| FACULTY | ACTIVE RESEARCH TOPICS | PROJECTS |
|---|---|---|
 | Working Areas: Computer Vision, Machine Learning, Artificial Intelligence, Video Adaptation, Video Processing General Information: In this study, an original adaptation decision-making technique and an innovative 3D video adaptation model that works in harmony with the spatial resolution-related and measured depth cues based on deep learning models will be developed, which keeps the depth perception at the optimum level and uses the transmission channel in the most efficient way under the constraints of bandwidth, viewer device screen size and different ambient lighting conditions.
Related Courses: Computer Vision (CS 566) | TUBITAK Project:
|
 | Research Areas: Computer Graphics, Human Computer Interaction, Virtual Reality, Computer Vision, Image Processing
| |
 | General Info: Main area of interest falls in the area of distributed and parallel processing: The field involves operating systems, networking, and parallel or distributed applications. The subject covers systems as well as applications’ issues. In particular, focused load balancing, routing, and reconfigurations issues. | |
 | Research Areas: Machine Learning for Healthcare, Information Security and Biometrics General Info: Heartprint signal is biometric modality which can be captured noninvasively using existing sensors. We investigate the acceptable methods of capturing heartprint signal with noninvasive and remote sensors, and possible machine learning techniques for secured biometric recognition. Possible applications on this modality in various domains include continuous biometric authentication, secured identity management, digital signature verification, liveness detection in multimodal biometrics, cryptographic key generation, and identity monitoring. This emerging biometric modality can play an important role in the digital identity management and information security in the era of generative machine learning.  | |
 | Research Areas: Image Processing, Computer Vision, Machine Learning, Symmetry Analysis and Classification of Repetitive Patterns General Info: The main interest lies in the studies of different aspects of repetitive patterns: automatic classification, capturing style in patterns, transferring style between patterns, symmetry breaking, metamorphosis
 Related Courses: Introduction to Machine Learning (CMPE 442), Machine Learning (CS 542) Students: Yaşar Anıl Sansak (MSc, alumni), Mert Kaya (MSc) | IRF Project:
|
 | Research Areas: Artificial Intelligence, Deep Reinforcement Learning, Natural Language Processing General Info: Eren Ulu's research centers on Deep Reinforcement Learning (DRL), aiming to create algorithms that help autonomous agents learn complex decision-making tasks from their environment. His work spans various fields like robotics, gaming, and autonomous systems, investigating both the theory and practical applications of DRL to tackle real-world problems. Additionally, he explores the synergy between DRL and Natural Language Processing (NLP), seeking novel ways to train autonomous agents to understand and produce human language.
  | |
 | Research Areas: Sentiment Analysis, Natural Language Processing, Information Retrieval Systems, Statistics, Data Mining, Deep and Machine Learning (Artificial Intelligence)
Related Courses: Information Access |
