Machine Learning ∙ Racing ∙ Optimization

I am a Machine Learning and Artificial Intelligence expert based in Barcelona with fifteen years of experience in high-level projects ranging from Formula 1 and the America's Cup 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 vehicle dynamics (see section below).

More about me on my LinkedIn profile.

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 good decisions now and get the best rewards in the future?

  * Predictive Modeling: How to 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?

  * Vehicle Dynamics: How to optimize car handling and performance?

  * Advanced Signal Processing and Machine Learning for Time Series: Kalman filters, particle filters, custom Bayes filters, nonlinear filters, nonlinear predictions in time series, etc.

Why did I do a PhD in Machine Learning?

Dealing with Uncertainty in Engineering

Why Should All Engineers Master Statistics?

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 creation of novel learning algorithms based on those insights.

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

Variational Gaussian Process State-Space Models,
R. Frigola, Y. Chen and C. E. Rasmussen.
Neural Information Processing Systems (NIPS, now NeurIPS), 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.
Neural Information Processing Systems (NIPS, now NeurIPS), 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]

Racing Intelligence,
Presentation @ Mobile World Congress, February 2023.

Machine Learning for Motorsport,
Seminar @ Yamaha MotoGP Team, December 2021.

Machine Learning for Engineering,
Seminar @ G-Research, July 2021.

Essential Machine Learning,
Workshop @ Inspire Growth Partners Ltd, Auckland, October 2019.

Team NZ in the America's Cup,
Talk @ Maxon Motor, Brunnen, September 2019.

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

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

Bayesian Time Series Models,
Course @ Proportunity Ltd, London, November 2017.

Essential Machine Learning,
Course @ Bodegas Torres, Vilafranca del Penedès, 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 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 @ Ferrari F1, Maranello, 19 March 2012.

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