Nicolas Chapados

VP of Research

Research Management

Leadership team

Nicolas Chapados is VP of Research at ServiceNow Research. He holds an engineering degree from McGill University and a PhD in Computer Science from University of Montreal, Canada. While still writing his thesis and jointly with his advisor Yoshua Bengio, he co-founded ApSTAT Technologies in 2001, a machine learning technology transfer firm, to apply cutting-edge academic research ideas to areas such as insurance risk evaluation, supply chain planning, business forecasting, national defence, and hedge fund management. From this work, he also co-founded spin-off companies: Imagia, to detect and quantify cancer early with AI analysis of medical images, Element AI, to help organizations plan and implement their AI transformation, and Chapados Couture Capital, a quantitative asset manager. He holds the Chartered Financial Analyst (CFA) designation.

Interests

Time Series Forecasting
Finances
Large Language Models

Publications

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados. At International Conference on Machine Learning (ICML), 2023.

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Explaining explainable AI. Nicolas Chapados, Richard Zuroff. At Book Chapter, 2023.

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TACTiS: Transformer-attentional copulas for time series. Alexandre Drouin, Étienne Marcotte, Nicolas Chapados. At Montreal AI Symposium (MAIS), 2022.

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TACTiS: Transformer-Attentional Copulas for Time Series. Alexandre Drouin, Étienne Marcotte, Nicolas Chapados. At International Conference on Machine Learning (ICML), 2022.

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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2020.

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Meta-Learning Framework with Applications to Zero-Shot Time Series Forecasting. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio. At International Conference on Learning Representations (ICLR), 2019.

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