PhD dissertation, Université Gustave Eiffel. (Defended on June 20, 2022) Slides of the defense.

#### Publications

Rapid quantitative magnetization transfer imaging:Utilizing the hybrid state and the generalized Bloch model, **Magnetic Resonance in Medecine**, 2023.

Reliable Time Prediction in the Markov Stochastic Block Model, **ESAIM: Probability & Statistics**,** **2022. **Python Notebook**

The Random Geometric Graph: Recent developments and perspectives, with Yohann De-Castro. **High Dimensional Probability volume 9**, 2022

Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks, with Yohann De-Castro. **Electronic Journal of Statistics**, 2022. **Python Notebook**

Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains, with Yohann De-Castro and Claire Lacour. **Bernoulli, **2022.

Three rates of convergence or separation via U-statistics in a dependent framework, with Yohann De-Castro and Claire Lacour. **Journal of Machine Learning Research**, 2022

Cramér-Rao bound-informed training of neural networks for quantitative MRI, with Xiaoxia Zhang, Kangning Liu, Sebastian Flassbeck, Cem Gultekin, Carlos Fernadez-Granda and Jakob Assländer. **Magnetic Resonance in Medicine**, 2022

**Preprints**

SIGLE: a valid procedure for Selective Inference with the Generalized Linear Lasso, with Yohann De-Castro. 2022

**Conference papers**

Rapid quantitative magnetization transfer imaging: utilizing the hybrid state and the generalized Bloch model, J. Assländer, C. Gultekin, X. Zhang, Q. Duchemin, K. Liu, R. WE Charlson, T. Shepherd, C. Fernandez-Granda, and S. Flassbeck. Proc. 31th Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2022 (** selected for oral presentation**)

Optimized dimensionality reduction for parameter estimation in MR fingerprinting via deep learning Q. Duchemin, K. Liu, C. Fernandez-Granda, J. Asslaender. Proc. 28th Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2020 (** selected for oral presentation**)

Watch the video presenting our work and read the slides of the presentation.

Concentration inequality for U-statistics for uniformly ergodic Markov chains, and applications, Q.Duchemin, Y. De Castro & C. Lacour. Bernoulli-IMS 10th World Congress in Probability and Statistics 2021 (** selected for oral presentation**) Watch the video presenting our work, read the slides of the presentation and read our poster.

**Awards**

**Maths & Computer science PhD Prize****2023**

Each year, Paris-Est Sup’s Thesis Prizes are awarded to each of its doctoral schools for the best work by the site’s PhDs, shortlisted from the previous year’s graduates for its quality, originality and social relevance.

**André Pasquet 2020 Prize**Prize that rewards the best student graduating from the engineering school “Ecole des Ponts”. An article was published with my acceptance speech: https://www.fondationdesponts.fr/prix-pasquet-quentin-duchemin/

**Activities**

Reviewing : AISTATS 2021, Statistics and Probability Letters (in 2021), Bernoulli Journal (in 2022), NeurIPS (in 2023)

Organization of seminars : PhD seminars of UGE

Attended Conferences:

– Theoretical Computer Science Spring School: Machine Learning, May 2022

– Colloque Jeunes Probabilistes et Statisticiens, October 2021

– 12th International Conference on Multiple Comparison Procedure, September 2021

– *IMS 10th World Congress in Probability and Statistics 2021*, July 2021 (selected for a presentation)

– *Franco-Dutch meeting « Bézout-Eurandom »*, IHP Paris, July 2021 (invited as speaker)

– *Convex geometry and random matrices in high dimensions*, June 2021

– *International conference on Mixtures, Hidden Markov models and Clustering*, June 2021

– *Robustness* and computational efficiency of algorithms in statistical learning, CIRM Luminy, December 2020

– *ICML*, July, 2020

**Talks**

01/2024 at EPFL (SDSC Academic group seminar): Exploring Advanced Modeling Approaches for (Structured) Temporal Signals

11/2023 at EPFL (SDSC PhD Fellows Workshop): Transformer Models for the Estimation of Transit Time in Watersheds

10/2023 at Université d’Angers: Concentration inequality for U-statistics in a dependent framework.

08/2023 at EPFL: Factorization Machines for recommendation systems

04/2023 at ETH Zurich: Numerical Gaussian Processes

10/2022 at SDSC (EPFL): Community detection in the Stochastic Block Model

05/2022 at Inria Paris (Dyogene Team): Post Selection Inference in the Generalized Linear Lasso

04/2022 at Université d’Orsay: Concentration inequality for U-statistics and applications

03/2022 at Université Paris-Nanterre (Séminaire Modal’X): Concentration inequality for U-statistics and applications

01/2022 at LAMA UGE: Introduction to optimal transport: connections between Monge formulation, Kantorovitch relaxation and its dual. (Reading group)

12/2021 at LJK Grenoble: The MRGG model: A Growth Model for Temporal Dynamic Networks

11/2021 at CERMICS (ENPC): Geometry detection in Random Geometric Graphs (Part 2/2)

10/2021 at CERMICS (ENPC): Geometry detection in Random Geometric Graphs (Part 1/2)

07/2021 at IHP Paris for Franco-Dutch meeting « Bézout-Eurandom »

07/2021 at IMS 10th World Congress in Probability and Statistics: MCMC method for the estimation of spectra of signed integral operator.

04/2021 at Télécom Paris: Concentration inequality for U-statistics in a dependent framework.

03/2021 at Université de Rouen: Concentration inequality for U-statistics in a dependent framework.

02/2021 at Université de Caen: Introduction to random graphs.

10/2020 at ENS Lyon (DANTE team): Link prediction in random graphs.

04/2020 at Cermics ENPC: The Stochastic Block Model: A survey.