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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.

Name: Yuval Meir Studies: Electrical and Computer Engineering with Thesis Under the supervision of : Prof. Shmuel Ben-Yaakov Seminar Title: Second harmonic generation in nonlinear capacitors and application in electric field sensor design Abstract: This seminar presents a study on the design of a novel electric field sensor based on the Second Harmonic Generation (SHG) phenomenon in Non-Linear Capacitors (NLC). The research focuses on developing a comprehensive model to describe the generation of the second harmonic signal under large-signal excitation, explicitly characterizing the correlation between the capacitor's non-linearity profile and the resulting harmonic magnitude. Bridging theoretical analysis with high-fidelity SPICE simulations, the study validates these findings through laboratory experiments and culminates in the design and implementation of a practical proof-of-concept electric field tester, demonstrating the feasibility of this sensing approach. Guy Perets MSc. In Communication Systems Engineering Mentors: Prof. Itshak Lapidot (Afeka), Dr. Yehuda Ben-Shimol (BGU) Subject: Waveform-Based Spoof-Robust Automatic Speaker Verification Using Deep Learning Seminar Summary: Modern speaker verification systems are increasingly targeted by spoofing attacks such as replay and synthetic speech. A waveform-based anti-spoofing system distinguishes genuine speech from manipulated audio by learning authenticity cues directly from the raw signal. It models the characteristic patterns of natural speech production and real recording chains, and flags samples that deviate in ways consistent with spoofing while aiming to stay reliable across diverse conditions and attack styles. Tuvia Hausdorff M.sc in Electrical and Computers Engineering Supervised by Dr. Dan Vilenchik Title: Robust Voice-Based Depression Detection in Real-World Settings using Voice Conversion GenAI Summary: My research explores how speech-based machine learning systems can robustly detect depression in real-world clinical settings, where data is limited, noisy, and demographically biased. I focus on using self-supervised speech representations and generative audio models to reduce dependence on labeled data and improve generalization across speakers and recording conditions. In particular, I study label-free and weakly supervised adaptation techniques, including voice conversion–based data augmentation and test-time adaptation, while keeping the pipeline aligned with clinical constraints such as sensitivity, uncertainty, and failure modes. The goal is to move voice-based mental health models from controlled benchmarks toward reliable, deployable systems that engineers can reason about, evaluate, and integrate into real products.

Full name: Itamar Elmakias Degree program: PhD Supervisor: Dr. Dan Vilenchik (cc to this email) Seminar title: Choosing the Right Feature Selection Algorithm: Dataset Hardness, Algorithm Cost, and Practical Guidelines Seminar abstract: Feature Selection (FS) is a central component in modern machine learning pipelines, particularly for high-dimensional classification tasks. While a large body of research proposes new FS algorithms, practitioners still lack clear guidance on when FS is truly beneficial, which algorithms to use, and how to balance performance gains against computational cost and stability. In the first part of this seminar, I will present findings from our recent work on dataset hardness characterization . Using a large-scale empirical study across many real-world datasets, we show that the effectiveness of FS strongly depends on intrinsic dataset properties, and that the common assumption that FS is universally beneficial does not hold. We introduce a practical taxonomy of datasets based on their response to FS, providing an empirical lens for understanding when FS is likely to help. In the second part, I will present ongoing work that shifts the focus from datasets to algorithms. We analyze FS algorithms through multiple operational dimensions, including runtime, stability across cross-validation folds, average and maximum performance gains, and sensitivity to the number of selected features. Based on these analyses, we propose a cost-aware, bucketed framework for FS algorithm selection, offering actionable guidelines for choosing an FS method under different time budgets and dataset regimes. Overall, the seminar aims to bridge the gap between theoretical FS research and practical decision-making, providing evidence-based heuristics for selecting FS algorithms rather than treating them as black-box components.

Seminar abstract: Feature Selection (FS) is a central component in modern machine learning pipelines, particularly for high-dimensional classification tasks. While a large body of research proposes new FS algorithms, practitioners still lack clear guidance on when FS is truly beneficial, which algorithms to use, and how to balance performance gains against computational cost and stability. In the first part of this seminar, I will present findings from our recent work on dataset hardness characterization . Using a large-scale empirical study across many real-world datasets, we show that the effectiveness of FS strongly depends on intrinsic dataset properties, and that the common assumption that FS is universally beneficial does not hold. We introduce a practical taxonomy of datasets based on their response to FS, providing an empirical lens for understanding when FS is likely to help. In the second part, I will present ongoing work that shifts the focus from datasets to algorithms. We analyze FS algorithms through multiple operational dimensions, including runtime, stability across cross-validation folds, average and maximum performance gains, and sensitivity to the number of selected features. Based on these analyses, we propose a cost-aware, bucketed framework for FS algorithm selection, offering actionable guidelines for choosing an FS method under different time budgets and dataset regimes. Overall, the seminar aims to bridge the gap between theoretical FS research and practical decision-making, providing evidence-based heuristics for selecting FS algorithms rather than treating them as black-box components. Thank you very much for your time. I would be happy to provide any additional information if needed. Best regards,

Title: Physical Layer Security utilizing a Multi-Homodyne Coherent Detection scheme. Abstract: As quantum computing advances, conventional encryption faces an existential threat. The 'harvest-now-decrypt-later' strategy ensures that sensitive data transmitted today remains vulnerable to future decryption. This talk presents an innovative approach to Physical Layer Security: Multi-Homodyne Coherent Detection (MHCD). By leveraging the massive bandwidth and unique phase characteristics of mode-locked lasers, this scheme creates a robust defense that renders data un-harvestable by quantum adversaries. As it transitions from laboratory research to commercial availability, this technology offers a practical path toward quantum-secure communications. We will explore the underlying architecture of the MHCD scheme and the specific engineering hurdles overcome to transform a complex optical concept into a viable commercial product.

Abstract: One-bit quantization has garnered significant attention in recent years for various signal processing and communication applications. Estimating model parameters from one-bit quantized data can be challenging, particularly when the quantization process is explicitly accounted for in the estimator. In many cases, the estimator disregards quantization effects, leading to model misspecification. Consequently, estimation errors arise from both quantization and misspecification. Traditional performance bounds, such as the Cramér-Rao bound (CRB), fail to capture the impact of misspecification on estimation performance. To address this limitation, we derive the misspecified CRB (MCRB) for parameter estimation in a quantized data model consisting of a signal component in additive Gaussian noise. We apply this bound to direction-of-arrival estimation using quantized measurements from a sensor array and to frequency estimation with oversampled quantized data. The simulations show that the MCRB is asymptotically achieved by the mean-squared-error of the misspecified maximum-likelihood estimator. Our results demonstrate that, unlike in finely quantized scenarios, oversampling can significantly enhance the estimation performance in the presence of misspecified one-bit quantized

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