About Me
I’m a 2025 M.Sc. graduate from Université Paris-Dauphine–PSL’s MODO (Modélisation, Optimisation, Décision et Organisation) program. I focus on backend development and algorithmic research across operations research and AI, and I’m actively seeking my first full-time role.
I’m fluent in Python and Java and work efficiently with tools like OpenAI Codex, Claude Code, and Trae. I have research experience in operations research and AI and enjoy turning complex real-world constraints into reliable, maintainable software.
Download My Resume
Work Experience
Quantitative Research Intern – Reinforcement Learning Trading
Feb 2025 – Jul 2025First Moment, Shanghai
- Built a minute-level BTCUSDT perpetuals RL trading framework with separate train/validation and a strict out-of-sample holdout; evaluated under next-bar VWAP execution with realistic transaction costs.
- Designed a continuous-action policy with standardized time series features; mapped target position to fixed notional partial adjustments to cap turnover and suppress noise trades.
- Proposed a reduced-clip schedule for PPO with momentum-based pretraining; implemented baselines (DQN, DDPG, Bollinger, momentum) and observed superior risk-adjusted performance on the holdout.
Research Intern – Synchromodal Transportation & Resilience
May 2024 – Nov 2024Group R3 – CentraleSupélec, Gif-sur-Yvette
- Built a reproducible CPLEX/OPL environment and modeling guide; implemented a baseline intermodal MILP and a synchromodal extension where flexible services activate only after fixed capacity is saturated, to quantify resilience benefits.
- Extended the models to a time-expanded network with full cost accounting (timetables, handling/warehousing, carbon tax) and disruption-adjusted truck travel times, enabling rapid replanning under sudden outages.
- Prepared a scenario suite (baseline demand, capacity reductions, link outages—single-node to regional cascade) and compared integer-flow vs. relaxed formulations with abandonment penalties to assess trade-offs in solution quality and runtime.
Backend Development Intern
Jun 2023 – Sep 2023Merit Interactive Co., Ltd., Hangzhou
- Developed and maintained large-scale backend and data processing services using Java, Spring Boot, Spark, Amazon S3, and ClickHouse on Kubernetes.
- Participated in the full SDLC from requirements analysis to deployment, delivering multiple feature iterations on schedule.
- Built and fixed RESTful APIs with clear controller and service layers; produced internal API documentation.
Software Development – VAT Automation
Apr 2021 – Jul 2021; Jun 2022 – Sep 2022; Sep 2023 – PresentLOGEFI SERVICES, Paris
- Automated end-to-end French VAT (TVA) workflow on impots.gouv.fr (login, declaration/payment, receipts/status), serving 20k+ client entities.
- Implemented with Selenium, openpyxl, PyMuPDF, pandas, and Tkinter. Packaged PyInstaller apps for non-dev use.
- Ongoing maintenance (Sep 2023 – Present): multiprocessing crawler and CNN-based automated CAPTCHA recognition.
Skills
Programming Languages
JavaPythonGoCC++JuliaPHPTypeScriptSQLOPL
Frameworks & Platforms
SpringAngularSparkDockerKubernetesCPLEX
Developer Tools & IDEs
GitShellIBM ILOGSeleniumIntelliJ IDEAVS CodePyCharmEclipse
Databases & Big Data
PostgreSQLMySQLClickHouseHiveAmazon S3
Languages
Chinese (Native)French (Professional)English (Professional)
Education
M.Sc. in Operations Research – Modeling, Optimization, Decision & Organization
2023 - 2025Université Paris-Dauphine – PSL
- Key coursework: Linear & Integer Programming, Network Optimization, Stochastic Optimization, Robust Optimization, Multi-Criteria Decision Analysis
B.Sc. in Computer Science (MIAGE) & M1 MIAGE
2021 - 2023Université Paris-Dauphine – PSL
DUT in Computer Science
2019 - 2021Université Paris-Saclay – IUT d'Orsay
Projects
Full-Stack Q&A Web Platform
Nov 2023 – Jan 2024
AngularSpring BootMySQLDocker
- Designed and implemented a Q&A application with an Angular front-end and Spring Boot back-end backed by MySQL
- Integrated role-based authentication with Spring Security and JWT
- Containerized the stack with Docker for streamlined deployment
Learning Combinatorial Optimization over Graphs (S2V-DQN)
2024 – 2025
Reinforcement LearningGraph EmbeddingsStructure2VecDQNCPLEX
- Research focus: evaluate when learned RL heuristics remain meaningful versus exact solvers. Trained on small generic graphs; tested on special graph classes (trees, grids) where exact algorithms exploit hidden structure and are fast, and on larger random graphs that are broadly hard—probing transfer and whether the model uncovers latent patterns.
- Observed near-optimal quality on trees and grids with emergent “checkerboard” partitions on grids; comparable to CPLEX on small structured cases and much faster with near-optimal solutions on larger dense instances.