is currently a PhD student in Computer Engineering, University of Maryland, College Park under the supervision of Prof. Cunxi Yu. Her research focuses on physics-aware infrastructure for optical computing platforms, hardware-software co-design, and efficient AI/ML algorithms. She also works in electronic design automation (EDA), focusing on machine learning for synthesis and verification. Her work received the Best Paper Award at DAC (2023), American Physical Society DLS poster award (2022) and Best Poster Presentation Award at DAC Young Fellow (2020). Yingjie won the Second Place at the ACM/SIGDA Student Research Competition (2023) and was selected as the EECS Rising Star (2023).

Resume: CV

Education

• Doctor of Philosophy, Computer Engineering, University of Maryland, College Park 2023 - present
• Doctor of Philosophy, Computer Engineering, University of Utah 2020 - 2023
• M.Eng, Electrical and Computer Engineering, Cornell University 2018 - 2019
• B.S., Electrical and Computer Engineering, Huazhong University of Science and Technology, Wuhan, China 2014 - 2018

 

Experience

• Research Intern, Google X 05/2024 - 11/2024
• Research Intern, Nvidia 05/2023 - 08/2023
• Hardware Engineer, DELL EMC, Shanghai, China 2019 - 2020

 

Publications

2024

• BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation
  Yingjie Li, Anthony Agnesina, Yanqing Zhang, Mark Ren, Cunxi Yu
  IEEE Design, Automation and Test in Europe Conference (DATE'24)
  

• DAG-aware Synthesis Orchestration
  Yingjie Li, Mingju Liu, Alan Mishchenko, Mark Ren, Cunxi Yu
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (to appear)
  orchestrate@ABC

• Survey of Machine Learning for Software-assisted Hardware Design Verification: Past, Present, and Prospect
  Nan Wu, Yingjie Li, Hang Yang, Hanqiu Chen, Steve Dai, Cong Hao, Cunxi Yu, Yuan Xie
  ACM Transactions on Design Automation of Electronic Systems (TODAES), 2024.

2023

• LightRidge: An End-to-end Agile Design Framework for Diffractive Optical Neural Networks
  Yingjie Li, Ruiyang Chen, Minhan Lou, Berardi Sensale-Rodriguez, Weilu Gao, Cunxi Yu
  The ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS'24).

• RubikONNs: Multi-task Learning with Rubik's Diffractive Optical Neural Networks
  Yingjie Li, Weilu Gao, Cunxi Yu
  The 32nd International Joint Conference on Artificial Intelligence (IJCAI-23).

• Physics-aware Roughness Optimization for Diffractive Optical Neural Networks
  Yingjie Li(co-first), Shanglin Zhou, Minhan Lou, Weilu Gao, Cunxi Yu, Caiwen Ding
  IEEE/ACM 60th Design Automation Conference (DAC '23).

• RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
  Jiaqi Yin, Yingjie Li, Daniel Robinson, Cunxi Yu
  IEEE/ACM 60th Design Automation Conference (DAC '23).

• Gamora: Graph Learning based Symbolic Reasoning for Large-scale Boolean Networks
  Nan Wu, Yingjie Li, Cong (Callie) Hao, Steve Dai, Cunxi Yu, Yuan Xie
  IEEE/ACM 60th Design Automation Conference (DAC '23) (Best Paper Award, 2/1157).
  

• Effects of interlayer Reflection and Interpixel Interaction in Diffractive Optical Neural Networks
  Minhan Lou, Yingjie Li, Cunxi Yu, Berardi Sensale-Rodriguez, Weilu Gao
  Optical Letter. Jan 2023.

2022

• FlowTune: End-to-end Automatic Logic Optimization Exploration via Domain-specific Multi-armed Bandi
  Walter Lau Neto, Yingjie Li, Pierre-Emmanuel Gaillardon, Cunxi Yu
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD'22). Github
  

• Physics-aware Complex-valued Adversarial Machine Learning in Reconfigurable Diffractive All-optical Neural Network
  Ruiyang Chen(co-first), Yingjie Li(co-first), Minhan Lou, Jichao Fan, Yingheng Tang, Berardi Sensale-Rodriguez, Cunxi Yu, Weilu Gao
  Laser & Photonics Reviews. Vol 16, July 2022.

• Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks
  Yingjie Li, Ruiyang Chen, Weilu Gao, Cunxi Yu
  IEEE/ACM 41st 2022 International Conference on Computer-Aided Design (ICCAD'22).

• Physics-aware Adversarial Machine Learning: An Experimental Study in Diffractive Optical Neural Networks
  Yingjie Li, Ruiyang Chen, Minhan Lou, Jichao Fan, Yingheng Tang, Berardi Sensale-Rodriguez, Weilu Gao, Cunxi Yu
  Invited talk at workshop 3rd ROAD4NN@Design Automation Conference (DAC '22 ROAD4NN).
  Presentation Video
  Pre-print
  

• An Open-source Compiler Framework for Diffractive Optical ML Architectures
  Yingjie Li, Ruiyang Chen, Minhan Lou, Berardi Sensale-Rodriguez, Weilu Gao, Cunxi Yu
  Workshop talk at 1st OSCAR@International Symposium on Computer Architecture (ISCA '22 OSCAR).

• RubikONNs: Multi-task Learning with Rubik's Diffractive Optical Neural Networks
  Yingjie Li, Weilu Gao, Cunxi Yu
  IEEE/ACM 58th Design Automation Conference (DAC '22 WIP).

• Combinatorial RL-based Scheduling for Pipelined Edge TPUs
  Jiaqi Yin, Yingjie Li, Cunxi Yu
  Github (to appear)
  TinyML Research Symposium 2022 (TinyML'22)

• Complex-valued Reconfigurable Diffractive Optical Neural Networks using Cost-effective Spatial Light Modulators
  Ruiyang Chen, Yingjie Li, Minhan Lou, Cunxi Yu, Weilu Gao
  Conference on Lasers and Electro-Optics (CLEO'22).

2021

• Real-time Multi-Task Learning in Diffractive Deep Neural Networks via Hardware-Software Co-design
  extended version @ arXiv
  Yingjie Li, Ruiyang Chen, Berardi Sensale Rodriguez, Weilu Gao, Cunxi Yu
  Nature Scientific Reports

• Late Breaking Results: Physical Adversarial Attacks of Diffractive Deep Neural Networks
  Yingjie Li, Cunxi Yu
  IEEE/ACM 58th Design Automation Conference (DAC '21).

• SLAP: A Supervised Learning Approach for Priority Cuts Technology Mapping (to appear)
  [Github (to appear)]
  Walter Lau Neto, Matheus Trevisan Moreira, Yingjie Li, Luca Amaru, Cunxi Yu, and Pierre-Emmanuel Gaillardon
  IEEE/ACM 58th Design Automation Conference (DAC '21).

Service

• Offical Reviewer: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)