I am a Master’s student specializing in Computer Vision and Artificial Intelligence in the Department of Computer Science at Stanford University. Previously, I completed my Bachelor of Science in Computer Engineering from the Department of Electrical and Computer Engineering at Purdue University in 2020.

Education & Certifications

  • Master of Science, Stanford University, Computer Science (2022)
  • Bachelor of Science, Purdue University, Computer Engineering (2020)

Stanford Advisors

Current Research and Scholarly Interests

Robot learning for intuitive human-robot interaction

Work Experience

  • Software Engineering Intern, Google (September 3, 2019 - December 6, 2019)

    - Worked with the Google Cloud AI team on using Model Distillation to create Explainable AI by generating rules that explain Deep Learning models
    - Created a system to tune the complexity of rules generated, number of rules generated, and accuracy of the Deep Learning model
    - Implemented Soft Decision Trees, Random Forests, and Gradient Boosted Decision Trees to compare their trade-offs for Model Distillation


    Seattle, WA

  • Machine Learning Intern, Qualcomm (May 20, 2019 - August 20, 2019)

    - Worked with the ML Application Analysis Team on using Deep Learning to make Qualcomm Snapdragon chips more power-efficient
    - Upgraded the automation tool of the QoS logger to run multimedia applications on Android Q and parse log files
    - Generated LSTM models using Neural Architecture Search (NAS) to estimate QoS parameters for minimal power consumption


    San Diego, CA

All Publications

  • Efficient Training of Deep Classifiers for Wireless Source Identification Using Test SNR Estimates IEEE WIRELESS COMMUNICATIONS LETTERS Wang, X., Ju, S., Zhang, X., Ramjee, S., El Gamal, A. 2020; 9 (8): 1314–18
  • Fast deep learning for automatic modulation classification IEEE Machine Learning for Communications Emerging Technologies Initiatives Ramjee, S., Ju, S., Yang, D., Liu, X., El Gamal, A., Eldar, Y. C. 2019
  • Deep learning for interference identification: Band, training SNR, and sample selection IEEE Signal Processing Advances in Wireless Communications (SPAWC) Zhang, X., Seyfi, T., Ju, S., Ramjee, S., El Gamal, A., Eldar, Y. C. 2019