Simon Bussy

The information era has witnessed an explosion in the collection of data in a variety of fields such as medicine, biology, marketing and finance. With it have come new theoretical and algorithmic challenges, that I find fascinating. Statistical learning is a field that precisely provides a theoretical framework for the design and analysis of predictive algorithms.

Recommended reading: The elements of statistical learning. J. Friedman, T. Hastie, R. Tibshirani.

High-dimensional statistics has become the focus of increasing attention in the modern era of big data. Today, massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity. I am interested in the development of new statistical methods to separate the signal from the noise.

Recommended reading: High Dimensional Statistics, Lecture Notes. P. Rigollet, J.C. Hütter.

Survival analysis is the analysis of time-to-event data. Such data describe the length of time from a time origin to an endpoint of interest. I am particularly interested in designing new methods for medical applications such as prospective cohort studies with longitudinal data in high-dimensional settings.

Recommended reading: Survival and Event History Analysis: A Process Point of View. O. Aalen, O. Borgan, H. Gjessing.

Roughly speaking, time series forecasting is the use of a model to predict future values based on previously observed values. Time series are widely used for non-stationary data, like economic, weather, stock price, or retail sales. I am particularly interested in designing new methods to model longitudinal data in high-dimensional settings.

Recommended reading: Time series analysis: forecasting and control. G. Box, G. Jenkins, G. Reinsel, G. Ljung.

Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize a cumulative reward in a particular situation. It can be employed in various applications to find the best possible behavior or path to take in a specific situation. I am interested in designing new methods in this context as well as new applications.

Recommended reading: Reinforcement learning: A survey. L.P. Kaelbling, M.L. Littman, A.W. Moore.

Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. These methods have dramatically improved the state-of-the-art in various applications, and the research perspectives are still huge.

Recommended reading: Deep learning. I. Goodfellow, Y. Bengio, A. Courville.