AI Projects #9 Showcase
AI Projects #9 Showcase will take place on Saturday, September 27 from 12.30 to 18:00, at Beykoz Kundura.
AI Projects is a 4-month research program where undergraduate, master's, and doctoral students collaborate to develop cutting-edge AI solutions. This intensive program provides a unique environment for participants to explore innovative ideas, conduct meaningful research, and build impactful projects.
Our ninth cohort brought together 25 passionate students who dedicated four months (May 30 - September 27) to developing 7 projects that address real-world challenges. From marine environmental monitoring to healthcare diagnostics, from cultural preservation to advanced NLP architectures, we are excited to share the results with you!
You can see the report of the 7th batch here and the 8th batch here.
Duration: 7 projects; 15 minutes presentation & 5 minutes Q&A each
Application Deadline: September 21 , 23.59.
Confirmation: by September 23, 23.59
We kindly ask you to apply for our exciting event due to limited space availability. You will be notified with a visitor form once your spot is confirmed!
Showcase PrograM
12.30 - 13.00 Welcoming & Onboarding
13.00 - 13.10 Ready to Showcase! (Pınar Yıldız, Program Manager, AI)
13.10 - 13.20 inzva’s Journey (Havva Yüksel, Program Lead, Algorithm)
13.20 - 13.30 AI Projects in Action (Zeynep Abalı, AI Projects Program Lead)
13.30 - 13.50 Recipe Recommender using Computer Vision and LLMs
Simge Şenyüz, Ayşe Yeşil, Serkan Kızılırmak
13.50 - 14.10 FolkBert : Robust Pose Estimation for Traditional Folk Dances
Melih Darcan, Ece Akdeniz, Muhammet Furkan Küpçü
14.10 - 14.30 Pediatric Appendicitis Detection on US Images
Hatice Karataş, Burak Emre Polat, Ege Uğur Amasya
14.30 - 14.50 MCP-Orchestrated Multi-Model Finance Chatbot
Güldeniz Bektaş, Ahmet Emir Topbaş, Şerife Gül Korkut, Taylan Ünal
14.50 - 15.50 Lunch Break
15.50 - 16.10 Hybrid SSM-Transformer Architectures for Long Context NLP
Kuzey Torlak, Anıl Dervişoğlu, Beyza Nur Deniz, Hüseyin Arda Aslan
16.10 - 16.30 Spatiotemporal Forecasting of Marine Mucilage with Sentinel-2
and ERA5: A Computer Vision Approach
Ece Özen İldem, Ahmet Zelka, Arya Zeynep Mete, Yiğit Kaya Balcı
16.30 - 16.50 Agent Based Task Oriented Dialogue System with Tool Calling Capabilities
Kerem Kosif, Eren Torlak, Umut Alkan, Yağmur Seda Tankutlu
16.50 - 17.15 Closing Remarks
Scroll to explore the projects↓
THE PROJECTS
Spatiotemporal Forecasting of Marine Mucilage with Sentinel-2 and ERA5: A Computer Vision Approach
Ece Özen İldem, Ahmet Zelka, Arya Zeynep Mete, Yiğit Kaya Balcı
This project leverages Sentinel-2 multispectral imagery (10–20 m resolution) and ERA5 climate variables (e.g., SST, wind, humidity) to detect and forecast marine mucilage in the Marmara Sea, the aim is comparing different algorithm performances and try to explain relation between climate variables and mucilage.
Recipe Recommender using Computer Vision and LLMs
Simge Şenyüz, Ayşe Yeşil, Serkan Kızılırmak
This project develops an end-to-end pipeline that utilizes computer vision for the automatic detection and segmentation of ingredients from a user's fridge, combined with an LLM enhanced by a RAG framework to generate personalized and practical recipe suggestions. The primary goal is to create a system that accurately identifies food items and delivers highly relevant cooking recommendations.
FolkBert : Robust Pose Estimation for Traditional Folk Dances
Melih Darcan, Ece Akdeniz, Muhammet Furkan Küpçü
This project focuses on developing a robust pipeline for pose estimation in traditional folk dances. By introducing a newly curated dataset and employing fine-tuned models, it seeks to accurately capture intricate body movements under the challenges posed by traditional clothing and accessories. The proposed approach is expected to advance research in motion analysis, enhance educational tools for dance instruction, and support the preservation and systematic study of cultural heritage.
Hybrid SSM-Transformer Architectures for Long Context NLP
Kuzey Torlak, Anıl Dervişoğlu, Beyza Nur Deniz, Hüseyin Arda Aslan
This project explores a hybrid model combining State Space Models (SSMs) like Mamba with Transformer attention layers to improve long-context language modeling. The architecture aims to balance efficient long range memory with localized attention based reasoning. Different configurations (eg stacked, alternating) will be implemented and evaluated on long sequence benchmarks. Results will compare performance and speed against baseline SSM-only and transformer only models.
Pediatric Appendicitis Detection on US Images
Hatice Karataş, Burak Emre Polat, Ege Uğur Amasya
This study presents a decision support system for appendicitis diagnosis using ultrasound imaging. The framework comprises three stages:
Localization: A YOLO-based detector identifies the appendix region in B-Mode ultrasound scans.
Segmentation: A U-Net model delineates the appendix contour and estimates its diameter.
Decision: Morphological features are combined with clinical and laboratory data, which are classified by an XGBoost model to produce the final diagnosis.
The system was trained and validated on static ultrasound slices, and will be further evaluated on dynamic video streams for real-time performance.
Agent Based Task Oriented Dialogue System with Tool Calling Capabilities
Kerem Kosif, Eren Torlak, Umut Alkan, Yağmur Seda Tankutlu
This project aims to create a better system for task-focused conversations to replace older models that are difficult to change and expensive to maintain. The new approach uses a team of specialized AI agents that work together, with each agent handling a key part of the conversation: understanding the user, tracking the chat's progress, deciding what to do next, and creating a response. This system will use large language models and can call on external tools to get things done, with the goal of building a conversational AI that is more flexible, easier to expand, and works better in real-world situations.
MCP-Orchestrated Multi-Model Finance Chatbot
Güldeniz Bektaş, Ahmet Emir Topbaş, Şerife Gül Korkut, Taylan Ünal
Customer service in the financial sector requires fast, accurate, and multilingual support, particularly for fraud detection and product inquiries. This project develops a modular, MCP-orchestrated chatbot that coordinates multiple specialized agents including Money, Fraud, Product, and Translation Agents to process real-time user queries. By integrating transaction history, account balances, credit information, and multilingual capabilities, the system leverages agent collaboration to handle diverse customer needs and ensure reliable interactions. The orchestration layer ensures smooth communication between agents, enabling context-aware and multi-agent dialogue management.
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