Resume
Academic Achievements
- Several papers under review or published, including 'EvoEmo: Towards Evolved Emotional Policies for LLM Agents in Multi-Turn Negotiation', 'EQ-Negotiator: Emotion Policing Personas for Anti-Manipulation in Credit Collection Dialogues', 'ExpoTab: One-Step Mixed-Type Tabular Data Generation using Manifold Learning'. Additionally, published 'Efficient and privacy-preserved link prediction via condensed graphs' in Expert Systems with Applications and 'Haplo-caller: A deep learning method for haplotype identification from mixed clonal samples' in VeriXiv.
Research Experience
- Currently a PhD student at the University of Cambridge working on data understanding and generation. Previous research experience includes developing a DP-based personalized clustering federated learning method while pursuing his MPhil at the University of Cambridge.
Education
- PhD candidate at the University of Cambridge, Department of Engineering, focusing on data understanding and generation; MPhil in Engineering from the University of Cambridge, where his research focused on a DP-based personalized clustering federated learning method; First-Class Honours degree in Engineering from the University of Birmingham, with an undergraduate thesis exploring the application of deep reinforcement learning algorithms for autonomous robotic dismantling tasks.
Background
- Research interests include unifying data understanding and data generation through latent-space modeling with Diffusion-based Generative Models; focusing on building principled frameworks that bridge representation learning and controllable generation across heterogeneous data regimes such as natural language, mixed-type tabular, structured relational/graph data, and emerging multimodal combinations.
Miscellany
- Recipient of Conference Scholarship from the Department of Engineering, Cambridge University, and Queens College PhD Scholarship, Cambridge University. Served as a reviewer for multiple IEEE journals and conferences.