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Foundation models for time series
The Forecasting Company builds planning systems based on our in-house foundation models for time series.
AmazonCo-founder and CEO at The Forecasting Company, helping enterprise customers improve their operational decision making. PhD grad from UC Berkeley's BAIR. I spent time with Amazon Forecasting Science in SCOT, Google Brain, and Bloomberg LP where I worked on forecasting, optimization and deep learning research.
PhD in ML for speech and full-stack developer. Interned at Sonos where I built an on-device wake word detector and at Bloomberg building a large ASR system. Worked full-time at JPMorgan in their Machine Learning Center of Excellence in New York, and then at two startups, team lead ML/NLP at Consigli, a prop-tech startup, and sr algorithm engineer and full-stack at Vind AI, a SaaS for designing off-shore wind parks.
Easy and accurate forecasts for navigating an uncertain world
The Forecasting Company reveals an easy-to-use, enterprise-grade forecasting system that requires no training, uses a large, cross-domain training dataset, and can incorporate user inputs or curated data streams to produce accurate time-series forecasts for forecasting and planning use cases.
Powerful quantitative forecasting models
The AI forecasting company for global macro events.
