I am Seyed-Ahmad Ahmadi (@nvahmadi), Senior Solution Architect at NVIDIA, for healthcare and life sciences accounts in the EMEA region.
I am interested in applied machine & deep learning in medicine, especially its translation into clinical environments. More generally, I am interested in using and combining various data types in the healthcare domain and beyond:
- Tabular data: mixed-type structured data in the wild (incl noisy and missing features). Preferred tools: NVIDIA's accelerated data science library RAPIDS, which offers GPU-accelerated versions of:
- Grid data: in 1D/2D/3D, mostly in the domain of medical image analysis. Preferred tool: MONAI.
- Text/language data: NLP using LLMs and RAG agents for retrieval from knowledge bases. Preferred tool: NeMo Framework
- Graph data: graph analytics and graph neural nets (GNNs)/Geometric Deep Learning. Preferred tool: PyG, via our PyG containers on NGC.
My main interest is to use deep learning for multimodal fusion of above data types, on healthcare data (multi-omics fustion), and other domains.
If you are interested in my publications, please have a look at my Google Scholar profile.
If you are interested in connecting with me, please visit my LinkedIn profile.