Senior Applied Scientist and computer vision lead with 10+ years architecting high-performance Edge AI and recognition systems. Expert in data-centric ML, using synthetic data pipelines and GenAI to cut model development cycles by 70%. Proven track record deploying optimized, real-time inference on constrained hardware. Deep expertise in object detection, temporal modeling, and scaling ML infrastructure 0-to-1.
Axon Enterprise, Inc.
- Architected a synthetic-data pipeline using diffusion-based image synthesis and VLM-guided auto-labeling for redaction detectors — cutting research-to-production from 8 months to 2–3, and supplying 30% of all training data.
- Led deployment of advanced redaction models, improving precision/recall by 5–10% and capturing 40% of customer redaction usage.
- Co-developed an org-wide standard for multi-object tracking and detection, adopted across 3 research and product teams to unify training and evaluation.
- Tech-led a 3-person team on a confidential pre-launch computer vision initiative, owning technical direction and architecture in an ambiguous 0-to-1 problem space.
- Delivered optimized face/head detectors achieving a 32% recall improvement at 90% precision.
Peloton Interactive, Inc.
- Sole owner of ML algorithms for temporal exercise recognition and repetition counting, achieving a <2% off-by-one error rate.
- Built an automated evaluation pipeline that moved model releases from monthly/bimonthly to weekly.
Samsung Next
- Engineered a real-time, on-device pose-based action recognition system for iOS, achieving 98% accuracy in exercise recognition.
- Hired and led a team of 3, establishing Samsung Next's initial ML capability alongside a scalable pipeline with lineage, versioning, and experiment tracking.
Sighthound, Inc.
- Engineered a multi-task convolutional network for face analytics in production identity systems, achieving <1% false accept/reject rates in commercial deployments.
- Enhanced License Plate Recognition accuracy by 20% through architecture adjustments and data strategy.
S. Z. Masood, G. Shu, A. Dehghan, E. G. Ortiz. “License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks.” arXiv:1703.07330. Sighthound technical report.
A. Dehghan, E. G. Ortiz, G. Shu, S. Z. Masood. “DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network.” arXiv:1702.04280. Sighthound technical report.
A. Dehghan, S. Z. Masood, G. Shu, E. G. Ortiz. “View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network.” arXiv:1702.01721. Sighthound technical report.
A. Dehghan, E. G. Ortiz, R. Villegas, M. Shah. “Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders.” IEEE Conference on Computer Vision and Pattern Recognition.
E. G. Ortiz, B. C. Becker. “Face Recognition for Web-Scale Datasets.” Elsevier Computer Vision and Image Understanding.
E. G. Ortiz. Taming Wild Faces: Large-Scale, Real-World Face Recognition in Still and Video Imagery. Ph.D. dissertation, University of Central Florida.
E. G. Ortiz, A. Wright, M. Shah. “Face Recognition in Movie Trailers via Mean Sequence Sparse Representation-based Classification.” IEEE Conference on Computer Vision and Pattern Recognition.
B. C. Becker, E. G. Ortiz. “Evaluating Open-Universe Face Identification on the Web.” IEEE CVPR Workshop on Analysis and Modeling of Faces and Gestures.
B. C. Becker, E. G. Ortiz. “Evaluation of Face Recognition Techniques for Application to Facebook.” IEEE International Conference on Automatic Face and Gesture Recognition.
A. Koufakou, E. G. Ortiz, M. Georgiopoulos, G. C. Anagnostopoulos, K. M. Reynolds. “A Scalable and Efficient Outlier Detection Strategy for Categorical Data.” IEEE International Conference on Tools with Artificial Intelligence.
“Repetition Counting within Connected Fitness Systems.” Peloton Interactive, filed 19 September 2023. US application 19/113,245 published April 2026, under examination.
“Connected Fitness Systems and Methods.” Peloton Interactive, filed 31 March 2022. Granted in Europe as EP 4,313,324 B1 on 17 December 2025; US application 18/553,078 under examination.
“Data-Enhanced Video Viewing System and Methods for Computer Vision Processing.” Sighthound, issued 2018.
“Computer Vision Pipeline and Methods for Detection of Specified Moving Objects.” Sighthound, issued 2017.
Unconstrained video face recognition, issued 2016 from the CVPR 2013 work. University of Central Florida.
Ph.D., M.S., B.S. — Computer Engineering
- Dissertation: Taming Wild Faces: Large-Scale, Real-World Face Recognition in Still and Video Imagery
- Advisor: Prof. Mubarak Shah, Director, Center for Research in Computer Vision