CV

General Information

  • Linfeng Wang
    • Current PhD student at London School of Hygiene and Tropical Medicine
    • 15th Nov 1997
    • English, Chinese, French, Japanese

Education

  • 2021 - 2025
    PhD in Computational Genomics
    London School of Hygiene and Tropical Medicine
    • Supporting Scheme - LiDo-DTP
    • Funding - BBSRC
    • Dissertation - Machine Learning-Enhanced Drug Resistance and Bioinformatics Transmission Profiling of Tuberculosis Using Genome Sequencing
  • 2019 - 2020
    MRes Bioengineering (Hons)
    Imperial College London
    • Merit
    • Modules - Computational & Statistical Methods, Frontiers in Bioengineering, Biomaterials
    • Dissertation - Design of an Artificial Bruch’s Membrane from Synthetic Polyesters
  • 2016 - 2019
    BSc Biochemistry (Hons)
    King's College London
    • First-Class Honour
    • Dissertation - Investigation of Concordance Between Molecular Dynamics Simulation and FRET Biosensor using Designed Protein Linker System

Technical Skills

  • Programming
    • Python, R, Bash, MySQL, HTML/CSS, C++
  • Data Science & ML
    • Pytorch, PyG, Fastai, scikit-learn, NumPy, Pandas, Matplotlib, Scipy, ggplot2, tidyverse, Jax
  • Genomics Tools
    • BWA-mem, samtools, bcftools, Rxml, Freebayes, Beast2, Figtree, trimmomatic, iTol, Plink2, GATK, Nextflow

Publications

  • Mixed infections in genotypic drug-resistant Mycobacterium tuberculosis. Scientific Reports (2023).
  • TB-ML - A framework for comparing ML approaches to predict drug resistance. Bioinformatics Advances (2023).
  • Whole genome sequencing of TB in the Philippines. Scientific Reports (2024).
  • TGV - Visualisation tools for transmission graphs. NAR Genomics and Bioinformatics (2024).
  • Detecting Shallow Gas from Marine Seismic Images. Turing Institute Report (2025).
  • TOAST - A novel tool for amplicon design in TB studies. BioRxiv (submitted).
  • LSTM-Based Transfer Learning Models for TB AMPs. (forthcoming).
  • Deep Learning Approaches for MIC Prediction in TB. (forthcoming).
  • Stepwise TB Outcome Prediction using XGBoost. (forthcoming).
  • GNN-based Positive Selection Detection in TB. (forthcoming).

Experience

  • 2022 - 2025
    PhD Candidate
    LSHTM (Clark Campino Phelan Lab), London
    • Machine learning models for drug resistance, treatment outcome, protein generation
    • Deep learning using CNN, RNN, GNN, Transformers
    • Designed tool for gene amplicon sequencing (Python & Nextflow)
  • 2025 - Present
    ML Consultant
    Deep Science Venture, London
    • Built CNN, RNN, VAE models for sequence tasks
    • Applied SHAP, LIME, DeepLIFT for model interpretation
  • Jan 2024
    Data Study Group Hackathon
    Alan Turing Institute
    • Built deep learning models for seismic data analysis
    • Deployed CNNs with contrastive learning for geophysical image analysis
  • Aug 2024 – Oct 2024
    PhD Placement
    Linkgevity, London
    • Built MLP and GNN models for drug-drug interaction prediction
    • Deployed scalable pipelines on GCP
  • Aug 2022 - Feb 2023
    Research Assistant
    ByteDance (VoyagerX), London
    • Built chemical databases; used autoencoders for neoantigen data
    • Led market research and scientific reporting

Teaching

  • Python coding – Master’s course at LSHTM
  • Genomics workshop instructor – Philippines, Indonesia, Thailand
  • Teaching assistant – Sysmic statistics course

Leadership & Service

  • 2024 - 2025
    Student Committee
    LiDo PhD Programme, London
    • Designed wellbeing surveys
    • Organised 3-day retreat for 300+ participants

Awards

  • 2021 - 2025
    UKRI BBSRC LiDo PhD Scholarship
  • 2018
    Wellcome Trust Biomedical Studentship
    • Fundinng for Summer internship in the Lab of Dr. Eugene Makeyev
  • 2020
    Imperial Award for Personal Development
    • Effective teamwork
    • Going above and beyond academic expectations
    • Independent open-minded thought
    • Self-awareness
    • active self-management.

Honors and Awards

  • 2020
    Imperial Award
    • Effective teamwork
    • Going above and beyond academic expectations
    • Independent open-minded thought
    • Self-awareness
    • active self-management.
  • 2018
    Wellcome Trust Biomedical Vacational Studentship

Interests

  • Judo, Scuba diving, basketball, writing, guitar, coding