CV

Academic CV and selected research experience.

Contact Information

Name Fengzhe Zhang
Professional Title Incoming PhD Student
Email fz287@cam.ac.uk
Phone +44 7421 727 978

Professional Summary

Incoming PhD student at the Gatsby Computational Neuroscience Unit working on generative modelling, variational inference, diffusion-based samplers, and probabilistic machine learning.

Experience

  • 2024 - 2025

    Cambridge, UK

    Research Assistant
    University of Cambridge
    Supervisor: Prof. Jose Miguel Hernandez-Lobato
    • Designed diffusion-based generative samplers for molecular energy functions; early experiments halved sampling time versus baseline DDPM.
  • 2024 - 2024

    Cambridge, UK

    MPhil Research Project: Efficient and Unbiased Sampling of Boltzmann Distributions
    University of Cambridge
    • Reduced number of function evaluations from 100 to 6-25 while preserving effective sample size on synthetic and equivariant n-body systems by integrating Consistency Models with importance sampling.
  • 2023 - 2023

    London, UK

    Undergraduate Research Assistant
    Imperial College London
    Constrained Optimisation in Variational Autoencoders
    • Eliminated hyper-parameter sweeps in beta-VAE by introducing the Constrain-KL algorithm, which enforces an exact KL target and matched NVAE accuracy on CIFAR-10.
  • 2022 - 2022

    London, UK

    Group Project
    Imperial College London
    Deflation Techniques for Non-linear PDEs
    • Implemented sparse Jacobian-Newton deflation in Julia, achieving a 3x speedup and uncovering five distinct solutions to a challenging 3-D nonlinear PDE.

Education

  • 2023 - 2024

    Cambridge, UK

    MPhil
    University of Cambridge
    Machine Learning and Machine Intelligence
    • Probabilistic ML
    • Deep Learning
    • Computational Statistics
    • Speech Recognition
    • Advanced ML
    • Reinforcement Learning
  • 2020 - 2023

    London, UK

    BSc
    Imperial College London
    Mathematics
    • Applied Probability
    • Optimisation
    • Stochastic Simulation
    • Computational Linear Algebra
    • Intro to Statistical Learning
    • Statistical Modelling
    • Methods for Data Science

Teaching

Awards

  • 2023
    Dean's List
    Imperial College London

    Top 10% of Mathematics undergraduates in 2021, 2022, and 2023.

  • 2022
    Undergraduate Research Bursary
    Department of Mathematics, Imperial College London
  • 2022
    Winton Prize
    Imperial College London

    Awarded for outstanding second-year group project.

Publications

Skills

Programming: Python, PyTorch, JAX, Julia, C/C++
Research Methods: Generative modelling, Variational inference, Diffusion models, Probabilistic machine learning, Stochastic simulation
Tools: Git, Linux, LaTeX

Projects

  • Efficient and Unbiased Sampling of Boltzmann Distributions

    Integrated consistency models with importance sampling to reduce function evaluations from 100 to 6-25 while preserving effective sample size.

  • Constrain-KL for beta-VAE

    Developed a constrained optimisation approach to set exact KL targets and avoid beta sweeps in variational autoencoders.