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Eyad Alshami

PhD Student at Max Planck Institute for Informatics (MPI-INF)

Germany

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Overview
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I am a PhD Candidate in Neuroexplicit Models at the Max Planck Institute for Informatics (MPI-INF) and the CISPA Helmholtz Center for Information Security. My research is conducted within the Computer Vision and Machine Learning group at MPI-INF, under the supervision of Prof. Bernt Schiele, and co-supervised by Prof. Jilles Vreeken at CISPA. My primary research focus lies in enhancing the explainability and interpretability of deep learning models, with applications in computer vision and machine learning. I aim to develop methods and frameworks that make complex deep learning models more transparent, understandable, and trustworthy, addressing one of the critical challenges in modern AI research.

Background

Tech-AI Models Building/Training Tech-Machine Learning and AI

Arabic English

Experiences

Research Intern

ZF Group (01 Jul 2022 - 01 Aug 2022)

Deep Learning Algorithms in Trajectory Prediction. The project involves literature research for L3 and L4 automated driving scenarios and the implementation of state-of-the-art reference architectures. Tasks: Screening of the literature on trajectory prediction based on machine learning algorithms (sequence-to-sequence learning), developing Python modules for data sets processing and demonstration, and supporting the team in the development and implementation of Deep Learning algorithms. The project involves literature research for L3 and L4 automated driving scenarios and the implementation of state-of-the-art reference architectures. Tasks: Screening of the literature on trajectory prediction based on machine learning algorithms (sequence-to-sequence learning), developing Python modules for data sets processing and demonstration, and supporting the team in the development and implementation of Deep Learning algorithms.

Research Assistant

CISPA Helmholtz Center for Information Security (01 Sep 2023 - 01 Sep 2024)

Comprehensive evaluation of prompt propagation for language models. The objective key areas for this project comprised: 1. The Sequencing of Prompts: Analyzed measurable differences pertaining to prompt prioritization within the context of the Language Learning Model (LLM). 2. Prompt Wording: Assessed prompt phrasing to ascertain if certain strategies were more effective and yielded more success, particularly from a prompt overwriting perspective.

Educations

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