Brain, cognition and machine learning
My main goal as a researcher is to develop new ideas, algorithms and mathematical models to extend the frontiers of science
and technology so as to improve the quality of life of humans and their environment. Mine is also a search for knowledge and a
desire to understand mind, cognition and rationality.
To this end, I conduct research in the following areas: Machine learning: Prediction and classification, sequential Monte Carlo and particle filtering, MCMC, variational inference, stochastic approximation, Bayesian statistics, optimization, probabilistic graphical models, structured relational stochastic models, active learning, online learning, unsupervised, semi-supervised and imitation learning. Cognitive Science and neural architectures: Cognition, sub-consciousness, sparse coding, Boltzmann machines, deep feature learning and invariance. Computer vision: Object recognition, image tracking and dynamic scene understanding. Robotics: Planning, navigation, sensing and actuation. Optimal control: Model predictive control, LQG, partially observed Markov decision processes (POMDPs) and reinforcement learning. Web-scale learning: Search engines, multimedia, web mining, social networks, collaborative filtering and recommender systems. Game theory: Sparse game representations and stochastic games.
Lately, I've been particularly interested in neural architectures and learning from web-scale datasets. The conjecture is that with a sound theory of intelligence, the right architectures and enough data, we might discover very simple (yet powerful) algorithms for perception, motor control and probabilistic reasoning.
To this end, I conduct research in the following areas: Machine learning: Prediction and classification, sequential Monte Carlo and particle filtering, MCMC, variational inference, stochastic approximation, Bayesian statistics, optimization, probabilistic graphical models, structured relational stochastic models, active learning, online learning, unsupervised, semi-supervised and imitation learning. Cognitive Science and neural architectures: Cognition, sub-consciousness, sparse coding, Boltzmann machines, deep feature learning and invariance. Computer vision: Object recognition, image tracking and dynamic scene understanding. Robotics: Planning, navigation, sensing and actuation. Optimal control: Model predictive control, LQG, partially observed Markov decision processes (POMDPs) and reinforcement learning. Web-scale learning: Search engines, multimedia, web mining, social networks, collaborative filtering and recommender systems. Game theory: Sparse game representations and stochastic games.
Lately, I've been particularly interested in neural architectures and learning from web-scale datasets. The conjecture is that with a sound theory of intelligence, the right architectures and enough data, we might discover very simple (yet powerful) algorithms for perception, motor control and probabilistic reasoning.
NEWS AND MEDIA :
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- Our big data spin-off
Zite was acquired by CNN.
- AISTATS 2010 demo by Ben Marlin.
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MITACS
kindly awarded me the "MITACS Young Researcher Award".
I thank all my students and academic/industry collaborators for it.
In BC, we have an amazing pool of talented young IT students and professionals. Slides:
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Monte Carlo lectures -
Sequential Monte Carlo NIPS Tutorial slides:
- If you have a strong degree in physics, math, stats, neuroscience, EE or CS, join our team by applying here
- Interview for CTV about an art tool I designed with Eric Brochu.
- Bayesian Interactive Optimization for Procedural Animation:
- Introduction to machine learning video