Graduate School of Informatics

02 -19 Ağustos 2019 2. GRUP MÜLAKATLARI
ODTÜ Akademik Takvimi’nde yapılan değişiklik nedeniyle sonbahar dönemi başvurularında iki gruplu değerlendirme uygulanacaktır. Bu uygulama hakkında detaylı bilgi linkteki duyuruda verilmektedir: http://ii.metu.edu.tr/tr/duyuru/lisansustu-basvurularla-ilgili-onemli-duyuru”.

Bilişsel Bilimler Anabilim Dalı 2. grup Doktora mülakatları 03 Eylül 2019 tarihinde yapılacaktır.

- ÖNEMLİ NOT - 

* Mülakata davet edilecek adayların isim listesi ve mülakat başlangıç saatleri ayrıca duyurulacaktır. 

** Tezsiz yüksek lisans programı için mülakat yapılmayacaktır. 

02 -19 Ağustos 2019 2. GRUP MÜLAKATLARI
ODTÜ Akademik Takvimi’nde yapılan değişiklik nedeniyle sonbahar dönemi başvurularında iki gruplu değerlendirme uygulanacaktır. Bu uygulama hakkında detaylı bilgi linkteki duyuruda verilmektedir: http://ii.metu.edu.tr/tr/duyuru/lisansustu-basvurularla-ilgili-onemli-duyuru”.

Bilişim Sistemleri Anabilim Dalı 2. grup Yüksek Lisans ve Doktora mülakatları 29 Ağustos 2019 tarihinde yapılacaktır.

- ÖNEMLİ NOT - 

Mülakata davet edilecek adayların isim listesi ve mülakat başlangıç saatleri ayrıca duyurulacaktır. 

M.S. Candidate: Zafer Şengül

Program: Cyber Security

Date: 07.08.2019 / 09:45

Place: A-212

Abstract: Malware are malicious programs that cause unwanted system behavior and usually result in damage to IT systems or its users. These effects can also be seen during military operations because high-tech military weapons, command, control and communication systems are also interconnected IT systems. This thesis employs conventional models that have been used for modeling the propagation of biological diseases to investigate the spread of malware in connected systems. In particular, it proposes a probabilistic learning approach, namely Bayesian Network analysis, for developing a framework for the investigation of mixed epidemic model and combat models to characterize the propagation of malware. Compared to the classical models, which have employed formula-based representations, the results of this thesis reveal more enriched representations of the superiority of one military force over the other in probabilistic terms.

M.S. Candidate: Habibe Cansu Demirel

Program: Bioinformatics

Date: 31.07.2019

Place: A-108

Abstract: Alternative splicing is a post-transcriptional regulation which is important for the diversity of the proteome and eventually the interactome. It enables the production of multiple proteins from a single gene with different structures. In a network point of view, these structural changes can introduce new interactions or cause the loss of the existing ones. The variations in this mechanism has been associated with various diseases including cancer. In this study, we reconstructed patient specific networks with tumor specific protein isoforms by integrating the protein structures and the interaction losses they bring with. For this purpose, we collected 400 breast cancer tumors and 112 normal RNA-seq data from the Cancer Genome Atlas (TCGA) and found the transcripts that show increased expression patterns in tumor cells. We mapped these transcripts to their available protein isoforms found in UniProt. Additionally, we compiled a structural human interactome from multiple sources and aligned the missing residues on isoforms with the known/predicted protein interfaces to find potential interaction losses. At the end, we constructed two interactomes for each sample; one filtered based on the lost interfaces as a result of predominant isoforms (called “terminal set”) and one filtered based on the expression. Then, we used the same terminal set with Omics Integrator to model two sets of networks based on the two patient-specific interactomes. Finally, we compared the resulting two networks and all tumor specific networks simultaneously to reveal pathway, protein-protein interaction and protein patterns that can cluster the tumors according to their similarities. The results of our analysis will contribute to the elucidation of tumor mechanisms and will help for target selection and developing therapeutic strategies.

PhD. Candidate: Emre Süren

Program: Bilişim Sistemleri

Date: 06.08.2019

Place: Konferans Salonu 01

Abstract: The prevalence and non-stop evolving technical sophistication of Exploit Kits (EKs) is one of the most challenging shifts in the modern cybercrime landscape. Over the last few years, malware infection via drive-by download attacks have been orchestrated with EK infrastructures. An EK serves various types of malicious content via several threat vectors for a variety of criminal attempts, which are mostly monetary-centric. In this dissertation, an in-depth discussion of the EK philosophy and internals is provided. A content analysis is introduced for the EK families where special contextaware properties are identified. A key observation is that while the webpage contents have drastic differences between distinct intrusions executed through the same EK, the patterns in URL addresses stay similar. This is due to the fact that auto-generated URLs by EK platforms follow specific templates. This dissertation proposes a new lightweight technique to quickly categorize unknown EK families with high accuracy leveraging machine learning algorithms with novel URL features. Rather than analyzing each URL individually, the proposed overall URL patterns approach examines all URLs associated with an EK infection. The method has been evaluated with a popular and publicly available dataset that contains 240 different real-world infection cases involving over 2250 URLs, the incidents being linked with the 4 major EK flavors that occurred throughout the year 2016. In the experiments, the system achieves up to 93.7 % clustering accuracy and up to 100 % classification accuracy with the estimators experimented.

The booklet can be found here.

M.S. Candidate: Amin Zabardast

Program: Medical Informatics

Date: 07.08.2019

Place: A-108

Abstract: Minimally invasive surgical procedures utilize technology to provide surgeons with more functionality as well as a better perspective to help them succeed in their tasks and reduce operations risks. Surgeons usually rely on screens and cameras during minimally invasive surgeries such as Laparoscopic, Endoscopic, or Robotic Surgeries. Currently, operating rooms use information from different modalities such as Computer-Aided Tomography and Magnetic Resonance Imaging. However, the information is not integrated, and the task of extracting and combining features falls under the surgeon’s expertise. Conventional cameras, although very helpful, are not capable of transmitting every aspect of the scene including depth perception. Recently stereo cameras are being introduced to operating rooms. Utilizing stereo endoscopic equipment alongside algorithms to process the information can enable depth perception.The process of extracting depth information from stereo cameras, also known as Stereo Correspondence, is still an active research field in computer science. Understanding depth information from the view is a necessary step for reconstruction of the scene in a 3D environment. Ultimately, this reconstructed environment acts as a basis to build an Augmented Reality with extra information baked into the scene to help the surgeon. Artificial Neural Networks (ANNs), specially Convolutional Neural Networks (CNNs), have revolutionized the computer vision research in the past few years. One of the problems that researchers tried to solve using ANNs was Stereo Correspondence. There are variations of CNNs with excellent accuracy in Stereo Correspondence problem. This thesis aims to achieve surface reconstruction from in vitro stereo images of organs using Deep Neural Networks and in silico simulations.