Research Focus

Trustworthy Intelligent Computing

A trustworthy system does what its legal users expect it to do – and not something else – despite environmental disruption, impairments caused by itself, human users and operators, and potential attacks by hostile parties. Design and implementation of errors must be avoided, eliminated, or somehow tolerated. It is not sufficient to address only some of these dimensions, nor is it sufficient simply to assemble components that are themselves trustworthy. Thus, trustworthiness is multi-facetted and holistic.

Intelligent computing aims to integrate systems to perceive, reason, learn, and act intelligently in the real world. As the real world data and processes are dynamic and stochastic, an intelligent system has to deal with large volumes of data, occasional updates to the data by the users, and unexpected changes in data distributions due to nuisance factors. Therefore, the design of such systems involves hard scalability and robustness constraints that needs to be satisfied. Accordingly, our research areas include but are not limited to the following:

Intelligent Systems

The objective in this research area is to develop smart components or autonomous systems. Classical artificial intelligence topics like, mathematical logics, approximate reasoning or distributed planning are combined with computational intelligence to hybrid techniques. Past projects in this area aimed at developing smart mechatronic devices, like autonomous robotic vision or re-configurable devices, as well as robotic software, like embodied and emergent cognitive architectures or generating ontologies from statistical sensory data with fuzzy syllogistic reasoning.

Our research concentrates on following topics:

  • Distributed Intelligent Virtual Environments (DIVE) Laboratory, directed by Assist. Prof. Bora Kumova
    • Smart Mechatronic Devices
    • Statistical Ontologies
    • Autonomous Robotic Vision
    • Emergent Cognitive Architectures

Information Systems Management

Information systems are information technologies, which are typically designed to enable humans to perform tasks for which the human brain is not well suited, such as: handling large amounts of information, performing complex calculations, and controlling many simultaneous processes. A typical information system is complementary hardware and software that people and organizations use to collect, filter, process, create and distribute data. Information Systems Management encompasses a variety of disciplines such as: information security, database management and decision support systems. There are various types of information management systems, for example: transaction processing systems, decision support systems, knowledge management systems, learning management systems, database management systems, and office information systems.

Our research concentrates on following topics:

  • Data Analytics Research Group (DARG), directed by Assist. Prof. Selma Tekir
    • Semantic analysis.
    • Representing and measuring high-level information characteristics such as coherence, credibility, and trust.
    • Data mining and fusion applications that support information warfare capabilities and facilities.
  • Data Processing Research Group (DWorld), directed by Assoc. Prof. Belgin Ergenç
    • Large Scale Query Optimization
    • Federated Query Processing on Linked Data
    • Dynamic Itemset Mining and Hiding
  • Information Systems Security Research Group (ISSRG), directed by Assist. Prof. Serap Şahin
    • Identification
    • Authorization
    • Privacy

Dependable Components and Systems

Dependability includes non-functional system features such as reliability, availability, safety, security etc. and efficient techniques for such trustworthy hardware components and software systems for the systems that society depends on, such as power grids, transportation, medicine, and finance, are vital, since failures in these complicated systems may lead to significant financial losses or even human casualties. Methodologies, architectures, and mechanisms that support modeling, design, implementation, and evaluation of trustworthy hardware and software systems and applications are our main concern.

Our research concentrates on following topics:

  • SoC and IC Testing
  • Model Checker based Testing and Verification
  • Fault-tolerant Design
  • Software Vulnerability Detection
  • Validation, Verification, and Testing
  • Software Reliability / Fault Tolerance
  • Metrics, Measurements, and Analysis

Members:

Computer Vision

Computer Vision (CV) research focuses on analyzing and understanding visual data in order to extract meaningful information contained within still images and video streams. Example outputs of computer vision algorithms include detected object locations and labels (such as faces, cars, pedestrians), a 3D model of the scene generated from images, images containing virtual objects rendered from the camera point-of-view, and pixel-level segmentation of the objects.

Our research concentrates on following topics:

  • Computer Vision Research Group (CVRG), directed by Assoc. Prof. Yalın Baştanlar
    • 3D Reconstruction from Images
    • Visual Object Detection/Classification
    • Omnidirectional Vision
    • Vision for Traffic Analysis
  • Visual Intelligence Research Group (VIRG), directed by Assist. Prof. Mustafa Özuysal
    • Real-time Object Detection and Tracking
    • Large Scale Object Identification
    • Augmented Reality
    • Scene Text Recognition
  • Computer Simulation, Intelligence and Graphics for Biometrics (CSI:GB) Lab, directed by Assist. Prof. Nesli Erdogmus
    • Computer Vision for Forensics
    • Biometric Recognition
    • Biometric Security
    • Realistic Biometric Data Generation