During the Spring Semester of 2025–2026, the seminar series organized by the ITU Data Science and Analytics Department brought together students and researchers with speakers from diverse academic and research backgrounds. The seminars covered a range of contemporary and interdisciplinary topics, including earthquake search-and-rescue planning, equity-oriented reinforcement learning for taxi dispatch systems, physics-informed machine learning, artificial intelligence applications in biomedical research, combinatorial optimization, and large language models. These events provided participants with opportunities to follow current scientific developments, gain insights into different research areas, and engage with experts in their respective fields.
| Date |
Seminar Title |
Speaker |
Brief Summary |
| 07 April 2026 |
Integrated Multi-Period Planning for Earthquake SAR: A Preliminary Case Study of Istanbul |
Dr. Nadi Serhan Aydın |
A multi-period optimization model for earthquake search-and-rescue planning in Istanbul was presented. |
| 14 April 2026 |
GiniDispatch: A Gini-Regularized Reinforcement Learning for Joint Earnings and Workload Equity in Taxi Dispatch |
Dr. Tuğrul Cabir Hakyemez |
A reinforcement learning approach aimed at improving income and workload equity in taxi dispatch systems was discussed. |
| 21 April 2026 |
Physics-Informed Machine Learning for Additive Manufacturing under Small-Data Constraints |
Dr. Sultan Zeybek |
Physics-informed machine learning methods for additive manufacturing under limited-data conditions were presented. |
| 28 April 2026 |
From Physical Models to Intelligent Systems: Artificial Intelligence-Based Approaches in Biomedical Research |
Dr. Emel Sokullu |
Applications and benefits of artificial intelligence in biomedical research were explored. |
| 12 May 2026 |
Combinatorial Optimization, Planning and Scheduling, Reinforcement Learning, and Reasoning with Large Language Models |
Dr. Zangir Iklassov |
The use of reinforcement learning and large language models in combinatorial optimization problems was examined. |
