about

Hello! I am a MSc Machine Learning student at University College London and a research intern in the Artificial Intelligence and Machine Learning group at Microsoft Research, I am interested in understanding intelligence through the scope of both machine learning theory and theoretical neuroscience. My research focuses on developing novel machine learning algorithms, drawing inspiration from biological systems, particularly how neural populations process information about uncertainty and make actions. I work in various topics in machine learning including generative models, reinforcement learning, and variational inference , hoping to create machine intelligence that emulate the adaptability of natural intelligence. I obtained my BSc in Psychology, also at UCL, where I worked on Bayesian decision-making and reinforcement learning to model human behaviour. Prior to this, I briefly worked as a photographer and cinematographer as a student at the Beijing Film Academy.

more info

right now
- working on a one-step diffusion project with Prof. David Barber...

education
- MSc Machine Learning, UCL - ongoing
- BSc Psychology, UCL (1st class) - 2023

before this
- BA Cinematography and Film Production, Beijing Film Academy (Dropout) - 2019

worked for/with
- Prof. Jinwen Ma - Research Assistant, EM algorithm, Peking University - 2023
- Prof. Maarten Speekenbrink - BSc Thesis, Reinforcement Learning and Uncertainty, UCL - 2023
- Prof. Fred Dick - Research Assistant, Auditory Neuroscience, UCL - 2022
- Prof. Fred Dick and Dr Magdalena Kachlicka, Research Project, Representation Learning for Natural Sound Categories, UCL - 2022

exhibitions
- Film Project - Beijing - Three Shadows Photography Art Centre - 2020

my face

• Hamiltonian Variational Auto-Encoder Combining Hamiltonian Importance Sampling with Variational Auto-Encoders.
  2024deep generative models, importance sampling, hamiltonian monte carlo, normalizing flow
  
• Modelling Inequity Aversion with Multi-Agent Reinforcement Learning Exploring population dynamics of agents with inequity aversion in sequential social dilemmas.
  2024reinforcement learning, game theory, social psychology
  
• Dope of Dopamine We used a house of distributional RL algorithms (e.g., QR, C51) to play games.
  2023reinforcement learning, distributional RL, uncertainty, decision
  
• Opponent Modelling in Dyadic Games Building games and inverse RL algorithms to study how humans infer uncertainty to guides their predictions of an opponent’s behavior and make actions.
  2023reinforcement learning, inverse-RL, uncertainty, decision, social psychology, BSc Thesis
  
• >> all projects

• Kernel Methods A walk through for kernel method in machine learning and RKHS
  2024machine learning, kernels, rkhs
  
• Probabilistic and Unsupervised Learning Self-study blog for the PGM course at CMU
  2024graphical models, bayesian machine learning, machine learning
  
• Approximate Inference Approximate inference and learning in probabilistic graphical models
  2024graphical models, bayesian networks, markov networks, mcmc
  
• Machine Learning Seminar Current research in machine learning.
  2023machine learning, seminar
  
• >> all posts

• Theoretical Neuroscience Notes for Computational and System Neuroscience
  2024theoretical neuroscience, computational biology
  
• Convex Optimization Revision notes for convex optimization
  2023optimization, machine learning
  
• Reinforcement Learning RL notes and cheats sheets over the years
  2022reinforcement learning, inverse RL, distributional RL
  
• >> all notes

photography

• presence places I lived and traveled to.
• land and sea landscapes.
• /\ "I wanted to cut up the blue of the sky."
• faces people I met, and their ideas.
• wars all over the world.
• alone together modern loneliness.
• >> instagram

contact