Abdullah Karaaslanli

I am a postdoctoral researcher at the department of Electrical and Computer Engineering at Michigan State University under the supervision of Profs. Panos Traganitis and Selin Aviyente. I work on various research problems related to graphs, graph signal processing, and graph based learning. I am currently working on graph topology inference, adversary detection in graphs and its application to crowdsourcing problem.

Before joining my current position, I was a postdoctoral researcher at University of Michigan for a short period. I was a member of Garmire Group, where I was applying graph based learning to spatial omics.

I obtained my PhD from the department of Electrical and Computer Engineering at Michigan State University under the supervision of Prof. Selin Aviyente. During my PhD, I worked on community detection and topology inference for different graph types.

news

May 2026	New paper utilizing multilayer graph signal processing for balance aware signed graph convolutional filtering presented at ICASSP 2026: Convolutional Graph Filter Design for Signed Graphs.
Mar 2026	Our paper on autoencoder based simulatenous node and edge anomaly detection presented at 2025 IEEE Asilomar Conference is now online: Simultaneous Detection of Anomalous Nodes and Edges in Graphs.
Mar 2026	New pre-print on efficient spectral embedding of dynamic graphs: Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs.
Aug 2025	New paper on optimal graph filtering for hub node detection published in IEEE TSIPN: Learning Graph Filters for Structure-Function Coupling Based Hub Node Identification.
Jul 2025	New pre-print on topology identification of signed graphs using net Laplacian as graph shift operator: Signed Graph Learning: Algorithms and Theory.
May 2025	Attended GSP Workshop 2025 to present our work: Identifying Adversarial Attacks in Crowdsourcing via Dense Subgraph Detection.
May 2025	Paper on dense subgraph detection for adversarial crowdsourcing accepted for presentation at ICASSP 2025: Identifying Adversarial Attacks in Crowdsourcing via Dense Subgraph Detection.