AI Projects #9 Report
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!
AI Projects #9 kicked off on May 30 with 28 participants and 9 project proposals. During the first four weeks, senior participants focused on developing their project proposals, while junior participants prepared through various training sessions supported by DataCamp, which provided full-course scholarships for our program!
After this preparation phase, juniors and seniors teamed up around the projects that matched their interests and areas where they wanted to improve. After three months of dedicated work, the program culminated in the AI Projects Showcase held on September 27 at Beykoz Kundura, where 25 participants successfully brought 7 projects to life!
THE PROJECTS
1. Spatiotemporal Forecasting of Marine Mucilage with Sentinel-2 and ERA5: A Computer Vision Approach, Simge Şenyüz, Ayşe Yeşil, Serkan Kızılırmak
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 to compare different algorithm performances and try to explain the relation between climate variables and mucilage.
2. Recipe Recommender using Computer Vision and LLMs, Kayra Kösoğlu, Mehmet Anıl Taysi, Simge Şenyüz
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.
3. 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.
4. 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.
5. DeTraffic: Deep Reinforcement Learning to De-Traffic Our Lives, Mehil Darcan, Volkan Bakır, Yusuf Koca
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.
6. Pediatric Appendicitis Detection on US Images, Hatice Karataş, Burak Emre Polat, Ege Uğur Amasya
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.
7. Agent Based Task Oriented Dialogue System with Tool Calling Capabilities, Kerem Kosif, Eren Torlak, Umut Alkan, Yağmur Seda Tankutlu
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.
You can see the report of the 7th batch here and the 8th batch here.
Thank you for your interest. See you in the next batch!
All participants have to abide by our CODE OF CONDUCT and LETTER OF CONSENT.
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