Transition to Third Child - A Machine Learning Perspective [02.03.2018 - 10:00-11:00]


Turkey is approaching the final stage of its fertility transition although its period fertility rate has stagnated around 2.2 per woman in the last decade. The transition from second to third birth is recognized as a critical landmark in completing fertility transition. In particular, we want to investigate the fundamental characteristics of women in such a transition. In this talk, I will show how we employ decision tree modeling, which is a well-known method in machine learning, on the data set of 2013 Turkey Demographic and Health Survey so as to identify mutually exclusive sub-groups of pioneers and laggards in the fertility transition process of Turkey. Overall results of the decision three models suggest that the third-birth propensity in Turkey differs remarkably on the basis of characteristics of women.

Burkay Genç
Hacettepe University

Burkay Genç got his BSc degree from Bilkent University Industrial Engineering Department, his MSc degree from Bilkent University Computer Engineering Department and his PhD from School of Computer Science, University of Waterloo. Before Hacettepe, he worked in Izmir University of Economics as an assistant professor of computer science and as the assistant to the provost; in TED University as an assistant professor of computer science and as the deputy head of the Computer Engineering Department. Now, he works in the Policy and Strategy Studies programme in the Institute of Population Studies. His research focuses on computational geometry, data visualization and data mining. He also teaches in Computer Animation and Game Technologies programme in the Institute of Informatics. He is proudly a learner, a gamer and a Linuxer.

Date: 02.03.2018 - 10:00-11:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room


ACM Hacettepe'nin Intel ile birlikte düzenlediği AI Workshop 21 Aralık'ta Hacettepe Üniversitesi, Teknokent 1. Arge Binası Konferans Salonunda gerçekleşecek. Etkinlik, makine öğrenmesine bir giriş yapmanızı sağlayacak ve sizi yeni teknolojilerle tanıştıracak.

Katılmak için linkteki formu doldurmanız yeterli :

Broşür 1

Broşür 2

Date: 21.12.2017

Location:Hacettepe Üniversitesi, Teknokent 1. Arge Binası Konferans Salonu

Robotics as a testbed for Computer Science [08.12.2017 - 10:00-11:00]


Robotics is often more associated with the Mechanical and Electrical Engineering disciplines than Computer Science and Engineering (CS), due to historical reasons. However, advances in robotics within the last decade pushes CS to the forefront of this research field.

In this talk, first, I will briefly describe some of the major challenges in robotics that act as realistic testbeds for CS research. I will argue that these challenges, influence and shape the research from a purely CS perspective. I will present results about the perception, learning and use of affordances and their linking with natural language as a case study. Finally, I will conclude by briefly talking about our new projects, and share the problems that we’ve started to tackle.

Erol Şahin
Bilkent University

Erol Şahin received his Ph.D. degree in Cognitive and Neural Systems from Boston University, his M.S. degree in Computer Engineering from METU, and his B.S. degree in Electrical and Electronics Engineering from Bilkent University. He founded the KOVAN Research Lab. at METU Computer Engineering Department in 2002. His research in swarm robotics, cognitive and developmental robotics was awarded 6.5M TL funding from TUBITAK and European Union. By being chosen for the sixth place from 31 projects, he was awarded a 250K Euros worth 53-DOF iCub humanoid robot platform by RobotCub project to support his research in cognitive robotics. He visited the Robotics Institute at Carnegie Mellon University between 2013-2015 as a Marie Curie International Outgoing Fellow. He is the editor of special editions of three journals and five conference/workshop proceedings. He took part as the technical program/area chair in the conferences ANTS'2010, AAMAS'2009 and ICDL/Epirob'2014 and worked as co-chair in the 3. Turkey Robotic Sciences Conference (TORK’2016). He is serving as an Associate Editor for the Adaptive Behavior journal since 2008 and as an Editorial Board member of the Swarm Intelligence journal since 2007.

Date: 08.12.2017 - 10:00-11:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Energy Efficient Architecture for Graph Analytics Accelerators [24.11.2017 - 11:00-12:00]


Specialized hardware accelerators can significantly improve the performance and power efficiency of computing systems. In this presentation, we will focus on hardware accelerators for graph analytics applications and propose a configurable architecture template that is specifically optimized for iterative vertex-centric graph applications with irregular access patterns and asymmetric convergence. The proposed architecture addresses the limitations of the existing multi-core CPU and GPU architectures for these types of applications.

The SystemC-based template we provide can be customized easily for different vertex-centric applications by inserting application-level data structures and functions. After that, a cycle-accurate simulator and RTL can be generated to model the target hardware accelerators. Our experiments showed that the hardware accelerators generated by our template can outperform a 24 core high end server CPU system by up to 3x in terms of performance with up to 65x better power consumption and significantly smaller area.

Özcan Öztürk
Bilkent University

Dr. Öztürk has been on the faculty at Bilkent since 2008 where he currently is an Associate Professor in the Department of Computer Engineering. His research interests are in the areas of manycore accelerators, cloud computing, GPU computing, on-chip multiprocessing, computer architecture, heterogeneous architectures, and compiler optimizations. Prior to joining Bilkent in Spring 2008, he worked as a software optimization engineer in Cellular and Handheld Group at Intel and Marvell. Dr. Ozturk's research has been recognized by Fulbright Senior Scholar Award in 2014, Turk Telekom Research Collaboration Award in 2012, IBM Faculty Award in 2009, European Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC) Paper Award in 2009, and ICPADS Best Paper Award in 2006. He is a member of GSRC, HiPEAC, IEEE, and ACM and is currently serving as editor/reviewer on leading IEEE, ACM, and various other journals. Dr. Öztürk received his Ph.D. degree in computer science and engineering from the Pennsylvania State University, the M.S. degree in computer engineering from University Of Florida, and received the B.Sc. degree in computer engineering from Bogazici University, Turkey.

Date: 24.11.2017 - 11:00-12:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room

A Hybrid Assessment Approach for Medical Device Development Companies [23.11.2017 - 10:00-11:00]


Medical device software development organisations are bound by regulatory requirements and constraints to ensure that developed medical devices will not harm patients. Medical devices have to be treated as complete systems and be evaluated in this manner. Medical device manufacturers in the US as well as in the EU must satisfy the associated regulatory demands of the region that the device will be marketed in. A variety of international standards have to be implemented in order to ensure regulatory requirements for a medical device. Instead of manufacturers having to ensure compliance to various regulatory standards individually, the DkIT researchers previously developed a medical device software process assessment framework called MDevSPICE® that integrates the regulatory requirements from all the relevant medical device software standards. MDevSPICE® was developed in a manner that suits plan-driven software development. In order to improve the usability of MDevSPICE in agile settings, a hybrid assessment approach has been developed.

The hybrid assessment approach combines the MDevSPICE® based process assessment method with steps for prioritization of improvement needs through value stream mapping, and enabling process improvement through the use of the KATA technique. This approach integrates agile methods into the medical device software development process whilst adhering to the requirements of the regulatory standards. The purpose of this seminar is to describe the approach and its implementation within four organisations that develop software in line with medical device regulations in Ireland.

Özden Özcan-Top
(Dundalk Institute of Technology, Ireland)

Dr. Özden Özcan-Top is a post-doctoral researcher at Dundalk Institute of Technology (DkIT), Ireland. Her primary research area is improving software quality and efficiency in the safety critical domain, specifically in medical device software development through the integration of agile software development methods. She received her M.Sc. and PhD degrees from Information Systems (IS) Department at Informatics Institute of Middle East Technical University (METU) in 2008 and 2015 respectively. She received her bachelor’s degree from Industrial Engineering Department at Yıldız Technical University in 2005. She worked as a research assistant at IS Department, METU for 4 years. Following that she worked at Fujitsu Technology Solutions Company as a quality specialist for 2,5 years and obtained hands-on practice on the topics that she had been performing research. She was involved in three TÜBİTAK projects as a researcher and published more than 20 artifacts. Her other research interests include software process assessment/improvement, software project management and software measurement and estimation.

Date: 23.11.2017 - 10:00-11:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Global Path Planning Strategies for Crowd Navigation [27.10.2017 - 10:00-11:00]


There are applications of crowd simulation systems in entertainment industry fields such as cinema and video games, and moreover in fields such as emergency and evacuation simulations in architectural structures, military simulation systems and urban planning. In the context of crowd navigation, the local navigation methods consider each agent in the crowd individually and plan their short term movements, while the global navigation methods approach the crowd as a whole. The hybrid navigation methods, on the other hand, aim to combine the successful parts of these two types that have been developed. Although hybrid navigation methods are more costly as they need to do more calculations, provided that the cost can be kept at a reasonable degree, they can produce much more successful results than local and global methods. In this talk I will explain two hybrid methods I have developed as a part of my PhD thesis, give a brief comparison of them and discuss their contributions on local navigation.

Cumhur Yiğit Özcan
Hacettepe University

Dr. Cumhur Yiğit Özcan is a research assistant at the Department of Computer Engineering, Hacettepe University. He also received his bachelor's (2010) and PhD (2017) degree from the same department. His research interests include topics such as computer graphics, AI for video games and crowd simulations. His PhD thesis and recent publications specifically focus on navigation and path planning in crowd simulations.

Date: 27.10.2017 - 10:00-11:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Keyphrase Extraction and Topic Segmentation [13.10.2017 - 10:00-11:00]


When we express some idea or story, it is inevitable to use words that are semantically related to each other. When this phenomenon is exploited from the aspect of words in the language, it is possible to infer the level of semantic relationship between words by observing their distribution and use in discourse. From the aspect of discourse, it is possible to model the structure of the document by observing the changes in the lexical cohesion in order to attack high-level natural language processing tasks. In this talk lexical cohesion is investigated from both of these aspects by first building methods for measuring semantic relatedness of word pairs and then using these methods in the tasks of topic segmentation and keyphrase extraction.

Gönenç Ercan
Hacettepe University

Gonenc Ercan is a faculty in Informatics Institute of Hacettepe University. Prior to joining Hacettepe University, he has served in Computer Engineering Department of Cankaya University as an Assistant Professor. He received his Ph.D. and M.Sc. degrees from Bilkent University and B.Sc. from Eastern Mediterranean Universtiy.

His research interests are Text Mining, Natural language processing and Information Retrieval focusing on the use of Lexical Semantics in high-level tasks. He has published articles in Information Science journals and conferences.

Date: 13.10.2017 - 10:00-11:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Energy Efficient Smart Programming Languages and Data Centers [29.09.2017 - 10:00-11:00]


As the size of the data centers and supercomputers keeps growing with the computational demand, so too does their power consumption. Current systems consume tens of megawatts of power daily, leading to millions of dollars in energy bills, significant power strains on local, state, or regional energy grid systems, and the environmental impact on natural resources that provide the power for the system. In the near future, as the computational system scales grow, the power costs might increase to hundreds of megawatts daily. This talk will explain the infrastructure of data centers and novel energy efficiency techniques using smart and dynamic programming languages that have been analyzed on top supercomputers in the world using thousands of processors.

Bilge Acun
Data Centric Systems team at IBM T.J. Watson Research Center in New York

Bilge Acun is a Research Scientist at the Data Centric Systems team at IBM T.J. Watson Research Center in New York. She received her Ph.D. from the Department of Computer Science at University of Illinois at Urbana-Champaign (UIUC) and received a B.S. from Bilkent University with magna cum laude.

Bilge's research interests are parallel and distributed computing, green computing, and high performance computing. Her research has been recognized as a cover-featured article in the prestigious IEEE Computer, as well as at top conferences in the area of High Performance Computing. She is the lead inventor of three patent-pending technologies.

Date: 29.09.2017 - 10:00-11:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Legal Information Retrieval: Unification of Legal Precedents [7 Nisan 2017 - 10:00-10:50]


Legal professionals are ethically obligated to be reasonably informed on legal documents related with the jurisdictions. Digitization of legal documents such as legislations, case laws, and scholarly work made it possible to search for relevant documents. Indeed, legal documents are an important source of data that has different characteristics compared with the typical textual documents. The jargon, polysemes and frequent changes in the domain make it harder to directly apply traditional information retrieval techniques. Previous applied techniques (i.e., keyword base search (full text search), classification and natural language processing with the utilization of legal ontologies) have been applied in the legal domain to achieve high recall (increasing the number of relevant documents) and high precision (reducing the number of irrelevant documents) but due to the nature of the domain failing to retrieve an important or precedential case is yet a serious problem. As a case study, unification of legal precedents is introduced. The similarity and relatedness of the documents are modeled as a network. It has been shown that maximal cliques in the network presents a promising grouping among the legal precedents.

Engin Demir
Çankaya University

Engin Demir is currently an assistant professor in the Computer Engineering Department of Çankaya University. He has received his B.S. in Computer Engineering and Information Science, M.S. and Ph.D. degrees in Computer Engineering from Bilkent University. Before joining Çankaya University, he was an assistant professor in the University of Turkish Aeronautical Association (THKU), served as the head of department between 2012 and 2016. He had worked as a postdoctoral researcher at the Ohio State University, USA (2009-2011) and the University of Oxford, UK (2011-2012). He has worked in several national and international research projects, funded by TUBITAK, NSF, EPSRC, Microsoft Research. His research interests include information retrieval, social network analysis, data mining, spatial network databases, algorithmic engineering.

Date: 07.04.2017 - 10:00-10:50

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Machine Learning in Adverse Settings [29.03.2017 - 10:00-10:50]


Machine learning and signal processing algorithms are finding use in diverse applications such as recommendation generation, surveillance and neural signal analysis. The data in these settings are typically voluminous and noisy. We propose computationally scalable novel algorithms for processing in adverse settings. Of special interest to us are the challenges of non-stationarity, abnormality and low SNR. We first introduce a highly dynamical online classification algorithm with strong mathematical guarantees regardless of source statistics. Our algorithm combines low-complexity methods to achieve superior modeling performance and adaptation to non-stationarity. We demonstrate the approach across several computer vision applications to object recognition, target tracking and anomaly detection for surveillance. Next, we address a problem in the domain of neural signal analysis that illustrates the challenge of interpreting signals with low SNR. Specifically, we attempt to identify the neural bases of naturalistic human face perception. Our proposed solution involves searching for statistical regularities in the data to find robust spatial, temporal as well as spectral features. These are combined through boosting to classify the stimuli as face or non-face in both dynamic and static conditions. Our results yield clues regarding the neural substrates of face processing in the human brain and also have implications for the applied areas of brain-machine interfaces and neurological diagnostics.

Huseyin Ozkan
Massachusetts Institute of Technology

Dr. Huseyin Ozkan is a postdoctoral research associate in Vision and Computational Neuroscience at Massachusetts Institute of Technology. He received his B.Sc. degrees in Electrical Engineering and Mathematics from Bogazici University; and his M.Sc. and Ph.D. degrees in Electrical Engineering from Boston University, and from Bilkent University, respectively. His research interests are in machine learning, signal processing, computer vision and computational neuroscience. He also worked full-time for ASELSAN and MERL (Mitsubishi Electric Research Laboratories) during his graduate studies, where he conducted research in computer vision for surveillance.

Date: 29.03.2017 - 10:00-10:50

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Physics-Based Biped Motion Control in Computer Animation [24.03.2017 - 10:00-10:50]


Kinematic character animation techniques offer easy-to-implement and performance-efficient ways of virtually resembling human motions in computer animation applications. However, the resulting kinematic motions are limited to only pre-designed scenarios and completely unaware of the interactions with the environment. In order to achieve perfect realism, character animation research headed to physics-based approaches, which define the character with physical body-parts and maintain an active control of the character by applying joint torques.

In this talk, I will first give a short overview of our recent research studies on physics-based character animation. Afterwards, I will focus on our physics-based walking controller approach for synthesizing virtual character motions that closely mimic the styles of given reference motions. While the state of the art purely physics-based approaches enable creating physically realistic motions, their results are mostly robotic without a human-like style. With this motivation, our study focuses on delivering both physically and stylistically realistic results in a performance-efficient and easy-to-implement way. Specifically, we emphasize on the importance of tracking intra-step center of mass velocity variations for style resembling and devise a novel strategy with the purpose of better velocity tracking. We demonstrate the capabilities of our approach under various environment conditions with different reference motions obtained by either motion capture or manual authoring.

Zümra Kavafoğlu
Hacettepe University

Dr. Zümra Kavafoğlu is a research assistant at Department of Mathematics, Hacettepe University and received her B.Sc(2007), M.Sc.(2010) and Ph.D.(2016) degrees from the same department. During her M.Sc. studies, she performed research on computational geometry and scientific visualization as a part of the TÜBİTAK project “Development of Mine Automation and Design Software”. She conducted an interdisciplinary Ph.D. research that focused on physics-based biped motion controllers, as a part of the TÜBİTAK project “Data Driven Virtual Human Animation”. From December 2014 to June 2015, she was a visiting scholar in Utrecht University(the Netherlands), Department of Information and Computing Sciences with TÜBİTAK International Doctoral Research Fellowship. Her current research interests include physics-based and data-driven character animation, especially building general controllers with more efficient inverse dynamics approaches.

Date: 24.03.2017 - 10:00-10:50

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Search Result Diversification for the Web and Social Media [10.03.2017 - 11:00-11:30]


I will begin with a short potpourri of our most recent research, emphasis being on the topics related to the web search engines and social Web. Then, I will focus on the web search result diversification problem and present our novel contributions in two directions. First, I will introduce a set of new explicit diversification strategies based on the score(-based) and rank(-based) aggregation methods. Next, I will present how these diversification methods perform in the context of Tweet search, and how we improve them using word embeddings.

Ismail Sengor Altingovde

Ismail Sengor Altingovde is currently an associate professor in the Computer Engineering Department of Middle East Technical University (METU) at Ankara, Turkey. He has received his B.S., M.S. and Ph.D. degrees, all in Computer Science, from Bilkent University (Turkey). Before joining METU, he has worked as a postdoctoral researcher at Bilkent University and L3S Research Center in Hannover, Germany. He has worked in several national and international research projects, funded by the Scientific and Technological Research Council of Turkey and the European Union Sixth and Seventh Framework Programs. His research interests include web information retrieval with a particular focus on search efficiency, social web and web databases. He has published over 50 papers in prestigious journals and top-tier conferences. He is one of the recipients of Yahoo! Faculty Research and Engagement Program (FREP) award in 2013 and Young Scientist Award (GEBIP) of Turkish Science Academy (TUBA) in 2016.

Date: 10.03.2017 - 11:00-11:30

Location: Hacettepe University, Computer Engineering Building, Seminar Room

High Fidelity Sky Models [03.2017 - 10:00-11:00]


Light sources are an important part of physically-based rendering when accurate imagery is required. High-fidelity models of sky illumination are essential when virtual environments are illuminated via the sky as is commonplace in most outdoor scenarios. The complex nature of sky lighting makes it difficult to accurately model real life skies. The current solutions to sky illumination can be analytically based and are computationally expensive for complex models, or based on captured data. Such captured data is impractical to capture and difficult to use due to temporally inconsistencies in the captured content. This work enhances the state-of-the-art in sky lighting by addressing these problems via novel sky illumination methods that are accurate, practical and flexible. This work presents two novel sky illumination methods where; the first of which focuses on clear sky lighting and the second one deals with illumination from cloudy skies.

Pınar Satılmış (Hacettepe University)

Dr. Pınar Satılmış is a research assistant at the Department of Mathematics, Hacettepe University. She has a BSc with a first degree and a MSc from Department of Mathematics, Hacettepe University. She did her PhD in Visualisation Group (Warwick Manufacturing Group), University of Warwick, 2017. Her research interests are Machine Learning for Graphics Applications, Light Transport, Optimization Techniques, Image Processing, High Dynamic Range Imaging.

Date: 03.03.2017 - 10:00-11:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Modeling, Analysis and Autopilot Design for UAVs [24.02.2017 - 10:00-11:00]


In this talk, Dr. Coşku Kasnakoğlu will first give information about his Unmanned Aerial Vehicles (UAV) Laboratory and its research efforts. The laboratory conducts work on the modeling, analysis and autopilot design for UAVs. The systems built are verified using computer simulations, hardware-in-the-loop tests and actual flights. Future research directions including intelligent methods and fault-tolerant approaches will also be discussed.

Coşku Kasnakoğlu

Dr. Coşku Kasnakoğlu obtained B.S. degrees from the Department of Computer Engineering and the Department of Electrical and Electronics Engineering and at the Middle East Technical University (METU), Ankara, Turkey in 2000. He obtained his M.S. and Ph.D. degrees from the Department of Electrical and Computer Engineering at the Ohio State University (OSU), Columbus, Ohio, USA in 2003 and 2007. From 1996-2000 he worked as a researcher at TUBİTAK Information Technologies and Electronics Research Institute (BILTEN). From 2000-2007 he was as a graduate researcher at OSU. In 2008 he joined TOBB University of Economics and Technology, where he is currently an associate professor in the Department of Electrical and Electronics Engineering. Dr. Kasnakoglu's current research interests include dynamical modeling, analysis and autopilot design for aerospace systems with applications to unmanned aerial vehicles (UAVs). He is a member of IEEE, AIAA and has published numerous scientific papers in respected journals and conferences

Date: 24.02.2017 - 10:00-11:00

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Learning Actionable Representations from Large-Scale Visual Information [29.12.2016 - 10:30]


Humans created a tremendous value by collecting and organizing all their knowledge in publicly accessible forms, as in Wikipedia and YouTube. The availability of such large knowledge bases not only changed the way we learn, it also changed how we design artificial intelligence algorithms. Recently, propelled by the available data and expressive models, many successful computer vision and natural language processing algorithms have emerged. However, we did not see a similar shift in robotics. Our robots are still having trouble recognizing basic objects, detecting humans, and even performing simple tasks.
We believe the main limitation is, robots need to handle many challenges like scarcity of the supervision and the shift between different modalities and domains. We show that the common solution to all these problems is understanding the latent structure of the data. We propose machine learning algorithms, which can learn latent semantic structure with no supervision over multiple domains and modalities.

Ozan Sener
Stanford University

Ozan Sener is a PhD candidate at Stanford University co-advised by Ashutosh Saxena and Silvio Savarese. He earned his BS. and MS degrees from the Middle East Technical University. He later joined Cornell University as a PhD student. He later moved to Stanford University following his advisor's move. His research interests include machine learning, computer vision and robotics. His recent works involve transfer learning among domains and modalities, unsupervised learning using generative models, and exploring the problems relating functionality of objects to their shape.

Date: 29.12.2016 - 10:30

Location: Hacettepe University, Computer Engineering Building, Seminar Room

Oyun Sektöründe Girişimcilik Semineri

IGDA Hacettepe Oyun Geliştiriciler Topluluğu'nun düzenlemiş olduğu GameDev101 seminerler dizisinin ilk etkinliği olan "Oyun Sektöründe Girişimcilik" semineri 22 Aralık saat 13:00'da seminer salonunda gerçekleşecektir.

Alictus şirketinin CEO’su A. Emre Taş’ın katılımıyla gerçekleşecek olan seminerde amaç, oyun sektöründe çalışmak veya kendi işini kurmak isteyen öğrenciler için yol göstermektir.

Etkinlik linki:

BBM601-701 Lisansüstü Seminer Dersleri - Sunum Takvimi
ACM Hacettepe Git WorkShop Etkinliği

ACM Hacettepe'nin düzenlediği Git Workshop'ı 9 Aralıkta 15:30-18:30 arası Bilgisayar Mühendisliği bölümünün Seminer Salonunda yapılacaktır. İlgilenen herkesin katılımını bekliyoruz.

Git; özellikle büyük çaplı yazılım geliştirme projelerinde kullanılan en büyük sürüm kontrol ve kaynak kod yönetim sistemidir. Projelerin sürüm kontrol aşamasındaki bütün tarihçesini barındıran bir depo görevi gören Git, aynı zamanda bir özgür yazılımdır.

Etkinlik linki:

Bölümümüzde yürütülen araştırma konuları ve öğretim üyeleri çalışmaları tanıtım sunumları

Sevgili öğrenciler,

Bölümümüzde yürütülen araştırma konularını ve öğretim üyelerimizin çalışmalarını tanıtabilmek amacıyla 9 ve 16 Aralık Cuma günleri sunumlar yapılacaktır. Program aşağıda bulunmaktadır. Şu an yüksek lisans ve doktora programlarına kayıtlı ve henüz tez danışman ataması yapılmamış tüm öğrencilerin bu sunumlara katılmaları zorunludur. Gelecekte yüksek lisansa devam etmeyi düşünen lisans öğrencilerimize ise katılmaları önemle tavsiye edilir.

December 2016
Saat Fri 9 Fri 16
09:30-09:45 Gonenc Ercan Suleyman Tosun
09:45-10:00 Burcu Can Mehmet Önder Efe
10:00-10:15 Harun ARTUNER Erkut Erdem
10:15-10:30 Kayhan İmre Aykut Erdem
10:30-10:45 Fuat Akal Nazli Ikizler
10:45-11:00 Ahmet Burak Can Pinar Duygulu
11:00-11:15 Adnan Ozsoy Murat Aydos
11:15-11:30 Ufuk Çelikcan Mehmet Koseoglu
11:30-11:45 Vahid Garousi Sevil Sen
11:45-12:00 Ayça Tarhan Lale Ozkahya
12:00-12:15 Fuat Akal

Peak Games şirketinin gün boyu sürecek bölüm ziyareti ve seminerleri [18.11.2016 - 11:00]


11.00 - 12.30 Game Development with Unity - Hakan Sağlam - Seminer Salonu

12.30 - 13.30 Öğle Yemeği (Peak Games sponsorluğunda)

14.00 - 16.00 Meet with Peak Games' Technical Leaders - Seminer Salonu

6.30 - 17.30 A glance at the Big Data Infrastructure of Peak Games - Serdar Şahin - Yıldız Amfi M10

Date: 18.11.2016 - 11:00-17:30

Location: Seminer Salonu, Bilgisayar Mühendisliği Bölümü / Yıldız Amfi

Identification of Legged Locomotion via Mechanics-Based and Data-Driven Models [11.11.2016 - 10:00]


The ultimate promise of legged locomotion on complex terrain led to both the construction of many legged robot platforms as well as mathematical models to describe their underlying dynamics. Motivated by the problems in the area, this study focuses on the identification of legged locomotor systems, in particular robotic legged locomotion via mechanics-based and data-driven models. In the first part, we worked on developing mechanics-based analytical models that can accurately represent center of mass trajectories of legged robots. We present systematic experiments on a well-instrumented monopedal robot platform to assess the model performance with physical robot data. On the other hand, representational power of such mechanics-based models are inevitably limited due to the nonlinear and complex nature of biological systems. To this end, we adopt a data-driven approach with the goal of furnishing an input—output representation of legged locomotor systems. Our method is based on formulating the identification problem of legged locomotion as a linear time-periodic system around a stable limit cycle and then develop frequency domain system identification techniques to obtain a state-space representation.

İsmail UYANIK, PhD
University of Bilkent

İsmail Uyanık is a Ph.D. student in Electrical and Electronics Engineering Department of Bilkent University. He received his B.Sc. and M.Sc. degrees from the same department in 2009 and 2011, respectively. His research has been devoted to discovering the principles by which animals move in nature. Inspired by biomechanical observations, he worked on developing novel system identification methods for identifying legged locomotion, in particular legged robotic systems. He also developed different legged robot platforms to experimentally validate his research findings. In 2014, he received Aselsan Ph.D. fellowship which is given to only a few Ph.D. students nationwide each year. -- İsmail UYANIK, PhD

Date: 11.11.2016 - 10:00

Location: Seminer Salonu, Bilgisayar Mühendisliği Bölümü

Servise Yönelik Yazılım Mimarileri [09.11.2016 - 13:00]


Servise yönelik yaklaşım, servis tasarım ilkeleri, servise yönelik mimari standartları ve teknolojileri, İş süreci yönetimi ve servise yönelik mimari.

Yrd. Doç. Dr. Barış Özkan
Atilim Universitesi

Dr. Barış Özkan 10 yılı aşkın bir süredir yazılım ölçümü, yazılım maliyet modelleri, gereksinim mühendisliği, yazılım mühendisliği, iş süreçleri yönetimi, yazılım ürün ve hizmet kalitesi , BT yönetişimi, yönetimi ve denetimi konularında dersler vermekte ve araştırma çalışmaları yapmaktadır.

Date: 09.11.2016 - 13:00

Location: Seminer Salonu, Bilgisayar Mühendisliği Bölümü

Action Recognition and Prediction with Applications to Daily Living [29.11.2015]


With the recent technological developments, egocentric cameras have become a part of our lives. They are designed as tiny cameras that can be worn without disturbing the person. In order to investigate the possible information gain from egocentric cameras, we used a multiple camera setting, where both an egocentric and multiple static cameras exist. Our research showed that when fused correctly, using the information from different types of cameras increases the recognition accuracy of actions. The model we proposed for this task is also suitable for other multi-modal settings. To prove the generality of the proposed model we also tested it on a setting with multiple static cameras and showed state-of-the-art results. Our model learns the importance of each camera in recognizing the actions, and it can also be used to direct the scenes automatically. We created examples of automatically directed scenes to show the concept.

We also addressed the problem of improving people's lives in a preventive way using egocentric cameras. In our work preventive refers to the general notion of reminders that can possibly prevent people from making mistakes that can cause problems. For example, when people are leaving a room while the stove is on, they might be reminded to turn the stove off. We proposed a notification decision mechanism that reasons about interdependencies between actions, checks at every time step whether there is a missing action that should be completed before the ongoing one ends, calculates a cost for missing it, and uses this cost to make a notification decision. Such a notification system requires recognizing the past actions and predicting the ongoing action while segmenting the activity observed so far. For this purpose, we proposed a model that uses standard features and accomplishes these three tasks successfully. We showed promising results on the extremely challenging task of issuing correct and timely reminders on a new egocentric dataset.

Bilge Soran, PhD
University of Washington

Bilge Soran is a Post-Doctoral researcher at the University of Washington, Mechanical Engineering Department working on healthcare applications of computer vision. She earned a Ph.D. degree from the University of Washington, Computer Science and Engineering Department in 2015, under the supervision of Linda Shapiro and Ali Farhadi. Her thesis topic was "Action Recognition and Prediction with Applications to Medical Diagnosis and Daily Living". She holds a M.Sc. degree from the same department, where the thesis topic was Parcellation of Human Inferior Parietal Lobule Based on Diffusion MRI. She earned another M.Sc. degree from the Gebze Institute of Technology, in 2007, where the thesis topic was Event Driven Molecular Dynamics Simulation. Before that, she obtained her B.Sc. degree from the Middle East Technical University, in 2002. She worked in The Scientific and Technological Research Council of Turkey between 2003-2009, as a researcher and senior researcher on avionics and simulation systems. Before that she had 2 years of industry experience.

Action Perception in the Human Brain [11.11.2015]

Successfully perceiving and recognizing the actions of others is of utmost importance for the survival of many species. For humans, action perception is considered to support important higher order social skills, such as communication, intention understanding and empathy, some of which may be uniquely human. Over the last two decades, neurophysiological and neuroimaging studies in primates have identified a network of brain regions in occipito-temporal, parietal and premotor cortex that are associated with perception of actions, also known as the Action Observation Network. Despite growing body of literature, the functional properties and connectivity patterns of this network remain largely unknown.

One of the goals of my research is to address these general questions about functional properties and connectivity patterns with a specific focus on whether this system shows specificity for biological agents. To this end, we collaborated with a robotics lab, and manipulated the humanlikeness of agents that perform recognizable actions by varying visual appearance and movement kinematics. We then used a range of measurement modalities including cortical EEG oscillations, event-related brain potentials (ERPs), and functional magnetic resonance imaging (fMRI) together with a range of analytical techniques including pattern classification, representational similarity analysis (RSA), and dynamical causal modeling (DCM) to study the functional properties, temporal dynamics, and connectivity patterns of the Action Observation Network.

While our findings shed light whether the human brain shows specificity for biological agents, the interdisciplinary work with robotics also allowed us to address questions regarding human factors in artificial agent design in social robotics and human-robot interaction such as uncanny valley, which is concerned with what kind of robots we should design so that humans can easily accept them as social partners.

Burcu Aysen Urgen received her PhD (2015) in Cognitive Science from University of California, San Diego (UCSD) under the supervision of Professor Ayse P. Saygin; her BS degree in Computer Engineering and Information Science from Bilkent University, and her MS degree in Cognitive Science from Middle East Technical University. She is currently a post-doc in University of Parma, Italy with Professor Guy Orban. Her primary research interest is the neural mechanisms underlying visual perception of actions in the human brain. In her PhD, she investigated whether the human brain shows specificity for biological agents using humanoid robots by collaborating with Professor Hiroshi Ishiguro's lab (Osaka University, Japan), neuroimaging methods including fMRI and EEG, and machine learning techniques. Her interdisciplinary work has been published and presented at international and interdisciplinary journals and conferences. She was also one of the three awardees of the Interdisciplinary Scholar Award by UCSD with her interdisciplinary work between cognitive neuroscience and human-robot interaction.

-- Burcu Aysen Urgen, PhD

Quantum Cuts: A Quantum Mechanical Spectral Graph Partitioning Method for Salient Object Detection, Çağlar Aytekin, Tampere University. [06.10.2016]

Çağlar Aytekin, Department of Signal Processing, Tampere University of Technology

Başlık: Quantum Cuts : A Quantum Mechanical Spectral Graph Partitioning Method for Salient Object Detection

Tarih: October 6, 2016 Thursday

Yer: Semier Salonu, Bilgisayar Mühendisliği Bölümü

Saat: 15:00

Özet: Salient Object Detection can be defined as highlighting the objects that visually stand out from the rest of the environment. The growing interest in this topic can be explained by its many direct applications such as image and video coding, summarization and object tracking. Moreover, it can be used to reduce the complexity of higher level Computer Vision tasks by reducing their search space. This talk focuses on this important and emerging Computer Vision task and describes novel approaches and methods for salient object detection in images and videos.
First part of the talk is constituted of a direct application of ideas originally proposed for describing the wave nature of particles in Quantum Mechanics and expressed through Schrödinger's Equation, to salient object detection in images. Second part introduces a spectral graph based salient object detection method, namely Quantum Cuts. The proposed method adopts a novel approach to spectral graph partitioning by integrating quantum mechanical concepts to Spectral Graph Theory. The third part of the talk covers improvements and extensions on Quantum Cuts by analyzing the main factors that affect its performance in salient object detection. Finally future directions in salient object detection research will be discussed.

Bio: Çağlar Aytekin was born in Turkey in 1987. He received the B.Sc. degree from the Electrical and Electronics Engineering Department from Middle East Technical University, Ankara Turkey in 2008. He received the M.Sc. department with Signal Processing major from Middle East Technical University, Ankara, Turkey in 2011. He is currently a Ph.D student and researcher in Department of Signal Processing, Tampere University of Technology. He has published 16 papers in international conferences and journals. He has won the the ICPR 2014 Best Student Paper Award for his work in unsupervised salient object detection. His research interests are, visual saliency, semantic segmentation, quantum mechanics based computer vision methods and deep learning.

Human activity analysis, Selen Pehlivan, TED Üniversitesi [09.06.2016]

Selen Pehlivan, TED University

Title: Human activity analysis

Date: June 9, 2016 Thursday


Abstract: In this talk, I will summarize our work on human activity analysis. First, focusing on multiple camera views, I will describe our method for fusion of frame judgments. We have shown that when there are enough overlapping views to generate a volumetric reconstruction, our recognition performance is comparable with that produced by volumetric reconstructions. Then, I will shortly introduce our recent work on weakly-supervised spatial-temporal action localization.

In the second part of the talk, I will present our work on fine-grained categorization. We introduce a unified approach for classification and keypoint localization, targeting subcategories with wide pose variations and high intra-class similarities. We apply our method to identifying instances of birds.

Finally, I will present our recent effort on action understanding to investigate the representational properties of the Action Observation Network using fMRI and modeling. In our study, we used powerful computational tools from computer vision as well as attribute-based semantic models to represent the videos consisting of natural actions, and linked those models to the brain responses (fMRI) using representational similarity analysis.

Bio: Selen Pehlivan holds a B.S. (Bilkent University, 2004) and an M.S. (Koç University, 2006) in Computer Engineering. Between 2004 and 2006, she was a member of the 3DTV EU Project. She got her Ph.D. degree (Bilkent University, 2012) also in Computer Engineering. From August 2009 to February 2011, she was a visiting scholar in Department of Computer Science at University of Illinois at Urbana-Champaign (UIUC). During her graduate studies, she worked as a research and teaching assistant. Before joining TED University, she was a postdoctoral research associate in Computer Vision Group at University of Central Florida (UCF).

Dr. Pehlivan is working on computer vision and machine learning with a primary focus on human activity understanding with video interpretation and object recognition.

Lisansüstü Araştırmaları Çalıştayı ve Bitirme Projeleri Sergisi [09.05.2016]

Inter Machines Communication [3.4.2015]

Life Sciences Data Management in a Research Center [27.3.2015]

Ultimate Trends in Software Engineering [19.3.2015]

Speech Recognition Systems [13.3.2015]

Forensic and Latest Topics [6.3.2015]

Big Data Mangement with Oracle [26.12.2014]

On Chip Networks [18.12.2014]

Problems in extremal graph theory[11.12.2014]

Management [3.12.2014]

R&D and Technology in Defense Industry [28.11.2014]

Smart Energy [21.11.2014]

Digital Companies and Excellence in business process [14.11.2014]

Treats and Defense in Network Security [7.11.2014]

Wireless Sensor Networks [24.10.2014]

Hacettepe University Department of Computer Engineering
06800 Beytepe Ankara