Plenary Speakers

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Dr. Hacer YALIM KELEŞ

Dr. Hacer YALIM KELEŞ received her B.S., M.S. and Ph.D. degrees in Computer Engineering from Middle East Technical University, Turkey, in 2002, 2005 and 2010, respectively. Her Ph.D. Thesis received the Thesis of the Year award by Middle East Technical University Prof. Dr. Mustafa Parlar Education and Research Foundation in 2010. From 2000 to 2007 she worked as a researcher and senior researcher at The Scientific and Technological Research Council of Turkey. During the years at TUBITAK, she primarily worked on different pattern recognition problems using multimedia data including audio and video. In 2010, she received an R&D grant from Ministry of Industry and Trade of Turkey and established her R&D company. Her follow-up project SOYA is funded by TUBITAK in 2011 and later awarded by TUBITAK as one of the best venture projects and sent to Silicon Valley for investment opportunities. She is the first woman who took this grant in Turkey. She is currently working as an Assistant Professor in the Department of Computer Engineering at Ankara University. Her research interests lie predominantly in the areas of computer vision and machine learning, particularly in deep learning. She is also interested in optimization of computational problems using GPUs.

Recent Trends In Machine Learning and Computer Vision

There is a tremendous acceleration in the Machine Learning researches in the last decade. A transformation took place from the utilization of simple human engineered features in linear algorithms to more structured and complex neural network architectures that we refer to as Deep Neural Networks. Although the mathematical models for neural networks exist since 60s, the real power of these models came to the light as a result of three main factors: (1) the improvements in the representative models, (2) massive increase in the computational power, (3) big data. We now observe the impact of deep learning in many areas of our everyday life such as computer vision, speech recognition, language understanding, recommendation systems, medical image analysis etc. In this talk, I will briefly present the important milestones in the computer vision research and how it is transformed with the deep learning methods; together with the recent research directions.


 

Dr. Semra Gündüç

Semra Gündüç graduated from Hacettepe University Physics Engineering Department where she studied Phase Transition of Statistical Systems, Modelling and Simulation of real-world systems, Quantum Entanglement, Social System Simulation and Complex Networks. She has been working as an Associate Professor in Ankara University Computer Engineering Department giving lectures on Computer Programming, Artificial Intelligence, Computer Simulation and Modelling.

The Structure of Complex Networks

We live in a world of connected units. All biological systems consist of connected units of various size, shape and complexity. Common examples of biological networks start with the biological brain, the very existence of all biological structures such as plants and all living creatures are examples of very complex structures of connected units. Roads, distribution of energy, hierarchical structures of social systems, internet, social networks are all seemingly different but mathematically similar structures. All such systems consist of simple connected units which through the connectivity behave as a large complex system. Such structures are very common and they are named as complex networks. Recently, similarities between such diverse structures became apparent and scientists from a wide variety of disciplines such as mathematicians, computer scientists, biologists, economists and physicists are interested on putting forward the mathematical structure of the complex systems. In this talk, the basic mathematical structure of complex networks will be discussed.


 

Dr. Nadia Kanwal

Nadia Kanwal received her M.Sc. and PhD degrees in Computer Science from the University of Essex, Essex, U.K., in 2009 and 2013 respectively. Presently she is working as Assistant Professor at Lahore College for Women University which she joined in 2001. Her research interests include Machine Learning, Computer Vision and Performance characterization of algorithms. Furthermore, she has published more than 20 Journal articles proposing new techniques for tracking, virtual reality, and navigation applications. Her significant contributions include the development of new descriptors for matching low-level image features. Moreover, a system for the navigation of blind person is also to her credit, the objective of which was to use low-cost sensors for efficient and accurate results. 

Currently, she is applying artificial intelligence techniques in helping medical experts for efficient and early diagnosis of diseases. Ms. Kanwal remained a student member of the IEEE Computer Society, the Institution of Engineering and Technology, and the British Machine Vision Association and has been actively involved in reviewing for conferences and reputed computing journals including IEEE Transaction.

Bridging the Gap between Human Expert and Machine Intelligence for Medical Diagnosis

Machine learning is playing pivotal role in developing automatic diagnostic solutions. For which, the focus remained to assist human experts in standardizing diagnosis of various diseases. However, differences in diagnostic results presented in the literature reflect the importance of general process development and refinement to link diseases with corresponding manifestations. This is only possible if the patients’ profile along with current symptoms becomes part of diagnostic process and contribute towards general rules development. This paper discusses the gaps between human experts and machine intelligence that need to be filled for a better development of self-learning medical consultant software. Furthermore, artificial intelligence based models can become virtual health worker at remote areas and can be used to collect enough data to predict unknown medical problems.                                   

Dr. Sadia Murawwat

Sadia Murawwat received her B.Sc Electrical Engineering from University of Engineering and Technology Lahore, Pakistan. She has done her Masters of Engineering from ASIAN INSTITUTE OF TECHNOLOGY, Thailand and PhD in Information and Communication Engineering from BEIJING INSTITUTE OF TECHNOLOGY China. Recently she is serving as Associate Professor in Electrical Engineering Department, Lahore College for Women University, Lahore, PAKISTAN. Her research interests include Wireless Communication (Tele-Traffic behavior, Switching Process, Handover Optimization), Broadband Communication (WiMAX, Heterogeneous Networks), Intelligent handover methods & optimizing MAHO protocol for heterogeneous networks.

Recognation of Technology Stress: Mobile Phones(MP)

Mobile Technology is all around us. As per International Telecommunication Union, there are 4.92 billion global mobile phone subscribers at present. Mobile phones have become so ubiquitous in our culture that the realization of social aspects of this mobile communication is also imparted. Youth are at the fore front of this broadband adoption. Smart phones, PDAs and other similar devices facilitate the user in diversified ways to communicate and enjoy broadband services with a touch of finger. This talk aims at the recognition of technology growth and related stress element. No doubt, it has revolutionized our life by eliminating so many devices around us to a single platform. However, research shows that there is a high correlation among Mobile phone usage including (calls, short message, downloading multimedia applications, location identification and on/off screen patterns) and stress components like sleep disturbances, variation in mood, tiredness, general health, caffeinated beverage intake and electronics usage. The higher reported stress level was related to higher activity level as per correlation analysis.