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



Abstract

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

Bio:
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]



Abstract

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

Bio:
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]



Abstract

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

Bio:
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]



Abstract

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
METU

Bio:
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]



Abstract

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)


Bio:
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]



Abstract

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


Bio:
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]



Abstract

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

Bio:
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: https://www.facebook.com/events/349330645448151/

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: https://www.facebook.com/events/177326476066161/

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]



Program:

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]



Abstract

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

Bio:
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]



Abstract

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

Bio:
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]



Abstract

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

Bio:
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.

Bio
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
Email: burcu.urgen@gmail.com
Website: http://www.burcuaysenurgen.com


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

10:00

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