I currently lead our generative AI and computer vision machine learning teams at Scale AI. At Scale, I have led development of ML in the loop for quality assurance and efficiency in our data engines. I created AFM-1, Scale’s first vision foundation model and led ML for human-AI collaboration in vision and language data engines. I led the teams that developed & productionized models for spam, cheating, and fraud detection, LLM-based graders, and in-house custom reward models for quality control. I have also led research efforts at Scale, guiding an org that has been able to derive business value out of AI research while simultaneously publishing our findings and acquiring top research talent.
I am currently focused on researching and building systems that most effectively leverage human supervision for the advancement of foundation models. Since 2015, I have been interested in developing models that can rapidly improve, by training powerful base models either though meta-learning, joint training, online learning, and improved calibration [1, 2]. Generally, I’m interested in technologies that can self-improve, collaborate with people, and improve human well-being.
Previously, I researched and developed deep learning systems at Standard Cognition, where I worked on video action recognition research, model training automation, transfer learning, domain adaptation, metrics development, hard mining, and model robustness. I also led migration of our core human pose estimation stack from tensorflow 1 to pytorch and implemented the real time production video inference service with TorchScript, Rust, and GStreamer. Before that, I was the first engineer at Explorer AI, an autonomous vehicle mapping company which was acquired by Standard Cognition.
I double mastered at the University of Arizona, focusing on machine learning and remote sensing, respectively. During that time, I was a researcher with the ML4AI lab in the School of Information. I was advised by Dr. Clayton Morrison and Dr. Greg Barron-Gafford.
personal github ~ linkedin ~ Scale AI github
Publications
Presentations
- Bringing Foundation Models to Automotive Data Engines. Tech.AD Europe 2024
- A Baseline Analysis of Reward Models’ Ability To Accurately Analyze Foundation Models Under Distribution Shift. AAAI Workshop on Responsible Language Models (ReLM 2024).
- Federated Reconnaissance: A New Framework for Distributed Continual Learning. Autonomy and Edge Computing session at the Fifth Annual Workshop on Naval Applications of Machine Learning (NAML 2021).
- Balancing Machine Learning Innovation and Scalability in Fast Growing Startups. Guest lecture in the Winter 2021 graduate course Machine Learning: Special Topics taught by Dr. Paul J. Atzberger at University of California Santa Barbara.
- Meta-Learning Initializations for Image Segmentation. NeurIPS 2020 Meta-Learning Workshop. https://meta-learn.github.io/2020/
- Continual Progress in Cascading Model Systems by Ensuring Reproducible State. Keynote speech at Deep Learning World 2020.