Zhoujing CHEN

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 2025

First 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 2024

Group 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 2023

Merit 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 – Present

LOGEFI 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 - 2025

Université 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 - 2023

Université Paris-Dauphine – PSL

DUT in Computer Science

2019 - 2021

Université 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.