Satya Nandivada

I’m a Machine Learning Engineer and Computational Systems Biology researcher building scalable, high-impact intelligent systems at the intersection of deep learning, graph modeling, and scRNA-seq, with hands-on experience in PyTorch, Transformers, and GNNs. I built DeepOMAPNet, a multimodal framework that predicts surface protein expression from RNA using kNN cell graphs, Graph Attention Networks, and Transformer-based fusion, enhanced with cross-modal attention, graph positional encoding, and efficiency optimizations like sparsification and mixed precision to scale training and inference. I also develop end-to-end pipelines for AML subtype classification and build hybrid mechanistic + learning models (e.g., UDEs) to study drug response and resistance, with a focus on delivering reliable, interpretable, real-world impact.