Welcome to my webpage! I am a machine learning researcher, statistician, and professional curious person. Currently, I am working on machine learning for biology and I am excited to be part of the team at Latent Labs, where I'm working on developing frontier models for biologics.
Previously, I had the opportunity to contribute to Altos Lab and their mission of transforming medicine through cellular rejuvenation programming, worked as a Research Scientist at Improbable, where I gained valuable experience applying my skills to real-world problems. During my time at Improbable, I also served as an Assistant Professor in Statistics at Durham University, where I enjoyed contributing to the academic community and mentoring the next generation of statisticians and machine learning practitioners. My research interests lie at the intersection of statistics, machine learning, and probability theory, with a particular focus on applications in the sciences. I'm also intrigued by the philosophical aspects of science and enjoy digging into the foundations when the opportunity arises. Recently, I've been working on projects involving generative AI, including large language models, embeddings, and diffusion models. I obtained my DPhil (the Oxford equivalent of a PhD) from the Department of Statistics at the University of Oxford. Below, you'll find a list of my publications, highlighting my contributions to the field.
News
-
The paper PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis from my time at Altos Labs has been accepted to Neurips 2025 Datasets and Benchmarks Track.
-
Our team at Latent Labs as published a preprint for our new all-atom protein design model Latent-X, where we demonstrate lab-validated state-of-the-art performance for the de-novo design of cyclic peptides and minibinders! Also check out the platform.
-
I am excited to join Latent Labs to work on the next generation of biologics frontier models!
-
Our team at Altos Labs will be presenting our paper PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis as a spotlight presentation at the Neurips workshop on AI for New Drug Modalities. If you're planning to attend, feel free to reach out — we'd love to connect!
-
Together with Bastian Grossenbacker Rieck and Juius von Rohrscheidt we investigate what happens when Bayesian Computation Meets Topology. Now accepted at TMLR!
Selected publications
-
Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2
-
Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design
-
Approximate Bayesian Computation with Path Signatures
-
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
-
Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise
-
Denoising diffusion probabilistic models on so (3) for rotational alignment
-
Learning Multimodal VAEs through Mutual Supervision
-
Robust neural posterior estimation and statistical model criticism
-
Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics
-
Amortised likelihood-free inference for expensive time-series simulators with signatured ratio estimation
-
Capturing Label Characteristics in VAEs
-
Large-sample asymptotics of the pseudo-marginal method