- Focus on computer vision, signal and image processing, and applied deep learning
- PhD in Mathematics, UT Dallas (2025) — research on computationally efficient neural architectures for image processing
- 4+ years in industry: ML at Progress Rail, signal/image processing at Viridien
- MS in Data Science, The University of Texas at Austin (January 2026 - Current)
- PhD in Mathematics, The University of Texas at Dallas (May 2025)
- MS in Mathematics, The University of Texas at Dallas (August 2019)
- BS in Mathematics, Minor in Physics, The University of Texas at Arlington (May 2017)
- Viridien — Seismic Imaging Analyst; 1+ year of signal and image processing for large-scale geophysical imaging on HPC
- Progress Rail (Caterpillar) — Data Scientist II; 3 years building ML solutions for fuel optimization tied to $100M+ in contracts
- Signal and Image Processing
- Machine Learning, Deep Learning, and Computer Vision
- Seismic Imaging
- Scientific Machine Learning
- Reinforcement Learning
- Hervert Hernandez, E.A., Cao, Y., & Kehtarnavaz, N. (2025). Computationally Efficient Neural Architecture Search for Image Denoising. IEEE Access, 13, 60743–60762.
- Hervert Hernandez, E.A., Cao, Y., & Kehtarnavaz, N. (2022). Deep learning architecture search for real-time image denoising. Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022, 1210205.
- enas-networks — Reference implementation for Computationally Efficient Neural Architecture Search for Image Denoising (IEEE Access, 2025).
- RL-controlled NAS
- DHDN-based architectures
- Jensen–Shannon dataset subsampling
- Machine Learning & AI: Deep Learning, Computer Vision, Reinforcement Learning, Model Training
- Signal & Image Processing: Image Denoising, Seismic Imaging
- Programming: Python, C++, MATLAB, PyTorch
- Cloud & Tools: AWS, SageMaker, Linux, Git
Active member of IEEE, SIAM, and SPIE
Last updated: June 2026