Shira Karmi Electrical and Computer Engineering Prof. Tami Riklin Reviv Abu Hussein, Tariq1, Abdulhalim Ibrahim1 Anastasia Ivanovski Supervisors - Prof. Ofer Hadar

Shira Karmi Electrical and Computer Engineering Prof. Tami Riklin Reviv seminar topic: Decoding Functional Networks for Visual Categories via GNN Abstract: Understanding how large-scale brain networks represent visual categories is fundamental to linking perception and cortical organization. Using high-resolution 7T fMRI from the Natural Scenes Dataset, we construct parcel-level functional graphs and train a signed Graph Neural Network that models both positive and negative interactions, with a sparse edge mask and class-specific saliency. The model accurately decodes category-specific functional connectivity states sports, food,vehicles) and reveals reproducible, biologically meaningful subnetworks along the ventral and dorsal visual pathways. This framework bridges machine learning and neuroscience by extending voxel-level category selectivity to a connectivity-based representation of visual processing. Abu Hussein, Tariq1, Abdulhalim Ibrahim1 Optically addressed spatial light modulators (OASLMs) are fundamental components in the development of parallel optical computers, serving critical roles in various applications, including optical correlators, high-speed modulation, compatibility with coherent light sources, image color conversion, image polarization conversion, image amplification, 3D displays, digital holography, and projection displays[1]. The structure of OASLM enables high resolution without the need for complicated fabrication processes because it is a single-pixel device. Our group has recently developed an IR-to-visible image conversion device using an InGaAs photosensor and a nematic LC layer[2]. Full name - Anastasia Ivanovski Supervisors - Prof. Ofer Hadar, school of Electrical and Computer Engineering, Ben Gurion University; Dr. Alaa Jamal, Institute of Agricultural and Biosystems Engineering, Volcani Institute Degree - M.Sc Electrical engineering student Seminar topic - Hybrid Physics-Informed Machine Learning for Real-Time Water Quality Prediction in Aquaculture Ponds Seminar abstract -  Effective aquaculture management relies on accurate forecasting of water temperature and dissolved oxygen, yet traditional models often force a trade-off between physical interpretability and data-driven flexibility. While mechanistic models struggle with environmental variability, purely data-driven approaches lack physical consistency and robustness to sensor noise. In this talk, I will present a hybrid framework that addresses these limitations using Physics-Informed Neural Networks (PINNs) and Symbolic Regression (PySR/SINDy). By embedding thermodynamic laws and diffusion equations directly into the model, we ensure predictive accuracy even in poorly instrumented settings. This approach results in a lightweight, interpretable decision-support tool that provides near real-time early-warning indicators for proactive pond management.