Hi there! My name is Zihao GUO, or you can call me Eric. I'm currently a PhD student @ Institut Polytechnique de Paris under the supervision of Dr. Andrea ARALDO and Dr. Mounim EL YACOUBI. My research topic is preserving graph performance in an exogenously perturbed environment.
I love everything about statistics and optimization, and I’m excited to share my knowledge, passion, and tools with collaborators.
Research
My research spans multiple threads focused on optimization under uncertainty, with an emphasis on large-scale decision-making in complex network systems.
- Formulating resilient optimization frameworks for vehicle routing and transport network design, prioritizing solution stability and service efficiency under multi-scale uncertainties.
- Advancing high-performance decomposition techniques, such as Column Generation, to tractably solve high-dimensional stochastic and combinatorial optimization problems.
- Integrating Deep Reinforcement Learning (DRL), Graph Neural Networks (GNNs), and Large Language Models (LLMs) to automate heuristic discovery and co-optimize real-time Learning-to-Optimize (L2O) pipelines for complex decision systems.
Publications
Full list on Google Scholar
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Predict-then-Optimize Framework for Public Transport Line Redesign under Fluctuating Traffic Conditions [Link] Zihao Guo, Andrea Araldo, Faycal Touzout, Mounim El-Yacoubi Under review |
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Data Sampling-driven Adaptive Modification of Bus Routes Under Time-Varying Road Conditions [Link] Zihao Guo, Andrea Araldo, Mounîm A El Yacoubi The 19th Learning and Intelligent Optimization Conference, 2025 |
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Restoring Accessibility During Urban Rail Disruptions via Bus Network Redesign [Link] Zihao Guo, Andrea Araldo, Mounîm A El Yacoubi Transportation Research Board (TRB) 104th Annual Meeting, 2025 Transportation Research Record |
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Rate variation and recurrent sequence errors in pandemic-scale phylogenetics [Link] Nicola De Maio, Myrthe Willemsen, Zihao Guo, Abhratanu Saha, Martin Hunt, Nhan Ly-Trong, Bui Quang Minh, Zamin Iqbal, Nick Goldman Nature Methods |
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A Sinh–Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems [Link] Xiong Wang, Yaxin Wei, Zihao Guo, Jihong Wang, Hui Yu, Bin Hu Biomimetics |
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SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization [Link] Zihao Guo, Yueying Cao ACM EITCE 2022. |
Education
Nantes Université
Master of Science degree · 2022 — 2023
Specialization in Mathematics and Applications.
Ecole centrale de Nantes
Engineer's degree - Generalist Engineer · 2021 — 2023
Focus on Applied Mathematics (Statistics & Data Science) and Research & Development.
Industrial Experience
Algorithm Engineer 2025.8 — 2025.11
Internship
@ Huawei — Shanghai, CHINA
Data Scientist 2022.4 — 2022.9
Internship
@ IFP Energies nouvelles — Rueil-Malmaison, FRANCE
Algorithm Developer 2023.4 — 2023.9
Internship
@ European Bioinformatics Institute — Cambridge, UK
Data Scientist 2021.11 — 2022.1
Internship
@ Tencent — Beijing, China (Remote)
Teaching
MSc - Intelligence artificielle et données
Teaching Assistant @ ENSTA Paris — Palaiseau, FRANCE
Languages
Chinese: Native language
English: Fluent (GRE 326)
French: Fluent (DALF C1)
Research Scientist
ContractOct 2023 - Oct 2026
- Developed LLM-based systems for algorithm generation and optimization using SFT and RLFT.
- Integrated traffic forecasting with multi-objective optimization to enable proactive public transport network redesign under uncertainty.
- Designed vehicle routing methods under uncertainty with MILP and stochastic optimization.
Shenyang University of Aeronautics and Astronautics
Bachelor of Engineering · 2017 — 2021
Specialization in Mechanical Engineering.
Algorithm Engineer — Huawei
2025.8 — 2025.11 · Shanghai, CHINA
- Developed a multimodal contrastive learning framework integrating 3D point clouds and wireless channel data to predict channel characteristics at target locations from surrounding environmental observations, aligning both modalities in a shared representation space and validating predictions against ground-truth channel measurements.
- Proposed a non-contiguous resource block allocation algorithm based on integer partitioning, extending the conventional RIV-based contiguous allocation mechanism to support more flexible spectrum resource allocation.
Data Scientist — IFP Energies nouvelles
2022.4 — 2022.9 · Rueil-Malmaison, FRANCE
- Analyzed large-scale public transport validation data stored and queried in MongoDB, and developed a dashboard to monitor mobility patterns and key indicators.
- Compared observed validation data with MATSim agent-based transport simulations to identify discrepancies and assess simulation accuracy.
- Developed time-series forecasting models (SARIMAX, NeuralProphet) to analyze influencing factors, predict passenger flows, and correct statistical estimates.
Supervised by Dr. Pierre Michel.
Algorithm Developer — EMBL-EBI
2023.4 — 2023.9 · Cambridge, UK
- Developed and evaluated large-scale phylogenetic algorithms for simulating genome evolution along large, short-branch phylogenetic trees, supporting datasets of up to 500,000 genomes.
- Built an automated Python/R/Bash pipeline for simulation, cluster execution, result collection, and benchmarking across multiple experimental configurations.
- Evaluated topological accuracy, likelihood, runtime, and memory consumption over repeated experiments, achieving a >100× speedup compared with the reference method while maintaining competitive phylogenetic accuracy.
Supervised by Dr. Nicola De Maio.
Data Scientist — Tencent
2021.11 — 2022.1 · Beijing, China (Remote)
- Developed a customer churn prediction model on approximately 100,000 customer records, engineering 30+ behavioral and usage features to identify high-risk users and support targeted retention strategies.
- Trained and compared 4 classification models using scikit-learn, optimized them with GridSearchCV, and achieved an ROC-AUC of approximately 0.86, with the top 20% highest-risk customers capturing around 55% of observed churners.
Supervised by Dr. Zhao Zhang.