Ehsan Estaji

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I am a Postdoctoral Researcher at Umeå University (UPSC).

My current research sits at the intersection of machine learning and plant genomics, with a focus on long non-coding RNAs (lncRNAs) and graph-based AI. I design models that learn from complex biological data— sequence models, self-supervised learning, and graph neural networks (GNNs)—to discover, annotate, and functionally characterize lncRNAs across plant species. I also build end-to-end pipelines that connect raw RNA-seq signals to interpretable network representations and biological hypotheses.

Applications include comparative genomics, functional prediction, regulatory network inference, and AI-assisted discovery in plant biology.

Academic Journey

Reverse-chronological: graph-based AI in plant genomics ← applied GNNs & network science ← security & ML ← applied graph theory.

Postdoctoral Researcher — Umeå Plant Science Centre (UPSC) 2025–present

Machine learning for plant genomics with a focus on long non-coding RNAs (lncRNAs) and graph-based AI. Building end-to-end pipelines from RNA-seq signals to interpretable graph models and biological hypotheses.

lncRNAGNNsSelf-supervisedRNA-seqGraphRAG

Postdoctoral Researcher — SnT, University of Luxembourg 2023–2024

Practical graph neural networks and network science; applications to transportation systems, epidemiology, and biological networks. Developed graph-centric analytics and prototypes bridging data to decisions.

Applied GNNsNetwork ScienceTransportationEpidemiologyBio networks

Ph.D. in Computer Science 2019–2023

Information security and machine learning. Learning-based methods for secure, data-driven systems and anomaly detection, linking statistical learning with practical security challenges.

Information SecurityMachine LearningAnomaly Detection

Assistant Professor of Mathematics 2012–2019

Teaching and research in applied mathematics and graph theory; supervised theses, developed curricula, and published on combinatorial and spectral aspects of complex networks.

Applied MathGraph TheoryCombinatoricsSpectral Methods

Ph.D. in Mathematics 2008–2012

Applied graph theory: structure, optimization, and spectral viewpoints for complex networks—forming the theoretical backbone for later graph-based AI and data-science work.

Applied Graph TheoryOptimizationSpectral Graphs

Profiles & Social

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