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David Duvenaud

David Duvenaud

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

Connect

University of Toronto

Google Scholar

About

David Duvenaud is a Canada CIFAR AI Chair at the Vector Institute and an assistant professor in the department of computer science and statistical sciences at the University of Toronto. He is also a founding member of the Vector Institute and the co-founder of Invenia, an energy forecasting and trading company.

Duvenaud’s research focuses on constructing deep probabilistic models to predict, explain and design things. He has developed continuous-depth neural networks which can adapt the amount of computation they need depending on the difficulty of the task. He builds models which can propose new molecules that have specified properties, and is working on automating parts of the data-collection pipeline for behaviour experiments.

Awards

  • Best Paper Award, Neural Information Processing Systems Conference (NIPSC), 2018

Relevant Publications

  • Li, X., Chen, R. T. Q., Wong, T.-K. L., & Duvenaud, D. (2020). Scalable gradients for stochastic differential equations. In Artificial intelligence and statistics.

  • Chang, C.-H., Creager, E., Goldenberg, A., & Duvenaud, D. (2019). Explaining image classifiers by adaptive dropout and generative in-filling. In International conference on learning representations.

  • Chen, R. T., Rubanova, Y., Bettencourt, J., & Duvenaud, D. K. (2018). Neural ordinary differential equations. In Advances in neural information processing systems (pp. 6571-6583).

  • Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., … & Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, 4(2), 268-276.

  • Grathwohl, W., Choi, D., Wu, Y., Roeder, G., & Duvenaud, D. (2017). Backpropagation through the void: Optimizing control variates for black-box gradient estimation.

Institution

Invenia

University of Toronto

Vector Institute

Department

Computer Science, Statistical Sciences

Education

  • PhD (Information Engineering), University of Cambridge
  • MSc (Computer Science), University of British Columbia
  • BSc Hons (Computer Science), University of Manitoba

Country

Canada

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