I study what AI models hear and what they know: deepfake speech detection, recognising distress in spoken language, and how language models represent the world.
I'm a second-year Computer Science student at University College London, working where audio meets language models.
With Yupei Li and Prof. Björn W. Schuller, I co-first-authored work on efficient audio deepfake detection (IJCNN 2026) and on LLMs that recognise depression from speech (Frontiers in Computer Science, 2025). In summer 2026 I was a research intern at UCL with Prof. Lewis D. Griffin, probing how LLMs represent relationships between real-world entities.
Before research there was competitive programming (USACO Platinum), and I still enjoy building things end to end, from multi-agent systems to small desktop tools.
How models listen, and what they know.
How relationships between real-world entities are represented inside LLMs, measured with controlled fine-tuning and cross-domain evaluation, as a route to understanding the world models they acquire.
Bio-inspired dropin and neuroplasticity mechanisms that dynamically adjust the number of neurons in selected layers, making deepfake audio detectors more efficient and more accurate.
Injecting clinical psychological knowledge into LLMs, alongside Wav2Vec audio features, to recognise depression from spoken interviews.
Selected papers
* equal contribution
Where I've worked
UCL Department of Computer Science · supervised by Prof. Lewis D. Griffin
Designed controlled fine-tuning and cross-domain evaluation experiments on entity-specific corpora, and built reproducible pipelines measuring how world knowledge transfers across related entities.
With Yupei Li & Prof. Björn W. Schuller · Remote
Audio AI, affective computing and NLP. Co-developed neuron-level dropin and plasticity for deepfake detection, and a knowledge-enhanced LLM pipeline for depression recognition.
University College London
Year 1 weighted average 70% (First-Class standard). Algorithms, Theory of Computation, Computer Systems, Object-Oriented Programming.
Things I've built
A multi-agent network with live pipeline visualisation: inspect each agent's intermediate state and edit the execution graph at runtime. Non-English prompts slipped past filters that held in English.
Python · LangChain · AWS Bedrock
Real-time transcription that answers text and voice queries in the matching modality, persisting extracted knowledge to a Neo4j graph. New tools register without touching the orchestrator.
Python · Neo4j · LangChain
A desktop utility that makes sending emoji on WeChat fast. My most-starred open-source project.
C++ · Windows
Implementation of neuron-level dropin and plasticity for audio deepfake detection on ResNet, gated RNN and Wav2Vec backbones.
Python · PyTorch
Along the way
I'm looking for Summer 2027 internships in ML research, research engineering, or quantitative roles, and I'm always happy to talk about speech and audio AI, LLM evaluation, or competitive programming.