Paulius Rauba

Founding Research Scientist at Cursive

Founding member of the technical staff. Large-scale reinforcement learning and methods for highly efficient (zero-latency) model deployment.

Research Scientist, Deep Learning at Salient

AI4Science generative spatio-temporal forecasting. Led successive generations of algorithms (Diffusion → Flow Matching → Functional Generative Networks); 24× inference speedup at no quality cost.

Lecturer at ISM University of Management and Economics

Designed and taught four courses across the ML curriculum: Deep Learning, Elements of AI, Data Mining for Business Intelligence, and Econometrics.

Expert (Contract) at European Commission

Selected European Commission projects in deeptech, deep learning, and predictive analytics.

Founder & CEO at Gauss

Bootstrapped an MLOps consultancy delivering predictive systems and machine learning infrastructure for small and mid-sized companies.

Data Scientist at Luminor Group

Owned end-to-end data science workstreams in retail banking: business problem → model design → production deployment.

Lecturer at Vilnius CODING School

Taught Python for data science, regression, classification, and clustering to small cohorts of ∼14.

Co-Founder at Nereus.AI

Co-founded an AI-powered education platform for higher-ed institutions after winning the Microsoft AI Guardians Competition.

Data Analyst at Exacaster

Big-data tooling (Spark, Hadoop, Airflow, SQL) for telco analytics. Full-stack delivery from analysis to automation.

Analyst at CIVITTA

Private-sector management consulting: feasibility analysis, business case work, and corporate restructuring effects.

Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models

Rauba P., van der Schaar M.
· ICLR 2026

Multi-Agent Systems Should be Treated as Principal-Agent Problems

Rauba P., Cepenas S., van der Schaar M.
· arXiv:2601.23211

[arxiv]

No More, No Less: Least-Privilege Language Models

Rauba P., Seputis D., Vanagas P., van der Schaar M.
· arXiv:2601.23157

[arxiv]

Probabilistic Transformers for Joint Modeling of Global Weather Dynamics and Decision-Centric Variables

Rauba P., Cikojevic V., Bartolic F., Levang S., Dickinson T., Dwelle C.
· Salient research model report

[arxiv]

Tiny Autoregressive Recursive Models

Rauba P., Fanconi C., van der Schaar M.
· ICLR 2026 Workshop on AI with Recursive Self-Improvement

Model Organisms for Generalization Resistance Under Distribution Shift

Barzdukas J., Peck J., Schulz J., Rauba P., Wells L.
· ICLR 2026 Workshop on Principled Design for Trustworthy AI

Statistical Hypothesis Testing for Auditing Robustness in Language Models

Rauba P., Wei Q., van der Schaar M.
· ICML 2025

Visualizing Token Importance for Black-Box Language Models

Rauba P., Wei Q., van der Schaar M.
· AISTATS 2025

Redefining Digital Health Interfaces with Large Language Models

Imrie F., Rauba P., van der Schaar M.
· Frontiers in Artificial Intelligence

Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models

Rauba P., Seedat N., Luyten M.R., van der Schaar M.
· NeurIPS 2024

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments

Rauba P., Seedat N., Kacprzyk K., van der Schaar M.
· NeurIPS 2024

Quantifying Perturbation Impacts for Large Language Models

Rauba P., Wei Q., van der Schaar M.
· Statistical Foundations of LLMs Workshop @ NeurIPS 2024

[arxiv]

Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset

Kirk H.R., Jun Y., Rauba P., et al.
· WOAH @ ACL 2021

[paper]