Lesia Semenova

I am an Assistant Professor of Computer Science at Rutgers University, where I lead a research group that advances the foundations, algorithms, and applied practice for safe, trustworthy, and interpretable AI through model and representation multiplicity. My work formalizes the Rashomon Effect—the existence of many equally accurate but behaviorally different models—to move the field beyond a single-model mindset.

A key question my work addresses is how to use model multiplicity in practice. By characterizing Rashomon sets, or sets of near-optimal models, I develop methods to navigate these spaces and identify models that satisfy additional desiderata, such as interpretability or robustness. This approach leverages model diversity to enable new algorithmic tools for robust recourse, personalized alignment, and stable decision-making in high-stakes fields like healthcare and public policy. Ultimately, my research aims to transform uncertainty from a source of instability into a resource for trust. I am increasingly extending these ideas to foundation models and LLMs, where multiplicity naturally arises through internal representations and reasoning paths.

Before joining Rutgers, I was a postdoctoral researcher at Microsoft Research (NYC) and received my PhD in Computer Science from Duke University. Earlier, I worked on augmented reality at Samsung R&D Institute Ukraine and earned my MS and BS in Applied Mathematics from Taras Shevchenko National University of Kyiv.

I am currently recruiting students at Rutgers. If you're interested in collaborating or joining my group, please take a look at this page.