Entrepreneur · AI Engineer · Data Scientist
I am the VP of Artifical Intelligence and a co-founder at RIOS, where I build robots that learn to autonomously perform tasks requiring advanced perceptual reasoning and human-level dexterity. A data scientist by education, I’ve developed an interest in applying modern methods of deep learning to machines that understand and interact with the real world and ways to develop more generalized intelligence around automation of all kinds.
I have experience in areas such as computer vision tasks, reinforcement learning, time-series analysis and prediction, multimodal generative AI and learning from human/AI feedback. I am an advocate of open source and I welcome offers to get involved with projects that work toward super-human intelligence and serve a greater good.
In past lives, I have been an entrepreneur, web developer, and graphic designer. I independently recorded and released an indie rock album under the moniker Alpha Leonis in 2009, have summited Mt. Hood twice, and am always looking for ways to reframe problems in search of blue oceans of opportunity.
- Artificial Super-Human Intelligence
- AI Agents and Machine Learning for Autonomous Systems
- Human/AI-in-the-Loop Feedback and Alignmenzt
- Multimodal Generative AI and Representation Learning
- Knowledge-informed machine learning (Geometric DL, Physics-informed ML)
- Causal Inference and Counterfactual Reasoning
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Master of Information and Data Science, 2018
UC Berkeley
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Geography, 2003
UC Santa Barbara
Experience
- Build and oversee a team that pioneered the RaaS (Robots-as-a-Service) and hybrid RaaS business model to provide automation on-demand
- Raise capital from seed to post Series B
- Develop algorithms, models and compute architecture for deployed robots in multiple industries
- Develop machine learning systems at scale for general manipulation, perception, planning, control and quality metrics
- Manage applied research efforts for new product development
Education
Probability and statistics, research design, field experiments, causal inference, time series analysis, information theory, deep learning, and machine learning at scale
Show courses (8)
Statistics for Data ScienceResearch Design and Applications for Data and Analysis
Statistical Methods for Discrete Response, Time Series, and Panel Data
Applied Machine Learning
Experiments and Causal Inference
Storing and Retrieving Data
Natural Language Processing with Deep Learning
Scaling Up! Really Big Data
University of Washington · Machine Learning Specialization (4)
Machine Learning Foundations: A Case Study ApproachMachine Learning: Regression
Machine Learning: Clustering and Retrieval
Machine Learning: Classification
Stanford · Probabilistic Graphical Models Specialization (3)
Probabilistic Graphical Models 1: RepresentationProbabilistic Graphical Models 2: Inference
Probabilistic Graphical Models 3: Learning
Deeplearning.ai · Deep Learning Specialization (4)
Neural Networks and Deep LearningImproving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Structuring Machine Learning Projects
Convolutional Neural Networks
