Education

  • 2018: M.S. in Signal and Information Processing, Xidian University
  • 2015: B.S. in Electronic Engineering, Xidian University

Work Experience

  • 2019 - present: Research Assistant
    • University of Houston (Houston, TX)
  • Summer 2022: Deep Learning Engineer Intern
    • PROS (Houston, TX)
    • Project: “Graph Neural Network (GNN) for Spatial-temporal Demand Forecasting”
    • Mentors: Dr. Justin Silver, Dr. Yan Xu
  • Summer 2021: Deep Learning Engineer Intern
    • PROS (Houston, TX)
    • Project: “Deep Learning Approach for Cold-start Problem in Recommender System” and “Multi-Task Learning for Demand Prediction Through a Hyper-Network”
    • Mentors: Dr. Yan Xu, Mr. Manu Chaudhary
  • 2014/10 - 2015/5: Field Application Engineer Intern
    • Texas Instruments (Shanghai, China)
    • Project: “Fast image pre-processing algorithms for face detection on Embedded Vision Engine (EVE)”
    • Mentor: Dr. Jing Cui

Skills & Languages

  • Programming: Python, Matlab, C/C++
  • AI Framwroks: Pytorch, Tensorflow, Scikit-learn
  • Data Manipulation: SQL, PySpark, Pandas
  • Toolkits: Docker, Linux, Git, LaTex, Databricks
  • Languages: English, Chinese (Mandarin)

Certificates

  • Generative AI with Large Language Models, online course authorized by DeepLearning.AI and Amazon Web Services.

Reviewers

  • IEEE Transactions on Electromagnetic Compatibility, 2023
  • IEEE Antennas and Wireless Propagation Letters, 2023
  • IEEE Transactions on Antennas and Propagation, 2022
  • IEEE Transactions on Geoscience and Remote Sensing, 2022
  • IEEE Geoscience and Remote Sensing Letters. 2021
  • IEEE Journal on Multiscale and Multiphysics Computational Techniques, 2019

Teaching Experience

  • 2023: ECE6379: Power Syst Oper and Modeling (graduate), teaching assistant, University of Houston
  • 2021: ECE6340: Interm Electromag Waves (graduate), teaching assistant, University of Houston
  • 2019: ECE6323: Optical Fiber Communications (graduate), teaching assistant, University of Houston

Awards & Honors

  • 2022: PROS Hackathon – Winning Team, Houston ($ 2,000)
  • 2015-2018: Outstanding Graduate Student, Xidian University
  • 2015-2016: First Class Graduate Scholarship (Top 5%), Xidian University, (￥2,000)
  • 2014: National Scholarship (Top 1%), China (￥5,000)
  • 2014: Meritorious Winner Award in the Mathematical Contest in Modeling (MCM)
  • 2013: National Encouragement Scholarship (Top 1%), China (￥5,000)
  • 2012-2013: Outstanding Volunteer of College Students Psychological Education Activity, Xi’an, China
  • 2012: Leader of Outstanding Summer Social Practice Team of Xidian University
  • 2011-2012: Role Model Freshmen of Academic Merit (1%), Xidian University

Publications

Journal Articles

  • Yanyan Hu, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang, and J. Chen, “3D cooperative inversion of airborne magnetic and gravity gradient data using deep learning techniques”, submitted to the special section Geophysics for critical minerals in Geophysics, accepted.
  • Yanyan Hu, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang, and J. Chen, “A Deep Learning-enhanced Framework for Multiphysics Joint Inversion”, in Geophysics, vol 88, no. 1, pp. 13-26, Jan. 2023, doi: 10.1190/geo2021-0589.1.
  • Y. Jin, Y. Zi, W. Hu, Yanyan Hu, X. Wu and J. Chen, “A Robust Learning Method for Low-Frequency Extrapolation in GPR Full Waveform Inversion,” in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, Sep. 2022, doi: 10.1109/LGRS.2022.3205590.
  • Yanyan Hu, Y. Jin, X. Wu, and J. Chen, “A Theory-guided Deep Neural Network for Time Domain Electromagnetic Simulation and Inversion Using a Differentiable Programming Platform”, in IEEE Transactions on Antennas & Propagation, vol. 70, no. 1, pp. 767-772, Jan. 2022, doi: 10.1109/TAP.2021.3098585.
  • P. Zhang, Yanyan Hu, Y. Jin, S. Deng, X. Wu, and J. Chen, “A Maxwell’s Equations Based Deep Learning Method for Time Domain Electromagnetic Simulations,” in IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 6, pp. 35-40, 2021, doi: 10.1109/JMMCT.2021.3057793.
  • Yanyan Hu, R. Guo, Y. Jin, X. Wu, M. Li, A. Abubakar, and J. Chen, “A supervised descent learning technique for solving directional electromagnetic logging-while-drilling inverse problems," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 11, pp. 8013-8025, Nov. 2020, doi: 10.1109/TGRS.2020.2986000.

Conference Proceedings and Posters

  • Yanyan Hu, X. Wei, X. Wu, J. Sun, Y. Huang, and J. Chen, “Deep learning enhanced joint inversion for mineral exploration using airborne geophysics: Application in Decorah area”, The 2023 International Meeting for Applied Geoscience & Energy (IMAGE), Houston, TX, USA, Aug. 2023.
  • Yanyan Hu, X. Wu, and J. Chen, “Deep Learning Enhanced Joint Inversion for Monitoring of Underground CO2 Storage Sites”, UH Energy Research Day, Houston, TX, USA, Aug. 2023.
  • Yanyan Hu, X. Wei, X. Wu, J. Sun, Y. Huang, and J. Chen, “3D Joint Inversion of Multi-physics Data Using Deep Learning Techniquesn”, XXXVth URSI General Assembly and Scientific Symposium, Sapporo, Japan, Aug. 2023.
  • Yanyan Hu, Y. Jin, X. Wu, and J. Chen, “Simulating Time Domain Electromagnetic Waves on a Differentiable Programming Platform”, The International Council for Industrial and Applied Mathematics (ICIAM), Tokyo, Japan, Aug. 2023.
  • Yanyan Hu, X. Wei, X. Wu, J. Sun, Y. Huang, and J. Chen, “Deep Learning Enhanced 3D Joint Inversion”, 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Oregon, OR, USA, Jul. 2023.
  • Yanyan Hu, J. Chen, X. Wu, and Y. Huang, “A flexible and versatile joint inversion framework using deep learning,” The 2022 International Meeting for Applied Geoscience & Energy (IMAGE), Houston, TX, USA, Aug. 2022.
  • Yanyan Hu, J. Chen, X. Wu and Y. Huang, “Multiphysics Joint Inversion Using Successive Deep Perceptual Constraints,” 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), Denver, CO, USA, Jul. 2022.
  • Yanyan Hu, X. Wu, and J. Chen, " Deep Perceptual Loss for Multi-Physics Joint Inversion," 83rd EAGE Annual Conference & Exhibition, Madrid, Spain, Jun, 2022.
  • Yanyan Hu, J. Chen, X. Wu and Y. Huang, “Deep Learning Enhanced Joint Inversion of Multiphysics Data with Nonconforming Discretization,” 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), Singapore, Singapore, 2021.
  • M. Chaudhary, Yanyan Hu, S. Boluki, “Multi-Task Learning for Demand Prediction Through a Hyper-Network”, Ken Kennedy AI and Data Science Conference, Oct. 2021.
  • Yanyan Hu, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang, and J. Chen, “Deep learning-enhanced multiphysics joint inversion”, First International Meeting for Applied Geoscience & Energy (IMAGE), Denver, CO, USA, Sep. 2021.
  • Yanyan Hu, Y. Jin, X. Wu and J. Chen, “Solving Time Domain Electromagnetic Forward and Inverse Problems using a Differentiable Programming Platform,” 2021 International Conference on Electromagnetics in Advanced Applications (ICEAA), Honolulu, HI, USA, Oct. 2021.
  • Yanyan Hu, Y. Jin, X. Wu, J. Chen, J. Chen, Q. Shen, Y. Huang, “Deep Learning Enhanced Joint Geophysical Inversion for Crosswell Monitoring,” 2021 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, Jan. 2021.
  • Yanyan Hu, R. Guo, Y. Jin, X. Wu, M. Li, A. Abubakar, and J. Chen, “A supervised descent learning technique for inversion of directional electromagnetic logging-while-drilling data,” The 90th SEG Annual Meeting, Oct. 2020.
  • Y. Jin, Yanyan Hu, X. Wu, and J. Chen, “RNN-based gradient prediction for solving magnetotelluric inverse problem,” The 90th SEG Annual Meeting, Oct. 2020.
  • S. Wang, W. Hu, Yanyan Hu, X. Wu, and J. Chen, “A physics-augmented deep learning method for seismic data deblending,” The 90th SEG Annual Meeting, Oct. 2020.
  • Yanyan Hu, Y. Jin, X. Wu, and J. Chen, “Solving Time Domain Electromagnetic Problems using a Differentiable Programming Platform,” 2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium), Montreal, QC, Canada, 2020.
  • Yanyan Hu, Y. Jin, X. Wu, and J. Chen, “A physics-driven deep-learning inverse solver for subsurface sensing”, The 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, Montréal, Canada, Jul. 2020.
  • P. Zhang, Yanyan Hu, Y. Jin, S. Deng, X. Wu, and J. Chen, “A Maxwell’s Equations Based Deep Learning Method for Time Domain Electromagnetic Simulations,” 2020 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS), Waco, TX, USA, 2020.
  • Yanyan Hu, Y. Jin, X. Wu, and J. Chen, “A PDE-based Deep Learning Scheme for Time Domain Electromagnetic Simulations,” The 42nd Progress in Electromagnetics Research Symposium, Xiamen, China, Dec. 2019.
  • Yanyan Hu, R. Guo, Y. Jin, X. Wu, M. Li, A. Abubakar, and J. Chen, “A Supervised Descent Learning Technique for Solving Well Logging Inverse Problems,” SPWLA Resistivity SIG Meeting, Houston, TX, USA, Nov. 2019.

Patent

  • T. Wang, Yanyan Hu, C. Liu, “A Method of Improving the Accuracy of Direction of Arrival Estimation,” Application No 201810500858.6, May. 2018.
  • T. Wang, Yanyan Hu, J. Liu, J. Li, “A Multi-step Optimization Method of Maximizing Area Coverage for UAVs Based on Ant Colony Algorithm,” Application No 201710510054.6, Jun. 2017.
  • B. Han, Yanyan Hu, J. Zhang, W. Qiu, S. Huang, R. Li, L. Sha, “Image Sequence Region of Interest Detection Based on Improved Visual Attention Model, Patent ID ZL2014 1 0317739.5,” approved on Feb. 15, 2017.