I’m passionate about exploring how we can understand preferences, model user behavior, and enhance recommendation systems and information retrieval to make technology feel more personal and meaningful.
Connect with me via Email, LinkedIn, or Twitter; explore my work on Google Scholar and GitHub; and if you'd like to hire me, here's my CV.
I ’m a passionate researcher and final-year PhD student at TU Wien, driven by a deep curiosity for understanding data and improving personalized recommendation systems. My work centers on uncovering implicit item characteristics to enhance recommendation relevance and user satisfaction, particularly in scenarios where limited interaction data poses challenges for traditional approaches. I’m dedicated to decoding subtle user preferences that are often unspoken yet influential. Alongside this, I have a strong interest in information retrieval and the rigorous evaluation of these technologies, which fuels my continuous drive to innovate within the field.
My research in Information Retrieval and Natural Language Processing has focused on developing robust evaluation methodologies across multiple test collections, with applications in both IR and Recommender Systems. This work is inspired by the need for more reliable, multi-task evaluation techniques that demonstrate the accuracy and applicability of these technologies.
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In this research trajectory, I focus on enhancing news recommendation systems by incorporating sentiment and emotional cues, while exploring their effects on diversity in recommendations. This work highlights both the benefits and risks of sentiment-based models in promoting diverse content.
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In this research, I explored tourism destination characteristics by leveraging gamified image-based strategies to uncover implicit user preferences. The focus was on understanding and predicting user needs through subtle preference elicitation techniques.
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For an extensive list of publications visit Google Scholar.
TU Wien, Austria
As a Research Assistant at TU Wien, I conducted extensive research in User Modeling, Recommender Systems, and Information Retrieval, collaborating with researchers from diverse backgrounds and open-sourcing impactful tools and artifacts. I worked closely with industry partners, aligning research with practical needs, and shared my expertise in the classroom—lecturing, assisting in courses, and mentoring students to support their academic and professional growth.
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Wingpaper, Austria
At Wingpaper, a startup aiming to create a "YouTube for documents," I focused on enhancing platform scalability and accessibility. My role involved ingesting data—primarily PDFs—transforming it into searchable and presentable formats. To make content more engaging, I developed a hybrid recommender system that used both content-based and collaborative filtering, boosting recommendation quality and improving user engagement metrics.
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hyperCMS , Austria
As a Web Developer at hyperCMS, I developed scalable, customized web solutions for global clients and built a comprehensive e-learning platform. I also provided hands-on training to ensure effective platform use across diverse user groups.
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Recognized as one of 16 nominees for best reviewer out of 264 reviewers, demonstrating a commitment to research quality.
Pitched ImageTwin, a tool to combat plagiarism and data fabrication, and won a €5,000 scholarship with workplace incubation at TUWi2 (TU Wien's startup incubator).
Awarded Best Article for “ What is the Personality of a Tourism Destination?” published in Information Technology & Tourism, selected as top article out of 31 submissions.
Won Best Master’s Thesis Award for “ Classifying and Mapping e-Tourism Data Sets”, selected out of 27 thesis submissions.
TU Wien, Austria
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TU Wien, Austria
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TU Wien, Austria
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Issued by DeepLearning.AI
Issued by IBM
Issued by Google
Issued by DeepLearning.AI and Stanford University
Issued by Imperial College London
Issued by DeepLearning.AI
German (native), Turkish (native), English (full professional).
Python, JavaScript, R, Java.
Reviewed submissions for major conferences and journals, including ENTER, CIKM, JITT, RecSys, SIGIR, and UMAP, contributing to the quality and rigor of academic discourse in information retrieval and recommender systems.
Led online session management and video production for the UMAP 2022 Conference, coordinating with a team of four to ensure smooth online participation for all attendees.
Enhanced accessibility of Soviet Holocaust film documentation by implementing an OCR-Translate-Index pipeline and conducting a comparative study, selecting DeepL as the optimal tool for translating caption sheets and reports.
Launched DIGHUM during COVID by setting up a website, managing bi-weekly virtual lectures, and creating a YouTube channel (100+ videos, 1300+ subscribers). Supported the DIGHUM manifesto with 1000+ signatures and coordinated four on-site workshops.