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Dr. Kozat conducts research broadly, but not limited to, in the following topics:

  • Machine Learning for Signal Processing,
  • Big Data and Analytics,
  • Deep Learning,
  • Text and Image Processing,
  • Online Learning,

Dr. Kozat performs and leads several research activities in many different signal processing fields including the broad research topics listed below. These research projects have been funded by IBM, IBM T. J. Watson Research Lab (New York), IBM Zurich Research Lab (Zurich), IBM Haifa Research Lab (Haifa), TUBITAK (The Scientific and Technological Research Council of Turkey), TUBA (Turkish Academy of Sciences), Turk Telekom, Avea, Koc University, Bilkent University, University of Illinois at Urbana Champaign (UIUC) and many other industrial programs and projects.

Several research opportunities and positions are available. If you are an undergraduate student or a prospective graduate student with an interest or a strong background in Mathematics, Probability and Signal Processing, and curious about exploring the world of research in the areas of Digital Signal Processing, NLP, Deep Learning, Machine Learning and Computer Vision, please send Dr. Kozat an e-mail stating your interest. Benefits include Full Tuition Waiver, Salary, Computing resources (desktops, laptops, ipads), Funding for both international and national conferences and other benefits. For more information on the current and past research projects, current research group members (and their publications), alumni (and their current positions in US), other technical activities, open positions and announcements, please directly contact with Professor Serdar Kozat. email: kozat@bilkent.edu.tr, office: Eng 309, Bilkent.

ACTIVE RESEARCH TOPICS

Context-aware Anomaly Detection System Based on Big Data Collected from Many Sources

Recent developments in information and detection technologies, and increasing use of intelligent mobile devices and internet have bolstered the capacity and capabilities of data acquisition systems beyond expectation. Today, many sources of information from shares on social networks to blogs, from intelligent device activities to security camera recordings are easily accessible. In our project, we will design state-aware anomaly detection and event prediction systems based on data from diverse sources acquired rapidly under unstationary conditions and quality. The problem of anomaly (or irregularity) detection and subsequent event prediction is a critical topic of national importance, particularly due to increasing border violations and cyber attacks in recent years. However, traditional techniques show insufficient performance in real-life applications.

In this project, we design novel algorithms that process text on social media, usage statistics of mobile devices and surveillance camera recordings in real time, and subsequently leverage these data for context-dependent anomaly detection and event prediction. The main aims of the project are: 1) Design of techniques that will scan text in Turkish on social media platforms, and that will extract text features for event detection and prediction; 2) Design of systems that will extract visual features from surveillance camera recordings using deep-learning methods; 3) Design of techniques that will process communication data as well as user information (such as location and time) from mobile devices to perform anomaly detection and event prediction; 4) Design of systems that will perform context-dependent processing and interpretation of data acquired at various dimensions, times and quality from written, audio and visual sources, as well as extracted features; 5) Development of a theoretical foundation for anomaly detection in big data applications.

Please refer to Context-aware Anomaly Detection System Based on Big Data Collected from Many Sources for more information.

Machine Learning for Big Data Signal Processing

Due to outstanding developments in sensor technologies, wide spread usage of smart phones and Internet, we now have the opportunity and capability to gather huge amounts of data in different real life signal processing applications (which was not possible in the past). Efficient and effective processing of this big data can significantly improve the performance of many signal processing algorithms. However, this big data has dimensions and volumes unseen before in signal processing problems, comes in different varieties and its quality, quantity and statistics rapidly change in time and among elements. To accommodate these problems, the big data should be adaptively processed by signal processing methods since the adaptive methods (1) can process the data online, i.e., instantly, without any storage requirement, (2) can constantly adapt to the changing statistics or quality of the data, hence can be robust and prone to variations and uncertainties.

In this project, in order to effectively process the big data, we aim to develop novel adaptive signal processing algorithms specifically tuned to effectively operate on large dimensional data and construct the big data adaptive signal processing framework, as the first time, by defining new cost measures and design methodologies.

Please refer to Machine Learning for Big Data Signal Processing for more information.

Please refer to Big Data Adaptive Signal Processing Algorithms, TUBITAK 113E517 for more information on several different projects and demos.

Distributed Processing and Smart Grid

Recently, we have experienced a dramatic growth in the capabilities of sensors and distributed sensor networks. Due to advances in device technologies, we now have sensors that can, not only collect data, but also process and communicate it among neighbors. The rapid increase in the processing power and decline in device dimensions of these sensors opened a wide range of new application areas for distributed processing, including mobile device networks, robot swarms and wearable electronics. We can tremendously increase performance using the collective intelligence and processing power of these advance units that can gather, process and communicate data, replacing the traditional sensors that only communicate observations to a central processing unit. There exists important theoretical and practical challenges to effectively use adaptive algorithms in the state-of-the-art distributed networks under real life conditions. The communication and energy consumption constraints, rapidly changing topologies and application requirements impede realistic usage of adaptive signal processing techniques. We aim to (1) develop, as the first time, novel distributed adaptive processing algorithms specifically designed to effectively operate under real life communication constraints, (2) demonstrate the effectiveness and efficiency of these approaches in different applications and prove these results mathematically, (3) provide algorithms with security guarantees in order to work under strict military and sensitive commercial specifications, and finally (4) construct a practical distributed adaptive processing framework by defining new cost measures and design methodologies that include realistic communication and power constraints.

Please refer to Distributed Processing and Smart Grid for more information.

Fundamentals of Deterministic Signal Processing

Signal processing field is by far considered as the power horse and the deriving force behind all recent advances from telecommunications, Internet, green communications to smart cities. However, there exist significant practical and theoretical difficulties to apply signal processing methods to these newly emerging real life systems since these systems show high degrees of non-stationarity, unpredictability and in many cases even chaotic, as an example, consider the uncontrollable Internet. This forces us to fundamentally change our perspective on how we design signal processing methods and introduce a completely radical approach to signal processing. Deterministic signal processing is envisioned to mitigate these challenges by removing all unrealistic assumptions on real life systems and provide algorithms that in a strong sense guaranteed to work and to dramatically improve performance.

This project is currently funded by IBM T.J. Watson Research Lab (New York), IBM Haifa Lab (Haifa), IBM Zurich Lab (Zurich).

Please refer to Fundamentals of Deterministic Signal Processing for more information.

Real Time Traffic Management

Since traffic congestion has drastically escalated due to the rapid growth in the number of vehicles, there is an extensive need for reliable and robust road traffic estimation and prediction systems to mitigate current or near-future congestion. Moreover, the significant increase in the cellular network coverage, highly sophisticated positioning systems (such as GPS) and the increased market penetration of cellular devices suggest the use of mobile networks to estimate and then predict the road traffic conditions.

In this project, we aim to leverage data collected through cellular devices in order to devise the next generation road traffic estimation and prediction methods. Along with publications, the final product will be patented and used in real life systems.

This project is currently funded by IBM T.J. Watson Research Lab (New York) and Avea Labs A. S.

Please refer to Real Time Traffic Management. for more information.