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Stephen Ra

6 accepted papers

2026

TwinWeaver: An LLM-Based Foundation Model Framework for Pan-Cancer Digital Twins

Nikita Makarov, Maria Bordukova, Lena von Voithenberg, Estrella Pivel-Villanueva, Sabrina Mielke, Jonathan Wickes +8
ICML 2026poster

Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce TwinWeaver, an open-source framework that serializes longitudinal patient histories into text, enabling unified event prediction …

Cited by 0Source ↗Scholar ↗
2024

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks

Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho
ICML 2024poster

Many scientific and industrial applications require the joint optimization of multiple, potentially competing objectives. Multi-objective Bayesian optimization (MOBO) is a sample-efficient framework for identifying Pareto-optimal solutions. At the heart of MOBO is the acquisition function, which det…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Concept Bottleneck Generative Models

Aya Abdelsalam Ismail, Julius Adebayo, Hector Corrada Bravo, Stephen Ra, Kyunghyun Cho
ICLR 2024poster

We introduce a generative model with an intrinsically interpretable layer---a concept bottleneck layer---that constrains the model to encode human-understandable concepts. The concept bottleneck layer partitions the generative model into three parts: the pre-concept bottleneck portion, the CB layer,…

Cited by 48Source ↗PDF ↗Scholar ↗
2024

Protein Discovery with Discrete Walk-Jump Sampling

Nathan C. Frey, Dan Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel +7
ICLR 2024oral

We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising. Our $\textit{Discre…

Cited by 42Source ↗PDF ↗code ↗Scholar ↗
2023

3D molecule generation by denoising voxel grids

Pedro O. Pinheiro, Joshua Rackers, joseph Kleinhenz, Michael Maser, Omar Mahmood, Andrew Martin Watkins +3
NeurIPS 2023poster

We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from a smooth distribution of noisy molecules to the distribution of real molecules. Then, we follow the _neural empirical Ba…

Cited by 20Source ↗PDF ↗code ↗Scholar ↗
2023

OpenProteinSet: Training data for structural biology at scale

Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Lukas Jarosch, Dan Berenberg, Ian Fisk +4
NeurIPS 2023poster

Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades. Recent breakthroughs like AlphaFold2 that use transformers to attend directly over large qu…

Cited by 19Source ↗PDF ↗code ↗Scholar ↗