CV

General Information

  • Linfeng Wang
    • Machine Learning Engineer at Biographica, London
    • PhD in Machine Learning and Computational Genomics, London School of Hygiene and Tropical Medicine
    • 15th Nov 1997
    • English, Chinese, French, Japanese

Experience

  • Apr 2026 - Present
    Machine Learning Engineer
    Biographica, London
    • Develop, test and benchmark deep learning models predicting gene expression from DNA sequence and biological context
    • Build scalable PyTorch training and fine-tuning pipelines on Azure and AWS, covering dataset construction, evaluation, hyperparameter optimisation and reproducible experimentation
    • Apply ML to gene-editing and crop-trait prediction, combining multi-omics data
  • Oct 2021 - Mar 2026
    PhD Researcher
    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)
  • Mar 2025 - Oct 2025
    ML Consultant
    Deep Science Venture, London
    • Built CNN, RNN, VAE models for sequence tasks
    • Applied SHAP, LIME, DeepLIFT for model interpretation
  • Aug 2024 - Oct 2024
    PhD Placement
    LinkGevity, London
    • Built MLP and GNN models for drug-drug interaction prediction
    • Deployed scalable pipelines on GCP
  • 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 2022 - Feb 2023
    Research Assistant
    ByteDance (VoyagerX), London
    • Built chemical databases; used autoencoders for neoantigen data
    • Led market research and scientific reporting

Technical Skills

  • Programming
    • Python, R, Bash, MySQL, HTML/CSS, C++
  • Data Science & ML
    • PyTorch, PyG, scikit-learn, Transformers, LLMs, CNNs, RNNs, GNNs, VAEs, XGBoost
    • NumPy, Pandas, Scipy, Jax, Fastai, Numba
  • MLOps & Cloud
    • AWS, GCP, Azure, Docker, Git, Nextflow, Jupyter, Huggingface, LangChain
  • Visualization
    • Matplotlib, Plotly, Seaborn, ggplot2, Streamlit, Flask
  • Genomics Tools
    • BWA-MEM, SAMtools, BCFtools, RAxML, FreeBayes, BEAST2, FigTree, Trimmomatic, iTOL, PLINK2, GATK

Education

  • 2021 - 2026
    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

Publications

  • Decoding positive selection in M. tuberculosis with phylogeny-guided graph attention models. BMC Bioinformatics (2026).
  • TOAST - A novel tool for designing targeted gene amplicons in TB genomic studies. BMC Genomics 26(1) (2025).
  • LSTM-based deep learning model for the discovery of antimicrobial peptides targeting M. tuberculosis. Bioinformatics Advances (2025).
  • A multi-stage machine learning framework for stepwise prediction of TB treatment outcomes. Research Square (preprint, 2025).
  • Detecting Shallow Gas from Marine Seismic Images. Turing Institute Data Study Group Report (2025).
  • Whole genome sequencing of TB in the Philippines. Scientific Reports 14:70471 (2024).
  • TGV - Visualisation tools for transmission graphs. NAR Genomics and Bioinformatics 6(4) (2024).
  • Mixed infections in genotypic drug-resistant Mycobacterium tuberculosis. Scientific Reports 13:1-8 (2023).
  • TB-ML - A framework for comparing ML approaches to predict drug resistance. Bioinformatics Advances (2023).
  • Genomic analysis of tuberculosis in Thailand. Research Square (submitted, 2025).
  • Deep Learning Approaches for MIC Prediction in TB. (forthcoming).

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 - 2026
    UKRI BBSRC LiDo PhD Scholarship
  • 2020
    Imperial Award for Personal Development
    • Effective teamwork
    • Going above and beyond academic expectations
    • Independent open-minded thought
    • Self-awareness
    • Active self-management
  • 2018
    Wellcome Trust Biomedical Studentship
    • Funding for summer internship in the Lab of Dr. Eugene Makeyev

Interests

  • Judo, bouldering, scuba diving, basketball, writing, guitar, coding