ROGER FRIGOLA

Machine Learning ∙ Racing ∙ Optimization






About Me

I am a Machine Learning and Artificial Intelligence expert based in Barcelona with more than ten years of experience in top level projects ranging from Formula 1 to marketing and winemaking. I have a PhD in Machine Learning from the University of Cambridge and I offer consulting services in artificial intelligence, predictive modeling, optimization and engineering (see section below).

More about me on my LinkedIn profile.

More about Machine Learning in this excellent article by McKinsey.


Consulting Services

I offer consulting services in artificial intelligence, predictive modeling, optimization and engineering. If you are interested in any of the following topics please feel free to email me [roger@rogerfrigola.com] or to contact me on LinkedIn to explore a potential collaboration.

  * Artificial Intelligence: How to learn automatically from data collected in the past to make the best possible decisions in the future?

  * Predictive Modeling: Do you want to gain insight and make predictions based on data?

  * Optimization: How to use predictive models to optimize revenue, lap time or any other goal? How to optimize when there are multiple conflicting goals?

  * Design of Experiments: If you have the chance to collect new data, what data will improve the predictive models the most? Which experiment should you run next in order to optimize your goals?

  * Uncertainty Quantification: How good are the predictions of a model? Is your current data informative enough to make confident predictions?


News


Writings

 Why did I do a PhD in Machine Learning?

 Dealing with Uncertainty in Engineering

 Why Should All Engineers Master Statistics?


PhD Thesis

My PhD thesis focused on learning nonlinear models of time series based on measured data. Those models rely on Gaussian processes and can provide probabilistic descriptions of uncertainty. My work resulted in new insights on the mathematical description of the models and the development of novel learning algorithms based on those insights.

Bayesian Time Series Learning with Gaussian Processes,
University of Cambridge, PhD Thesis, 2015.
[pdf] [bib]


Publications

Variational Gaussian Process State-Space Models,
R. Frigola, Y. Chen and C. E. Rasmussen.
Advances in Neural Information Processing Systems (NIPS), 2014.
[pdf] [bib]

Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM,
R. Frigola, F. Lindsten, T. B. Schön and C. E. Rasmussen.
19th World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, 2014.
[pdf] [bib]

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC,
R. Frigola, F. Lindsten, T. B. Schön and C. E. Rasmussen.
Advances in Neural Information Processing Systems (NIPS), 2013.
[pdf] [bib]

Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes,
R. Frigola and C. E. Rasmussen.
52nd IEEE Conference on Decision and Control (CDC), Florence, Italy, 2013.
[pdf] [slides] [bib] [code]

A Wrench-Sensitive Touch Pad Based on a Parallel Structure,
R. Frigola, L. Ros, F. Roure and F. Thomas.
IEEE International Conference on Robotics and Automation (ICRA), Pasadena (California), USA, 2008.
[pdf] [bib]


Talks

Inteligencia Artificial para la vela,
SAIL INN Pro, Bilbao, 1 March 2018.

Essential Machine Learning,
Course @ Carnovo, Barcelona, 20 December 2017.

Essential Machine Learning,
Course @ Bodegas Torres, Vilafranca del Penedès, 3 May 2016.

Advanced Machine Learning,
Course @ Porsche Motorsport, Weissach, 28 January 2016.

Essential Machine Learning,
Course @ Porsche Motorsport, Weissach, 27 January 2016.

Engineering in the Age of Machine Learning,
Meet Up @ Kernel Analytics, Barcelona, 30 October 2015.
[pdf]

Gaussian Process Models for Nonlinear Time Series (with Carl E. Rasmussen),
Tutorial, Cambridge, 16 April 2015.
[pdf]

Learning Dynamical Systems with Gaussian Processes,
Research Talk, Cambridge, 24 February 2014.
[pdf]

Probabilistic Models for Big Data (with Alex Davies),
Tutorial, Cambridge, 13 February 2014.
[pdf]

Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes,
52nd IEEE Conference in Decision and Control, Florence, 12 December 2013.
[pdf]

Tutorial on Bayesian Nonparametric Nonlinear System Identification (with Andrew McHutchon),
ERNSI Workshop, Nancy, 25 September 2013.
[pdf]

Bayesian Nonparametric Nonlinear System Identification,
Reglerteknik Monday Meeting, Linköping University, 10 June 2013.
[pdf]

Learning to Control: State Estimation,
Research Talk, Cambridge, 30 April 2012.
[pdf]

Statistical Inference for Engineers,
Seminar, Maranello, 19 March 2012.
[pdf]

An Overview of Control Theory,
Tutorial, Cambridge, 12 January 2012.
[pdf]


Miscellaneous

I recommend the company Minerva Knowledge in Barcelona for language services, project management, online learning and open access academic publishing.


Contact

roger@rogerfrigola.com

View Roger  Frigola's profile on LinkedIn

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