| FACULTY | ACTIVE RESEARCH TOPICS | PROJECTS |
|---|---|---|
 | Çalışma Alanları: Bilgisayarla Görü, Makine Öğrenimi, Yapay Zeka, Video Adaptasyonu, Video İşleme Genel Bilgi: Bu çalışma alanında, uzamsal çözünürlük bağlantılı ve derin öğrenme modellerine dayanarak ölçümlenen derinlik ipuçlarını temel alarak, bant genişliği, izleyici cihaz ekran boyutu ve farklı ortam aydınlatma koşulları kısıtlamaları altında, derinlik algısını optimum seviyede tutan ve iletim kanalını en etkin şekilde kullanan özgün bir adaptasyon karar alma tekniği ve onunla uyumlu çalışan yenilikçi bir 3B video adaptasyon modeli geliştirilecektir.
İlgili Dersler: Bilgisayarla Görü (CS 566) | Tübitak Projesi:
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 | Research Areas: Computer Graphics, Human Computer Interaction, Virtual Reality, Computer Vision, Image Processing
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 | 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.  | |
 | Çalışma Alanları: Sinyal İşleme, Görüntü/Video İşleme, Makine Öğrenimi Genel Bilgi: Sinyal ve görüntü işleme, uzaktan algılama, hiperspektral veri işleme, görüntüden nesne tanıma, nesne takibi, biyolojik veri işleme; bu alanların makine öğrenmesi ve yapay zeka teknik ve modelleri ile birleştirilmesi şeklindedir.
Dersler: Sinyaller ve Sistemler, Dijital Görüntü İşleme
| Tübitak Projesi:
BAP Projesi:
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 | 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
 Couses: Introduction to Machine Learning (CMPE 442), Machine Learning (CS 542) Students: Yaşar Anıl Sansak (MSc, alumni), Mert Kaya (MSc) | IRF Project:
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 | 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.
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 | Çalışma Alanları: Duygu Analizi, Doğal Dil işleme, Bilgi Erişim Sistemleri, İstatistik, Veri Madenciliği, Derin ve Makine Öğrenimi (Yapay Zeka)
Dersler: Bilgi Erişimi |
