First-Class Honours graduate in Computer Science with AI from the University of Nottingham, building reliable, transparent, and human-centered AI systems — at the intersection of LLMs, NLP, Explainable AI, and AI Safety. This September, starting an MSc in Cognitive Science at the University of Edinburgh.
I'm interested in AI research and engineering, with a particular focus on building reliable, transparent, and human-centered artificial intelligence systems. My interests lie at the intersection of Large Language Models (LLMs), Natural Language Processing, Explainable AI, AI Safety, and trustworthy machine learning.
I recently graduated with First-Class Honours in Computer Science with Artificial Intelligence from the University of Nottingham. During my undergraduate studies, I conducted research on zero-shot learning for financial market prediction using LLMs and financial news. Alongside research, I enjoy building practical AI systems that bridge academic ideas with real-world applications — from financial risk prediction, to multimodal human-robot interaction using ROS 2 and LLMs, to explainable AI pipelines for decision support.
My long-term goal is to contribute to frontier AI systems that are not only capable, but interpretable, reliable, and aligned with human values.
Originally from Istanbul, Turkey, currently based in Edinburgh, United Kingdom, where I'll continue my studies with an MSc in Cognitive Science at the University of Edinburgh this September.
View CV (PDF)@article{karaoglu2026zeroshot,
author = {Karaoglu, Ali Mert},
title = {Zero-Shot Learning and Explainability for Stock Market Movement Prediction},
journal = {arXiv preprint arXiv:2606.12210},
year = {2026}
}
Agent-based simulation comparing evacuation guide policies across a 14,400-run factorial experiment.
ML pipeline predicting treatment outcomes from clinical variables and MRI radiomics data.
Intent-classification chatbot handling multi-step Q&A and simulated purchase flows.
ROS 2 multimodal human-robot interface combining voice, gesture, and LLM-driven control.
Learning-based hyper-heuristic adapting strategy selection for the Sight-Seeing Problem.
Feel free to reach out — happy to chat about AI research, collaborations, or anything else.