Making unlearning stick in genomic foundation models

This project will investigate whether knowledge distillation can make targeted unlearning more robust in genomic foundation models. Genomic models such as Evo are increasingly released with open weights, creating important dual-use concerns because conventional safeguards, such as refusal training or output filtering, can be removed through fine-tuning. Existing unlearning methods are cheaper than retraining a model from scratch, but they often suppress a capability rather than fully remove it, allowing the capability to re-emerge after exposure to a small amount of relevant data.

The project will test whether UNDO, a recently proposed method that combines unlearning, weight perturbation, and knowledge distillation, can prevent this recovery in biological sequence models. UNDO has shown promising results in text-based language models but has not yet been evaluated in genomic models, which differ substantially in architecture, training data, and the relationship between prediction loss and biological capability.

The mentee will begin with our existing targeted-unlearning pipeline for Evo, avoiding the need to build the underlying infrastructure from scratch. They will create distilled student models using benign genomic data, subject these models to adversarial fine-tuning on the removed domain, and evaluate whether the targeted biological capability returns. The project will also explore the trade-off between computational cost, retained model performance, and resistance to capability recovery. Depending on progress, the work may examine which components of Evo’s StripedHyena architecture are most important for storing or recovering genomic capabilities.

The primary goal is to provide a clear empirical answer to whether distillation-based unlearning transfers successfully from text models to genomic foundation models. Both positive and negative findings would be valuable for the safe development and open release of biological AI systems.

The project will be co-supervised by me and my PhD student who is working with me in biosafety.

Relevant references include Lee et al., “Distillation Robustifies Unlearning” (arXiv:2506.06278), Cloud et al., “Gradient Routing” (arXiv:2410.04332), and the Evo genomic foundation model developed by the Arc Institute.

This project will investigate whether knowledge distillation can make targeted unlearning more robust in genomic foundation models. Models such as Evo are increasingly capable of learning complex biological sequence patterns and are often released with open weights. This creates an important safety challenge: conventional safeguards, including refusal training and output filtering, do not remove information from a model’s parameters and can often be bypassed or reversed through fine-tuning. Existing unlearning methods are more practical than retraining a model from scratch, but they may only suppress a capability, allowing it to re-emerge after the model is exposed to a small amount of relevant data.

Mentees will take end-to-end driving of the project, including reviewing the relevant literature, refining the experimental design, implementing the noise-and-distillation pipeline, running multi-GPU training experiments, performing adversarial fine-tuning, developing and applying biological capability evaluations, analysing compute, robustness trade-offs, interpreting the results, and preparing the final report or manuscript. The mentor will provide strategic guidance, access to the existing Evo unlearning infrastructure, and regular feedback, but the mentee will lead the day-to-day research and execution.

High proficiency in Python and PyTorch, including the ability to independently debug model-training code, data pipelines, and GPU memory or performance issues. Hands-on experience training or fine-tuning a transformer or other large sequence model. Following an established implementation is acceptable, but applicants should understand training loops, loss functions, checkpointing, evaluation, and hyperparameter selection. Familiarity with modern deep-learning concepts, including knowledge distillation, representation learning, transfer learning, and model evaluation. Prior experience with machine unlearning is helpful but not required. Experience using Linux, Git, and remote computing environments, preferably including multi-GPU training, SLURM, cloud computing, or an institutional high-performance computing cluster. Ability to design and execute experiments independently, including selecting appropriate controls, tracking experiments, analysing results statistically, and distinguishing genuine capability removal from temporary performance suppression. Sufficient mathematical and machine-learning background to read recent technical papers and reproduce methods from them. Relevant coursework or equivalent experience may include machine learning, deep learning, natural language processing, computational biology, statistics, or optimization. Strong research communication skills, including maintaining clear documentation and presenting results in written reports, figures, and research meetings.

No local preference