Nasim Yahyasoltani
Northwestern Mutual Assistant Professor
Department of Computer Science
Marquette University, Milwaukee
Office: 240C, Cudahy Hall
Tel: 414-288-3790
Email: nasim(at)yahyasoltani(at)
marquette(dot)edu
About Me
I am currently Northwestern Mutual Assistant Professor of Computer Science at Marquette University, Milwaukee, WI. I obtained my Ph.D. in Electrical Engineering from the University of Minnesota, Minneapolis, MN, in June 2014 under the supervision of Professor Georgios B. Giannakis. Prior to joining Marquette University, I was a Senior Data Scientist at Harley-Davidson Motor Company.
Research Interest
- Statistical signal processing
- Machine learning
- Optimization theory
- Network science
- Application domains include wireless communications and networking, smart grid and healthcare.
Research Lab
I am the director of the Machine Learning, Optimization and Data (MOD) Lab. For more details see Group.
News
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Paper Acceptance in ICASSP 2026
Our conference paper titled "MERI-VL-MIL: Multi-Expert Reasoning-Informed Vision-Language Multi-Instance Learning for Few-Shot Cancer Subtyping" was accepted to ICASSP 2026.
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Journal Acceptance in IEEE Transactions on Artificial Intelligence
Our journal titled "Uncertainty Estimation for Graph-based Learning in Digital Pathology" got accepted to IEEE Transactions on Artificial Intelligence.
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Journal Acceptance in IEEE Transactions on Artificial Intelligence
Our journal titled "Explainable and Position-Aware Learning in Digital Pathology" got accepted to IEEE Transactions on Artificial Intelligence.
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Congratulations to Dr. Milan Aryal on His PhD Graduation
We are proud to announce that Milan Aryal has completed his PhD and graduated from the lab after successfully defending his dissertation titled “Graph-Based Learning and Generative Models in Digital Pathology".
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Journal Acceptance in Knowledge-Based Systems
Our Journal titled "Explainability-based graph augmentation for out-of-distribution robustness in digital pathology" got accepted to Knowledge-Based Systems
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Journal Acceptance in Knowledge-Based Systems
Our Journal titled "Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation" got accepted to Knowledge-Based Systems
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Paper Acceptance in ICASSP 2025
Our conference paper titled "Uncertainty Estimation for Out-of-Distribution Detection of Whole Slide Images" was accepted to ICASSP 2025.
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Journal Acceptance in IEEE Transactions on Power Systems
Our journal titled "A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid" got accepted to IEEE Transactions on Power Systems
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Paper Acceptance in IEEE GLOBECOM
Our conference paper titled "A Graph Motif Adversarial Attack for Fault Detection in Power Distribution Systems" got accepted to the 2024 IEEE Global Communications Conference
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Journal Acceptance in IEEE Transactions on Artificial Intelligence
Our journal titled "Context-Aware Self-Supervised Learning of Whole Slide Images" got accepted to IEEE Transactions on Artificial Intelligence.
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Paper Acceptance in ICMLA
Our conference paper titled "Robust and transferable graph neural networks for medical images" got accepted to the International Conference on Machine Learning and Applications (ICMLA 2023).
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Paper Acceptance in MLSP
Our conference paper titled "Graph-based Multi-Task Learning for Fault Detection in Smart Grid" was accepted to the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2023).
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Paper acceptance to ICASSP
Our conference paper titled "Position-Aware Graph-Based Learning of Whole Slide Images" got accepted to the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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Paper Acceptance in ICASSP
Our conference paper titled "Context-aware Graph-Based Self-Supervised Learning of Whole Slide Images" got accepted to the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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Paper Acceptance in ICMLA
Our conference paper titled "Identifying Catheter and Line Position in Chest X-Rays Using GANs" got accepted to IEEE Intl. Conf. on Machine Learning and Applications.