Xudong Wu

Ph.D. Student in Reinforcement Learning, University of Hong Kong

Previously:
BSc (Hons) in Mathematics & Statistics, University of Edinburgh
BSc in Information and Computing Science, Dalian University of Technology

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About Me | Education | Research
Academic Background | Honors

The University of Hong Kong, Hong Kong SAR (Starting Sep. 2025)

• Ph.D. Student
• Research areas: Reinforcement Learning (RL), Large Language Models (LLMs), Embodied AI
• Advisor: Prof. Jiayu Chen, The University of Hong Kong
• Co-advisor: Prof. Vaneet Aggarwal, Purdue University
• Co-advisor: Prof. Wenjie Huang, The University of Hong Kong

University of Edinburgh, Edinburgh, UK (Sep. 2023 – Jul.2025)

• BSc (Hons) in Mathematics and Statistics
• First-Class Honours (Equivalent to 4.0/4.0 GPA)

Dalian University of Technology, Dalian, China (Sep. 2021 – Jun. 2023)

• BSc in Information and Computing Science
• Average score: 89.9/100

University of Edinburgh, Edinburgh, UK (Jan. 2025 – Mar. 2025)

• Position: Research Collaborator
• Project: Dynamic Self-Rewarding for Medical Large Language Models (Med-LLMs)
• Research Focus:
  • Developed a dynamic self-rewarding framework for aligning medical LLMs without human-annotated supervision
  • Integrated a two-tier judge system where ChatGPT-4o dynamically refines evaluation prompts to mitigate reward misspecification and scoring bias
  • Executed multi-round Direct Preference Optimization (DPO) to align model behavior through self-generated preference pairs and adaptive reward modeling
  • Fine-tuned and evaluated LLMs (Mistral-7B) on domain-specific datasets (HealthCareMagic, PubMedQA, MedMCQA), targeting empathy, factuality, and coherence
  • Conducted task-specific benchmarking and error analysis to uncover performance bottlenecks due to hallucination and distributional drift across iteration stages

University of Edinburgh, Edinburgh, UK (Aug. 2024 – Apr. 2025)

• Position: Honours Dissertation / Final Year Project
• Supervisor: Dr. Amanda Lenzi
• Thesis Title: A Comparative Study of Simulation-Based Inference Algorithms
• Research Focus:
  • Benchmarked three cutting-edge Simulation-Based Inference (SBI) algorithms—BayesFlow, Sequential Neural Likelihood (SNL), and Affine Flow Matching (AFM)—on both synthetic and structured real-world inference tasks
  • Demonstrated that AFM outperforms amortized and sequential methods in capturing spatial structure in high-dimensional Poisson–CAR disease mapping models
  • Designed a robust evaluation framework using recovery line metrics and ECDF-based posterior calibration to analyze estimation accuracy and uncertainty quantification
  • Identified a key trade-off in joint parameter inference: increasing dimensionality improves model expressiveness but amplifies uncertainty due to parameter interaction
  • Implemented a full end-to-end SBI workflow and published open-source code: github.com/xudongwu-0/SBI-comparison

University of California, Irvine, USA (June 2024 - Sep. 2024)

• Position: Summer Research Assistant
• Supervisor: Prof. Chen Li (IEEE Fellow)
• Project: Optimizing Texera, a machine learning-based data analysis workflow platform
• Responsibilities:
  • Integrated AI-driven automation for workflow optimization, enabling seamless machine learning pipeline execution
  • Developed an automated report generation system that converts data analysis workflows into structured insights
  • Enhanced Texera’s data cleaning and visualization capabilities to improve model interpretability

University of Edinburgh, Edinburgh, UK (Feb. 2024 - May 2024)

• Position: Research Assistant in Uncertainty Economics
• Research Focus:
  • Analyzed the economic impact of uncertainty during the COVID-19 pandemic using stochastic models
  • Implemented Monte Carlo simulations and Bayesian inference techniques for probabilistic estimation
  • Developed computational tools for visualizing uncertainty in economic forecasting

University of Edinburgh, Edinburgh, UK (Sep. 2023 - Dec. 2023)

• Position: Project Leader in Differential Equations
• Research Focus:
  • Developed a system of ordinary differential equations (ODEs) to model bacterial infections and antibiotic treatments
  • Used Fourier series analysis and Laplace transforms to predict bacterial resistance patterns
  • Optimized drug treatment schedules using numerical simulations

Dalian University of Technology, Dalian, China (Dec. 2022)

• Position: Research Project in Mathematical Modeling
• Research Focus:
  • Developed a population dynamics model incorporating fertility rate adjustments
  • Improved predictive accuracy by incorporating age-stratified birth rate variations
  • Performed parameter sensitivity analysis to optimize demographic forecasting

University of Edinburgh, Edinburgh, UK (Sep. 2023 - Jun. 2025)

• BSc in Mathematics and Statistics, First-Class Honours
• Mathematical & Optimization Courses:
  • Numerical Ordinary Differential Equations – Stability & convergence analysis, applied to RL optimization
  • Honours Differential Equations – Applied in stochastic dynamic programming & Bellman equations
  • Honours Complex Variables – Conformal mapping & contour integration, useful for stochastic control
  • Honours Analysis – Functional analysis and variational methods, applicable to policy optimization
• Statistics & Probabilistic Modelling:
  • Financial Mathematics – Stochastic processes, Ito calculus, and Black-Scholes model
  • Stochastic Modelling – Markov chains, Poisson processes, Brownian motion, key concepts for RL
  • Statistical Computing – Monte Carlo methods, MCMC, and Bayesian inference
  • Statistical Methodology – Hypothesis testing, regression, and statistical decision theory
• Programming & Computational Methods:
  • Applied Statistics – Hands-on experience with real-world data, Bayesian updating in ML
  • Python, C++, MATLAB – Numerical computing, algorithm development for RL applications

Dalian University of Technology, Dalian, China (Sep. 2021 - Jun. 2023)

• BSc in Information and Computing Science, GPA: 89.2/100
• Mathematical & Theoretical Foundations:
  • Mathematical Analysis (1-3) – Real & complex analysis, measure theory fundamentals
  • Ordinary Differential Equations – Theoretical and numerical solutions to dynamic systems
  • Abstract Algebra – Group theory, rings, and fields, applicable in cryptography & optimization
  • Complex Function Theory – Applications in function approximation & conformal mapping
• Probability & Statistical Modelling:
  • Probability & Mathematical Statistics – Statistical inference, Bayesian learning, decision theory
  • Real Analysis – Measure theory and Lebesgue integration, foundation for stochastic processes
• Computational & Algorithmic Skills:
  • C++ Programming – Algorithm design, object-oriented programming
  • Python Programming – Data structures, numerical computation, scientific computing
  • Mathematical Modelling – Applied techniques in RL-based decision-making and optimization

• First-Class Scholarship - Dalian University of Technology, 2022-2023 (Top 5%)
• Dual Degree Student Scholarship - University of Edinburgh, 2022-2023
• International Study Scholarship - Dalian University of Technology, 2022-2023