Full name: Ariel Harush Full Name: Nadav Amitai Full name: Kobi Kenzi

Name: Ariel Harush Igal Bilik, Gilad Katz Master-Agent “proto-plan” System (MAPS) Distributed Learning-based Approach for Joint Navigation in Local Environments :Abstract Coordinating autonomous vehicles at unsignalized inter- sections remains challenging for multi-agent reinforcement learning (MARL) systems, which often require large action spaces, privileged information, or homogeneous agent de- signs that limit real-world deployment. We propose a hier- archical deep reinforcement learning (DRL) architecture in which a central Master agent generates a compact embed- ding, denoted as a “proto-plan,” that encodes a high-level coordination strategy. Individual Worker agents then com- bine this “proto-plan” with their local observations to deter- mine vehicle-specific actions, effectively decoupling strate- gic coordination from low-level control. This design enables heterogeneous agent types, supports flexible action spaces without combinatorial explosion, and allows independent re- training of coordination and control modules. Experiments across 72 diverse intersection configurations in the High- wayEnv simulator demonstrate that the proposed approach achieves collision-free navigation while maintaining high traffic throughput, outperforming state-of-the-art baselines. Our results show that “proto-plan”-based hierarchical learn- ing provides a scalable and adaptable framework for multi- vehicle coordination in complex traffic scenarios. Full Name: Nadav Amitai. Degree: M.Sc. in Electrical and Computer Engineering. Supervisors: Koby Todros and Igal Bilik. Seminar Title: Measure-Transformed Recursive Least Squares Abstract: This research deals with robust system identification in the presence of impulsive noise. To this end, we present a new robust variant of the recursive-least-squares (RLS) estimator, called measure-transformed (MT)-RLS. The MT-RLS is an exact recursive counterpart to the recently developed MT least-squares estimator (MT-LSE), which operates by applying a transform to the probability measure of the data. This stands in contrast to other robust RLS variants that only approximate robust batch estimators. The considered measure transform is generated by a non-negative data-weighting function, called the MT-function. We have previously shown that a properly chosen MT-function can significantly mitigate the influence of outliers arising from impulsive noise, thereby substantially enhancing the estimation accuracy of the MT-LSE. Consequently, as an exact recursive extension, the MT-RLS fully inherits the robustness of MT-LSE. The MT-RLS is illustrated in simulations, underscoring its advantage over RLS and other robust extensions in both stationary and non-stationary environments. Full name: Kobi Kenzi Name of supervisor: Dr. Dan Vilenchik Degree program: M.Sc. in Communication Systems Engineering Seminar topic: From Messy ICU Signals to Causal Insight: Learning Meaningful Patient States from Irregular Clinical Time Series Seminar abstract: This seminar presents a research pipeline for applying causal inference methods to irregular clinical time-series data, focusing on ICU datasets such as PhysioNet 2012 and MIMIC-III. The work combines time-series preprocessing, clinically meaningful latent variable construction, causal graph design, confounder selection, and treatment-effect estimation. The main goal is to explore how latent clinical states and domain-informed causal assumptions can support more interpretable analysis of patient trajectories and potential clinical effects, while avoiding strong unsupported medical causal claims. The seminar emphasizes the methodological framework, implementation challenges, validation strategies, and the role of causal inference in extracting meaningful insights from complex medical time-series data