Lavinia Ghita

Lavinia is a solutions architect manager at NVIDIA, leading the EMEA technical team for the Financial Services Industry (FSI). Her work focuses on large‑scale AI systems, with emphasis on distillation methods, domain-adaption techniques and model compression methods for efficient AI pipelines. Her research also covers foundation‑model architectures for time‑series analysis and the development of general methods for modeling structure and latent dynamics in temporal processes. Prior to NVIDIA, Lavinia worked on research and applications in this space at a quantitative hedge fund and in big tech. She holds advanced degrees in Mathematics from the École Polytechnique Fédérale de Lausanne (EPFL).
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Posts by Lavinia Ghita

Simulation / Modeling / Design

GPU-Accelerated Clustering for Financial Instruments at Scale

Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor... 13 MIN READ
Agentic AI / Generative AI

Synthetic Data Generation for Financial AI Research with NVIDIA NeMo

Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings... 13 MIN READ
Decorative image.
Agentic AI / Generative AI

Build Efficient Financial Data Workflows with AI Model Distillation

Large language models (LLMs) in quantitative finance are increasingly being used for alpha generation, automated report analysis, and risk prediction. Yet... 11 MIN READ
Agentic AI / Generative AI

Transforming Financial Analysis with NVIDIA NIM

In financial services, portfolio managers and research analysts diligently sift through vast amounts of data to gain a competitive edge in investments. Making... 13 MIN READ