I am a Computer Science master's student at ENS PSL in Paris, working at the intersection of ML and data systems.
My main interests are databases, optimization, machine learning, and solving large-scale computation problems.
Research Experience
For a detailed list of my publications, please see my research page.
My research focuses on databases optimizations, and ML for databases. I also have knowledge in DL, LLM and other commonly used AI methods.
Research Internship at University of Zürich
Under the supervision of Dan Olteanu, I have the opportunity to work on Reinforcement Learning methods for Adaptive Query Processing.
Research Internship at NTU Singapore
Under the supervision of Gao Cong and Jiachen Shi, my work focuses on developing new index recommendation opportunities for modern database systems.
Research Project at ENS-PSL
I continued my summer research on query enumeration as part of the M1 curriculum, supervised by Luc Segoufin within the Valda team at ENS.
Research Internship at INRIA
I worked with Nofar Carmeli and David Carral on the enumeration of acyclic conjunctive queries with self-joins. A presentation of this work is available here.
Projects
You can also find my main public projects on github.com/CRouvroy.
Reinforcement Learning for Autonomous Cars
A 2D car environment trained with reinforcement learning to follow complex routes.
Hypothetical Index for Column-Oriented Databases
A lightweight what-if index estimator for column-oriented databases.
Diffusion Models for Galaxy Generation
Diffusion models implemented from scratch for galaxy generation.
GNN for Fake News Detection
Graph neural networks for fake news detection on Twitter.
RISC CPU in Netlist with Custom Assembly
A RISC processor implemented in Netlist with custom assembly.
PureScript Compiler
A compiler for a subset of PureScript, from parsing and type inference to x86 code generation.
Git Clone in C++
A customizable Git clone in C++ with server support, branches, and merges.
Triton FlashAttention / LoRA and KV Store / LLM for Code Generation
Studied each part for the MVA course on LLM for Code and Proof.
Curriculum Vitae
My full Curriculum Vitae is available for download here.
Here are my primary areas of interest and the related courses I completed. All courses were completed with the highest distinction (mention très bien), except for those in italics which are part of my current curriculum.
My academic background has provided me with both the practical skills for high-efficiency coding (e.g., building a compiler, a Git clone from scratch, and many AI projects) and the deep theoretical knowledge in areas like Linear Algebra, Complexity Theory, and Database Theory.
Artificial Intelligence
- Learning Theory From First Principles (IASD, Francis BACH)
- Computer Vision (Jean Ponce)
- Reinforcement Learning (IASD, Olivier CAPPÉ)
- Convex Optimisation (Adrien Taylor)
- Optimization for machine learning (IASD, Clément W. ROYER)
- Large Language Models(IASD, Alexandre ALLAUZEN)
- LLM for Code and Proof (MVA, Marc LELARGE & Nathanaël Fijalkow)
- Deep Learning For Image Analysis (IASD, Étienne DECENCIÈRE)
- Statistical Learning (Alessandro Rudi)
- Geometric Data Analysis (MVA, Jean Feydy)
- Deep Learning (Kevin Scaman)
- AI Safety Atlas (CesIA)
Data
- Research project at NTU Singapore. Supervised by CONG Gao and SHI Jachen.
- Data Acquisition, Extraction, and Storage (IASD, Pierre SENELLART)
- Database Theory (Pierre Senellart)
- Research Internship at INRIA. Supervised by Nofar Carmeli and David Carral
- Supervised research project at ENS-PSL. Supervised by Luc Segoufin.
Applied Computer Science
- OS (Timothy Bourke)
- Numerical System
- Compilation (Jean-Christophe Filliâtre)
Formal & Algorithmic
- Algorithmic (Tatiana Starikovskaya, Pierre Aboulker)
- Lambda-Calculus (logic) (ENS Paris-Saclay, Jean Goubault-Larrecq)
- Formal Language & complexity theory (Michaël Thomazo)
- Advanced Complexity (MPRI)
- Combinatorial Optimisation (Chien-Chung Huang)
Contact
You can reach me via email at [given name].[family name]@ens.psl.eu.
You can also find me on LinkedIn.