postdoctoral research position

Through the ERC Synergy grant OCEAN (On intelligenCE And Networks: Synergistic research in Bayesian Statistics, Microeconomics and Computer Sciences), I am seeking one postdoctoral researcher with an interest in Bayesian federated learning, distributed MCMC, approximate Bayesian inference, and data privacy.

The project is based at Université Paris Dauphine, on the new PariSanté Campus.  The postdoc will join the OCEAN teams of researchers directed by Éric Moulines and Christian Robert to work on the above themes with multiple focus from statistical theory, to Bayesian methodology, to algorithms, to medical applications.

Qualifications

The candidate should hold a doctorate in statistics or machine learning, with demonstrated skills in Bayesian analysis and Monte Carlo methodology, a record of publications in these domains, and an interest in working as part of an interdisciplinary international team. Scientific maturity and research autonomy are a must for applying.

Funding

Besides a 2 year postdoctoral contract at Université Paris Dauphine (with possible extension for one year), at a salary of 31K€ per year, the project will fund travel to OCEAN partners’ institutions (University of Warwick or University of Berkeley) and participation to yearly summer schools. University benefits are attached to the position and no teaching duty is involved, as per ERC rules.

The postdoctoral work will begin 1 September 2023.

Application Procedure

To apply, preferably before 31 May, please send the following in one pdf to Christian Robert (bayesianstatistics@gmail.com).

• a letter of application,
• a CV,
• letters of recommendation sent directly by recommenders

top off…

religions in the classroom

Two recent stories reported in the New York Times about U.S. professors being fired for posting art pieces that students or parents found offensive to their beliefs. One (above) was a painting within a 14th-century Islamic history book supposed to represent G and M. As showed [with much warning] during an art class at Hamline College. The other is the (monumental) Renaissance Michelangelo’s David [exhibited a la Galleria dell’Academia, in Florence]. Whose posting during a Florida sixth-grader class on Renaissance art led to accusations of pornography! These extreme cases of religious beliefs taking over the classroom (and rationality!) remind me of the difference I noticed between teaching in D and W, since in the former institution, classes and tests can take place any day that is not a public holiday, following general secular rules in French public institutions, while in the latter, every possible effort should be made (by the University) to provide an alternative test…

latest math stats exam

As I finished grading our undergrad math stats exam (in Paris Dauphine) over the weekend, which was very straightforward this year, the more because most questions had already been asked on weekly quizzes or during practicals, some answers stroke me as atypical (but ChatGPT is not to blame!). For instance, in question 1, (c) received a fair share of wrong eliminations as g not being necessarily bounded. Rather than being contradicted by (b) being false. (ChatGPT managed to solve that question, except for the L² convergence!)

Question 2 was much less successful than we expected, most failures due to a catastrophic change of parameterisation for computing the mgf that could have been ignored given this is a Bernoulli model, right?! Although the students wasted quite a while computing the Fisher information for the Binomial distribution in Question 3… (ChatGPT managed to solve that question!)

Question 4 was intentionally confusing and while most (of those who dealt with the R questions) spotted the opposition between sample and distribution, hence picking (f), a few fell into the trap (d).

Question 7 was also surprisingly incompletely covered by a significant fraction of the students, as they missed the sufficiency in (c). (ChatGPT did not manage to solve that question, starting with the inverted statement that “a minimal sufficient statistic is a sufficient statistic that is not a function of any other sufficient statistic”…)

And Question 8 was rarely complete, even though many recalled Basu’s theorem for (a) [more rarely (d)] and flunked (c). A large chunk of them argued that the ancilarity of statistics in (a) and (d) made them [distributionally] independent of μ, therefore [probabilistically] of the empirical mean! (Again flunked by ChatGPT, confusing completeness and sufficiency.)

All about that [Detective] Bayes [seminar]

On 10 January 2023, at 14:00, Campus Pierre et Marie Curie (Sorbonne Université), Room 15.16-309, an All about that Bayes seminar presentation by Daniele Durante, visiting Paris Dauphine this month:

Daniele Durante (Bocconi University) – Detective Bayes: Bayesian nonparametric stochastic block modeling of criminal networks

Europol recently defined criminal networks as a modern version of the Hydra mythological creature, with covert structure and multifaceted evolutions. Indeed, relationships data among criminals are subject to measurement errors, structured missingness patterns, and exhibit a complex combination of an unknown number of core-periphery, assortative and disassortative structures that may encode key architectures of the criminal organization. The coexistence of these noisy block patterns limits the reliability of community detection algorithms routinely-used in criminology, thereby leading to overly-simplified and possibly biased reconstructions of organized crime topologies. In this seminar, I will present a number of model-based solutions which aim at covering these gaps via a combination of stochastic block models and priors for random partitions arising from Bayesian nonparametrics. These include Gibbs-type priors, and random partition priors driven by the urn scheme of a hierarchical normalized completely random measure. Product-partition models to incorporate criminals’ attributes, and zero-inflated Poisson representations accounting for weighted edges and secrecy strategies, will be also discussed. Collapsed Gibbs samplers for posterior computation are presented, and refined strategies for estimation, prediction, uncertainty quantification and model selection will be outlined. Results are illustrated in an application to an Italian Mafia network, where the proposed models unveil a structure of the criminal organization mostly hidden to state-of-the-art alternatives routinely used in criminology. I will conclude the seminar with ideas on how to learn the evolutionary history of the criminal organization from the relationship data among its criminals via a novel combination of latent space models for network data and phylogenetic trees.