Amir-massoud Farahmand

(SoloGen)

 

Research Goal


In the 21st century, we live in a world where data is abundant. We would like to take advantage of this opportunity to make more accurate and data-driven decisions in many areas of life such as industry, healthcare, business, and government. Even though many machine learning and data mining researchers have developed tools to benefit from big data, their methods so far have mostly been about the task of prediction. My goal, however, is to use data to control, that is, taking actions in an uncertain world with a complex dynamics in order to achieve a goal such as maximizing the relief of a patient with a chronic disease, sustainable management of natural resources, or controlling a complex power grid.


Admittedly, we are not there yet. Theoretical foundations should be laid and tools must be developed. But I believe that data-driven decision making defines a new era in human civilization, and my research moves us toward that era. For more details about data-driven control and decision making and understanding my contributions, please refer to my Research Statement or take a look at my Publications. Also if you have any questions, please feel free to contact me.

Research Interests

  1. BulletMachine Learning and Statistics: statistical learning theory, nonparametric algorithms, regularization, manifold learning, non-i.i.d. processes, online learning

  2. BulletReinforcement Learning, Sequential Decision Making, and Optimal Control: high-dimensional problems, regularized algorithms, inverse optimal control

  3. BulletRobotics: uncalibrated visual servoing, learning from demonstration, behavior-based architecture for robot control

  4. BulletLarge-scale Optimization

  5. BulletEvolutionary Computation: cooperative co-evolution, interaction of evolution and learning

News

  1. BulletSample-based Approximate Regularization is accepted at the International Conference on Machine Learning (ICML), 2014. Joint work with Philip Bachman and Doina. Extended version with proofs is here.

  2. BulletUploaded Classification-based Approximate Policy Iteration: Experiments and Extended Discussions on arXiv. A shorter version is submitted to IEEE Trans. on Automatic Control. Joint work with Doina, André, and Mohammad.

  3. BulletI left McGill University after more than two years of working on many interesting projects with Doina Precup, Joelle Pineau and other wonderful members of the Reasoning and Learning Lab. The good news is that I am joining the Robotics Institute, Carnegie Mellon University to work with Drew Bagnell.

  4. BulletWe are organizing Sequential Decision-Making with Big Data workshop at AAAI-2014.

  5. BulletTwo papers at the Neural Information Processing Systems (NIPS) conference, 2013. Learning from Limited Demonstrations and Bellman Error Based Feature Generation using Random Projections on Sparse Spaces. Joint work with Beomjoon, Doina, Joelle, Mahdi, and Yuri.

  6. BulletPhD Outstanding Thesis Award for the period of 2011-2012, Department of Computing Science, University of Alberta, 2013.

Academic blog: ThesiLog

Persian blog: ضدخاطرات

Academic and Non-academic Tweets