About Me

Hi, I am Lizhan Hong (Apollo洪历展).

I am currently an Engineering Student (M.Sc. Level) in Applied Mathematics at École Polytechnique (Paris, France), and a B.S. candidate in Computer Science & French at Shanghai Jiao Tong University.

My academic journey is a blend of rigorous mathematical theory and practical engineering application. I am passionate about bridging the gap between theoretical models and real-world financial or industrial systems.

[News] I am actively seeking a Summer Internship (June–September 2026) in Quantitative Finance or Data Science.

Here is my Resume in Chinese, in English, and in French.

Research Interests

My research focuses on Quantitative Finance, Machine Learning, and Digital Twins. I am particularly interested in:

Schrödinger Bridge Diffusion Models & Markovian properties.
Quantitative Trading Strategies (Multi-factor models, LSTM, Covariance shrinkage).
AI for Science (Nuclear Reactor Digital Twins, Metaheuristic algorithms).

Selected Experience & Projects

1. Quantitative Trading Strategy (Alpha Generation)

Shanghai Advanced Institute of Finance (SAIF)

I developed and backtested robust multi-factor equity strategies targeting the CSI 300 universe.

Methodology: Integrated traditional financial indicators (MACD) with advanced Machine Learning models (LSTM) and statistical techniques (Ledoit-Wolf covariance shrinkage).
Performance:
- Achieved a Sharpe Ratio of 1.5 and >100% 3-year return.
- Controlled Max Drawdown within 10% (CSI 300 daily).
Risk Control: Mitigated overfitting and ensured stable out-of-sample performance using L1/L2 regularization and strict turnover control (considering 0.1% commission).

2. Generative Financial Modeling (Stochastic Processes)

École Polytechnique

This project focuses on the theoretical derivation and simulation of Schrödinger Bridge Diffusion Models for financial time series.

Theory: Conducted research on the derivation and Markovian properties of discrete Schrödinger bridges.
Implementation: Implemented Sinkhorn Algorithm, Iterative Proportional Fitting (IPF), and Schrödinger Bridges Diffusion.
Application: Applied these models for high-fidelity financial time series simulations and risk management scenarios.

3. Digital Twin & AI for Engineering (Past Program)

AISEA Laboratory | SJTU Incubator

As a Project Leader and Research Assistant, I led the development of operational digital-twin platforms.

Algorithms: Designed a novel hybrid KNNLHS algorithm (latency <0.1s) and utilized SVD-Autoencoder for Model Order Reduction.
Application: Developed a modular Reactor Operation Digital Twin using metaheuristic algorithms (Fast Simulated Annealing, Cuckoo Search).

Concept Demo (Sheng Xuanhuai Innovation Contest Silver Award):

Digital Fitness & Interaction System: We applied our digital character modeling and LLM-driven interaction technology to gym environments.

Publications

Decision tree based parameter identification and state estimation: Application to Reactor Operation Digital Twin.
- Nuclear Engineering and Techniques • 2025 • Hong, L. et al
Optimizing near-carbon-free nuclear energy systems: advances in reactor operation digital twin through hybrid machine learning algorithms.
- Nuclear Science and Techniques • 2024 • Hong, L. et al
A Noise and Vibration Tolerant Resnet for Field Reconstruction with Sparse Sensors.
- Communications in Computational Physics • 2024 • Hong, L. et al

Skills

Programming: Python, HTML, SQL, MATLAB, C, Java
Deep Learning: Tensorflow, PyTorch, Keras
Tools: NumPy, Pandas, pyMOR, Scikit-learn, Git
Languages: Mandarin (Native), English (Fluent), French (Fluent)

Personal Specialty & Hobbies

Outside of the lab and library, I am dedicated to physical excellence and leadership.

Sports: CrossFit, Hyrox, Rugby, American Football.
Bodybuilding: I am the Champion of the 2023 Shanghai Intercollegiate Cup.
Leadership: I have served as the leader of the Calisthenics association and captain of the SPEIT-Fitness team.

“Grit: Back broken from a gym injury, but I reinforced my core and trained harder.”