22 citations · 22 across the 6 of their papers we have counts for
7 papers
SeqMaestro: From nucleotide sequences to biological hypotheses through interpretable machine learning
Evgeny S. Saveliev, Krzysztof Kacprzyk, Charlotte Capitanchik +7
Nucleotide sequence analysis is central to problems spanning regulatory genomics, evolutionary biology, and phenotype prediction. Classical bioinformatics methods extract interpret…
Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
Evgeny S. Saveliev, Samuel Holt, Nabeel Seedat +3
Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by inefficient search strategies a…
Knowledge-Informed Kernel State Reconstruction from Heterogeneous Partial Observations
Luca Muscarnera, Silas Ruhrberg Estévez, Samuel Holt +2
Real-world scientific systems are rarely observed through complete, regularly sampled state trajectories. Instead, measurements are often partial, noisy, and heterogeneous, providi…
Towards Human-Guided, Data-Centric LLM Co-Pilots
Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat +2
Machine learning (ML) has the potential to revolutionize various domains, but its adoption is often hindered by the disconnect between the needs of domain experts and translating t…
CliMB: An AI-enabled Partner for Clinical Predictive Modeling
Evgeny Saveliev, Tim Schubert, Thomas Pouplin +2
Despite its significant promise and continuous technical advances, real-world applications of artificial intelligence (AI) remain limited. We attribute this to the "domain expert-A…
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine
Evgeny S. Saveliev, Mihaela van der Schaar
TemporAI is an open source Python software library for machine learning (ML) tasks involving data with a time component, focused on medicine and healthcare use cases. It supports d…
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev +1
Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the…