Spencer J. Chang

PhD working on texture analysis and statistical features for deep learning

About Me

Greetings! I am currently a fully cleared PhD candidate at the University of Florida in their Electrical and Computer Engineering department (final clearance achieved in late Mar 2025). I worked with Prof. Jose C. Principe from Fall 2019 to Spring 2022. I am currently a SMART Scholar working with Prof. Alina Zare and will be graduating in Spring 2025.

My current research work is in semantic segmentation, texture analysis, and the improvement of deep learning models with learned statistical features. Additionally, I am passionate about teaching and hope to become a professor in the future.

I have a plethora of personal interests aside from purely research:

Curriculum Vitae (PDF)

  • Last Update: April 2025

Education

  • University of Florida (UF)
    • PhD in Electrical and Computer Eng, 2019 - current (grad in 2025)
  • Cal Poly SLO (CPSLO)
    • MS in Computer Science, 2017 - 2019
    • BS in Computer Engineering, 2013 - 2017

Research Experience

  • SMART Scholar, UF (2022 - current)
    • Working with Prof. Alina Zare in the GatorSense Lab
    • Researching texture analysis in semantic segmentation with an emphasis on using learned statistical features in deep learning models to improve solutions
  • Graduate Research Assistant, UF (2019 - 2022)
    • Worked with Prof. José C. Principe in the Computational NeuroEngineering Lab
    • Investigated information-theoretic learning and proxy objective methods of explaining and improving deep learning architectures.
  • Brain Tumor Classification Using Hit-or-Miss Capsule Layers
    • CPSLO MS Thesis, 2018 - 2019
    • Committee: Prof. John Seng, Prof. Phillip Nico, Prof. Maria Pantoja

Recent Publications

S. Chang, R. Chowdhry, A. Zare. Paper under double-blind review.

Y. Song, G. Sapes, S. Chang, R. Chowdhry, T. Mejia, A. Hampton, S. Kucharski, T. M. S. Sazzad, Y. Zhang, B. L. Tillman, M. F. R. Resende Jr, S. Koppal, C. Wilson, S. Gerber, A. Zare, W. M. Hammond. Hyperspectral signals in the soil: Plant-soil hydraulic connection and disequilibrium as mechanisms of drought tolerance and rapid recovery, Plant Cell Environ., Jun. 2024, doi: 10.1111/pce.15011.

S. J. Chang, R. Chowdhry, Y. Song, T. Mejia, A. Hampton, S. Kucharski, TM Sazzad, Y. Zhang, S. J. Koppal, C. H. Wilson, S. Gerber, B. Tillman, M. FR Resende Jr., W. M Hammond, A. Zare. HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study. Computers and Electronics in Agriculture, Volume 225, 2024, 109307, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2024.109307.

S. Duan, S. Chang, and J. C. Principe, Labels, Information, and Computation: Efficient Learning Using Sufficient Labels, J. Mach. Learn. Res., vol. 24, no. 31, pp. 1–35, 2023.