Our focus is on new theoretically and computationally solid techniques for a wide class of statistical problems through a new “operational” framework for analysis of inferential routines which relies on extensive use of the methodology involved in developing modern optimization.
Our research agenda includes, but does not reduce to
design of efficient testing routines with applications to diagnostics from heterogeneous data
new methods of recovery of signals from nonlinear observations with application in classification and identification and inverse problems for non-linear PDE
design of robust procedures with focus on iterative (adversarial) adaptive techniques
large-scale online algorithmic implementation of inference routines
adaptive techniques for network inference from multi-sensor data, application to identification of temporal dynamics in biomedical data, (stochastic) optimization methods which allow efficient use of streaming data, …
ACTIVITIES
The chair is collaborating with French companies Biomerieux and ST Microelectronics through supervision of 2 CIFRE PhD’s. We continue established collaborations with “MAGNET“ chair (by joint membership of E. Devijver), and chairs “Towards More Data Efficiency in Machine Learning” and “Optimization & Learning.” New collaborations are established with ENSAE-CREST through joint supervision of PhD projects.
- Efficient statistical routines motivated by convex optimization
- Algorithms of stochastic and deterministic optimization
- Stochastic system identification and time series analysis
- Causal and functional data inference, semi-supervised and unsupervised learning
- Inter-disciplinary collaborations, namely, in healthcare and electronic circuit design
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