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title Senior Machine Learning Scientist, generative and agentic models for biology
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Senior Machine Learning Scientist, generative and agentic models for biology

London, UK • linkedin.com/in/ctr26 • github.com/ctr26 • Google Scholar
Generative and agentic models for biology • LLM post-training • out-of-distribution modelling • virtual cells

Summary

I build generative models that generalise to unseen biology, aiming to build the first virtual cell. I apply machine learning to biological data, from light-sheet microscopy and terabyte-scale image archives to single-cell transcriptomics and knowledge graphs. I have published in Nature Biotechnology, NeurIPS, and Nature Methods. I write the research code and the infrastructure under it, from tools used by other labs to agents grounded in real biological databases, and training and inference on cloud and HPC.

Experience

Senior Machine Learning Scientist, Valence Labs @ Recursion Pharmaceuticals
London, UK • Oct 2024 – present

  • Build agents that use real biological databases as tools. They decode within Ensembl, HGNC, and ChEMBL vocabularies, check tool calls against structured resources, and use retrieval and verifier ensembles for grounding. Their gene–gene hypotheses are explainable and draw on several sources.
  • Co-authored TxPert (Nature Biotechnology, 2026), a state-of-the-art transcriptomic perturbation predictor conditioned on multiple biological knowledge graphs. Owned benchmark task design, data curation, and the out-of-distribution ablations.
  • Post-train multimodal LLMs over knowledge graphs, literature, and omics; use reinforcement learning to align agent behaviour with verifiable biological objectives.
  • Train large diffusion transformers to generate perturbational biology that generalises to unseen perturbations, cell types, and combinations, rather than interpolating between ones it has.
  • Model single-cell and bulk RNA-seq jointly with high-content phenotypic imaging; each modality pins down what the other leaves open.
  • Design active learning strategies to choose the next experiment under a fixed budget, linking model uncertainty to experiments the wet lab can run.
  • Lead the engineering on a large scientific project. Set technical direction and engineering standards across ML and biology; scale training and inference across GPUs in the cloud; promote code review, reproducibility, and shared tooling to carry research prototypes into production.
  • Work on evaluation strategy and error analysis for proteome-scale binding-affinity screening.
  • Organise the Virtual Cell Journal Club, bringing ML and wet-lab teams together.

Senior Research Associate and AI Engineering Lead, EMBL-EBI (Uhlmann Group and Bio-Image Archive)
Cambridge, UK • Dec 2022 – Oct 2024

  • Supervised 6 PhD students; set the lab's coding standards, CI, and peer review.
  • Created bioimage_embed and co-authored ShapeEmbed (NeurIPS 2025), which learn contour and morphology representations without labels. Both ship as production Python used by other labs.
  • Designed scalable, containerised pipelines with automated experiment tracking to process terabytes of microscopy across HPC and cloud.

Part-time and consulting roles held alongside EMBL-EBI, 2022 – 2024

AI/ML Founding Engineer (part-time), Amun AI AB
Stockholm, Sweden • 2022 – 2024

  • Built a Kubernetes (GKE) model-serving platform on NVIDIA Triton and KServe, with auth, monitoring, and autoscaling. Served 100+ models to 30+ daily users.

AI/ML Engineering Consultant (part-time), DeepMirror
Cambridge and London, UK • 2022 – 2024

  • Shipped MouseMindMapper, a brain-histology segmentation product bringing in £50k of revenue a year. Owned it end to end, from data and training to packaging and docs.
  • Wrote a high-performance C++ cheminformatics fingerprinting library for production.

Data Scientist, Brazma Group, EMBL-EBI
Cambridge, UK • Dec 2019 – Dec 2023

  • Co-authored the successful €5M AI4LIFE grant for federated bioimage AI infrastructure; contributed to its platform architecture and model-sharing strategy.
  • Drove large-scale AI microscopy analyses in the Image Data Resource; worked with Google Cloud on representation learning.
  • Taught an annual deep learning course to 40+ researchers, from PhD students to PIs.

Software Engineer (COVID-19 response), European Nucleotide Archive, EMBL-EBI
Cambridge, UK • Mar 2020 – Sept 2020

  • Built CI/CD for the COVID-19 Data Portal, so it published global data daily. Scaled NGS alignment and ETL in Nextflow and Kubernetes as volumes surged.

Computational Microscopist, National Physical Laboratory
London, UK • 2018 – Dec 2019

  • Developed new 3D organoid segmentation methods for cancer research; consulted for MSquared on advanced imaging.

Education

PhD, Engineering, University of Cambridge • 2014 – 2018 (EPSRC PES-CDT)
Thesis on light-sheet microscopy for tracking particles in large specimens

  • Designed and built a new light-sheet microscope, and wrote the code that ran it. Automated acquisition, bead tracking for homographic alignment of the optics, and signal optimisation driven by the tracker.
  • Wrote flOPT, a micrometre-scale tomographic reconstruction that recovers sample pose from tracked fiducial beads and back-projects each ray, so it stays robust to mechanical jitter and drift where the Radon transform breaks down. Published in Scientific Reports, open-source Python with OpenCV.
  • Supervised two MRes students and one BSc student.

MRes, Photonics, University of Cambridge and UCL • 2013 – 2014 • structured illumination microscopy reconstruction
MSci, Physics (First-Class Honours), Nottingham Trent University • 2009 – 2013 • top physics graduate

Selected Publications

  1. TxPert. Out-of-distribution transcriptomic perturbation prediction over biological knowledge graphs. Nature Biotechnology (2026), co-author. doi:10.1038/s41587-026-03113-4
  2. ShapeEmbed. Self-supervised learning of 2D contour representations. NeurIPS (2025), second author. poster
  3. MIFA. Metadata and accessibility standards for reusable AI training datasets in bioimaging. Nature Methods (2025), co-author. doi:10.1038/s41592-025-02835-8
  4. DL4MicEverywhere. Reproducible, containerised deep learning for microscopy. Nature Methods (2024), co-author. doi:10.1038/s41592-024-02295-6
  5. CIR4MICS. Synthetic ground truth for benchmarking image-analysis methods. Bioinformatics (2023), co-author. doi:10.1093/bioinformatics/btad587
  6. The COVID-19 Data Portal. Rapid open data sharing for SARS-CoV-2 research. Nucleic Acids Research 49(W1) (2021), co-author. doi:10.1093/nar/gkab417
  7. mmSIM. Open toolbox for structured illumination microscopy. Phil. Trans. R. Soc. A (2021), first author. doi:10.1098/rsta.2020.0353
  8. Frame-localisation OPT (flOPT). Tomographic reconstruction from tracked fiducial beads. Scientific Reports (2021), first author. doi:10.1038/s41598-021-83454-z

The full list is on Google Scholar.

Patents

  • Virtual Cell Foundation Model, patent pending, 2025 (Recursion)
  • TxPert, transcriptomic perturbation prediction, patent pending, 2025 (Recursion)

Skills

ML and AI. Diffusion transformers, generative modelling, fine-tuning and post-training foundation models, reinforcement learning, agents and tool use, constrained decoding and retrieval grounding, active learning, contrastive and self-supervised learning, OOD and uncertainty, evaluation and benchmark design
Frameworks. PyTorch, Lightning, Hugging Face, Pyro, TensorFlow, scikit-learn
Biology and data. Single-cell and bulk RNA-seq, high-content and phenotypic imaging, histopathology, GNNs, knowledge graphs; UniProt, PDB, Ensembl, HGNC, NCBI, ChEMBL
Languages. Python (primary), R, C++, Rust, MATLAB, Java
Compute. Multi-GPU (A100, V100), CUDA, distributed training, SLURM, HPC, GCP, AWS
MLOps and infrastructure. Kubernetes, Docker, NVIDIA Triton, KServe, MLflow, CI/CD, Terraform; Nextflow, Snakemake, Airflow

Grants, Teaching, and Service

  • Grants. AI4LIFE (2022, €5M EU Horizon, co-author) • EPSRC CDT Studentship (2013 – 2018, £120k) • Nuffield Research Bursary (2012)
  • Teaching. Led the Deep Learning for Bioimage Analysis course (2019–2023), 40+ participants a year
  • Supervision. 6 PhD students in AI and spatial biology, and 3 project students
  • Peer review. Nature Methods, Scientific Reports, Journal of Microscopy, ISBI (2022, 2023), ICASSP (2024)
  • Talks. FOM (2018, 2022, 2023), MMC (2018, 2022), CBIAS (2023)