AI Researcher | PhD Student


Hi, I'm Hassan, a PhD student at the UKP Lab at Technische Universität Darmstadt (TUD) in Germany, supervised by Prof. Iryna Gurevych. Previously, I was an AI Researcher at the German Research Center for Artificial Intelligence (DFKI) in Berlin. I hold a Master’s degree in Computer Science from Saarland University, where I graduated with distinction. During my studies, I gained valuable industry experience at renowned organizations such as Bosch Center for AI (BCAI) in Germany, Amazon EU in Luxembourg, and the Max Planck Institute for Informatics, where I contributed as a Research Assistant.
My current PhD topic is on Multi-table Retrieval. My research in general centers on LLMs and Generative AI. I focus on developing innovative LLM-based applications, ranging from research prototypes to practical demonstrations. For instance, during my time at DFKI, I worked on creating a chatbot tailored for graduate students, aimed at improving their understanding of university courses. Additionally, I contributed to enhancing the user experience of AI-driven phone assistants by utilizing the capabilities of LLMs.
For my Master's thesis, supervised by Prof. Dietrich Klakow in collaboration with the NLP and Semantic Reasoning group at Bosch Center for AI, I explored Cross-Domain Neural Entity Linking. My work involved investigating a Transformer-based model to streamline domain adaptation by identifying optimal fine-tuning data across knowledge bases.
I am deeply passionate about researching and experimenting with AI models, balancing trade-offs, and applying these models to impactful real-world use cases. Outside of work, I enjoy photography, staying active through fitness, delving into philosophy, and immersing myself in new cultures through travel and group activities.
Hassan Soliman
Updates
2025
[03-2025] Presented my publication with the title "Retrieval-Augmented Chatbots for Scalable Educational Support in Higher Education" at the GenAL-LA workshop at the LAK'25 conference.
[02-2025] Presented an abstract poster with the title "A Survey on Advances in Retrieval-Augmented Generation over Tabular Data and Table QA" at the ELLIS workshop on Representation Learning and Generative Models for Structured Data.
[01-2025] Started a Ph.D. at the Technical University of Darmstadt in Germany. My first work is on researching and developing a robust multi-table retrieval system that not only retrieves relevant tables from complex databases but also ensures their joinability for coherent evidence assembly.
Interests
Artificial Intelligence (AI)
Natural Language Processing (LLM)
Information Retrieval (IR)
Question Answering (QA)
Retrieval Augmented Generation (RAG)
Education
Awards
Master of Science in Computer Science
Saarland Informatics Campus, Saarland University
2019 — 2022
Thesis Title: Cross-Domain Neural Entity Linking
Supervision: Prof. Dietrich Klakow
Saarbrücken, Germany
Bachelor of Science in Computer and Communication Engineering
Faculty of Engineering, Alexandria University
2013 — 2018
Thesis Title: Egyptian Car License Plate Information Detection
Supervision: Prof. Marwan Torki
Alexandria, Egypt
Second Best Demo Paper
ECTEL
2024
Krems, Austria
First Class Honor Degree
Alexandria University
2018
Alexandria, Egypt
Philosophy Doctor in Computer Science
Uniquoutious Knowledge Processing Lab,
Technische Universität Darmstadt
2025 — Present
Thesis Title: Safeguarding Multi-modal LLMs against Misleading Evidence Attacks (Multi-table Retrieval)
Supervision: Prof. Iryna Gurevych
Darmstadt, Germany
Selected Publications
[09-2024] Hassan Soliman, Miloš Kravčík, Alexander Tobias Neumann, Yue Yin, Norbert Pengel and Maike Haag. 2024. Scalable Mentoring Support with a Large Language Model Chatbot. Technology Enhanced Learning for Inclusive and Equitable Quality Education (ECTEL), September 16–20, 2024, Krems, Austria, 6 pages.
ECTEL'24
ACL'24
RepL4NLP
[05-2022] Hassan Soliman, Heike Adel, Mohamed H. Gad-Elrab, Dragan Milchevski, and Jannik Strötgen. 2022. A Study on Entity Linking Across Domains: Which Data is Best for Fine-Tuning?. In Proceedings of the 7th Workshop on Representation Learning for NLP, ACL, 184–190, Dublin, Ireland.
Experience
AI Researcher
German Research Center for AI (DFKI)
Jan 2023 — Dec 2024
Led two projects in the Educational Technology lab, managing technical implementation and supervising two students.
Developed a chatbot for a graduate-level course that answered student queries with 87% accuracy. One of the two papers published on the project was nominated for the Best Demo Award at ECTEL 2024.
Applied advanced Retrieval-Augmented Generation (RAG) techniques, including Hybrid Ensemble Search and Reranking Mechanism, to enhance chatbot interactions and improve the retrieval of course materials.
Supported mentoring-style conversations by leveraging flexible agentic workflows with LangGraph, utilizing multiple small open-source models hosted on Azure, and using databases for user usage tracking and monitoring.
Implemented a sub-module for adaptive dialogue systems, customizing responses based on user emotional state and demographics, and benchmarking performance using OpenAI LLMs and open-source models.
Berlin, Germany


Applied Scientist Intern
Bosch Center for AI (BCAI)
May 2022 — Aug 2022
Contributed to the NLP & Semantic Reasoning group, applying findings from my master’s thesis on Neural Entity Linking to a high-impact industrial project using real data at Bosch.
Refactored, tested, and documented production-level code for machine learning models, ensuring scalability and efficiency for real-world deployment, leveraging the in-house GPU cluster for model fine-tuning.
Trained and evaluated machine learning models on a large-scale domain-specific dataset, achieving 77% end-to-end recall for top-3 entity predictions, outperforming existing models.
Renningen, Germany


Research Assistant
Max Planck Institute for Informatics (MPII)
Nov 2020 — May 2021
Developed a model prototype within the Database & Information Systems group to identify diverse peer groups for entities, contributing to advanced set expansion techniques.
Implemented a baseline model for entity set expansion, leveraging Wikipedia lists as a knowledge source to enhance the model’s accuracy and comprehensiveness in the expanded sets.
Optimized the algorithm’s performance by achieving a 3x faster runtime using efficient sparse matrix multiplication techniques, significantly improving computational efficiency.
Saarbrücken, Germany
Master's Thesis Student
Bosch Center for AI (BCAI)
Jun 2021 — Jan 2022
Joined the NLP & Semantic Reasoning group and worked on a unified system for linking named entities to general-domain (Wikipedia) and domain-specific knowledge bases (KBs), using context-aware embeddings (BERT) to learn a joint vector space. A pre-print of the thesis is available on arXiv. https://arxiv.org/abs/2210.15616.
Optimized a state-of-the-art model for cross-domain applications, supporting domain extension and identifying optimal data sources for fine-tuning, and improved GPU memory utilization for efficient embedding calculations.
Achieved a 9% increase in Average Precision for the top-1 entity and a 20% gain in Mean Average Precision (MAP) for top-10 entity linking across four domain-specific KBs, resulting in a workshop publication at ACL 2022.
Renningen, Germany
Software Development Engineer Intern
Amazon
Aug 2019 — Feb 2020
Maintained a web-based simulation tool for the Fulfillment Acceleration team using the AWS cloud platform, working as a full-stack software engineer.
Enhanced delivery speed simulations for prime customers, contributing to a successful report on fulfillment operations and improving delivery efficiency.
Collaborated as a system administrator in an Agile environment, managing server infrastructure and providing technical support for team tools.
Luxembourg, Luxembourg






Projects
SmolLM: Implementing, Fine-Tuning, and Aligning a LLM for Grammatical Error Correction
Implemented the SmolLM-135M (by HuggingFace) language model architecture, including components like Rotary Positional Embeddings, KV Cache, and Grouped-Query Attention, RMS Normalization, and SwiGLU Activation.
Fine-tuned the model on the Grammatical Error Correction (GEC) task using the Grammarly CoEdIT dataset.
Applied RLAIF through Direct Preference Optimization (DPO) to align model outputs with desired corrections.
Created a Colab notebook to guide users through implementation, fine-tuning, and evaluation processes.
Achieved significant improvements in grammatical error correction accuracy, scoring an expected BLUE score of ∼ 0.48.
Leveraged Python libraries such as PyTorch, Transformers, Datasets, and TRL to build and train the model effectively.
The code is open-source and published on Github.




LinguaLexMatch: Enhanced Document Language Detection
Developed and evaluated three language detection models, including an Embedding-Based approach, a TF-IDF-based Multinomial Naive Bayes model, and a fine-tuned Transformer-Based methodology.
Implemented an embedding-based approach using the intfloat/multilingual-e5-large-instruct model by generating a representative embedding for each language and classifying documents based on cosine similarity.
Benchmarked models on the papluca/language-identification dataset, achieving 99.81% accuracy with the embedding-based model.
Analyzed performance metrics such as Accuracy, F1 Scores, and Confusion Matrices across 20 different languages.
Developed a Colab notebook for replicable implementation and evaluation of different language detection models.
Utilized Python libraries including Datasets, Transformers, and Scikit-learn for model development and evaluation.
The code is open-source and published on Github.


Scalable Mentoring Support with a LLM Chatbot
Designed and implemented a chatbot based on an LLM-based Agent to provide scalable educational support and timely feedback to students of education sciences, demonstrating significant potential of generative AI in education.
Utilized Advanced techniques in Retrieval Augmented Generation (RAG) to enhance chatbot interactions, e.g., Hybrid Ensemble Search and Reranking Mechanism, enabling it to retrieve and analyze course materials effectively.
The code is subject to a Non-Disclosure Agreement.


Using LLMs for Adaptive Dialogue Management
Adapted user-directed utterances using LLMs based on the user's parameters like gender, age, and sentiment, aiming to optimize user satisfaction in conversational AI systems, focusing on healthcare patient-practice interactions.
Evaluated different LLMs and open-source tools for effectiveness in utterance adaptation, in terms of speed, cost-effectiveness, and quality of the generated text based on the adaptation relevancy and adaptation adequacy.
The code is subject to a Non-Disclosure Agreement.