Ioannis Ch. Paschalidis

Distributionally robust optimization for credible learning in predictive health applications

Data-driven predictive models are increasingly used in clinical settings, but adoption hinges on both interpretability and credibility —i.e., alignment with domain knowledge. For tabular electronic health records data, conventional deep models are hard to interpret, while sparse...

Preferential CDR masking in paired antibody language models improves binding affinity prediction

Therapeutic antibodies are a leading class of biologics, yet their unique architecture poses challenges for computational modeling. Each antibody comprises paired heavy and light variable domains with conserved framework regions that maintain structure and hypervariable complemen...