Senior Projects Poster Session
CMPE Senior Project Poster Session was held on Thursday, May 31, 2018.   Read more...
Data for Refugees
Türk Telekom, TÜBİTAK and Boğaziçi University initiated the "D4R – Data for Read more...
EU Funding for Full-time Msc/Phd Positions in Cognitive Robotics and Robot Learning
Project name: IMAGINE: Robots Understanding Their Actions by Imagining Their Read more...
Special 6-week training course organized with Havelsan: "Introduction to Machine Learning and Data Analysis"

CmpE Events


  1. CmpE 579/700 Seminar: What comes after opening your robot's box? by Baris Akgun, Koc University
    • Start time: 12:00pm, Tuesday, December 18th
    • End time: 01:00pm, Tuesday, December 18th
    • Where: AVS Conference Room, BM
    • Summary:

      Current consumer and end-user robots are mostly purpose built and only capable of doing a single or a few tasks. The most general robots that can be found are manipulators, which until recently, has been behind cages or inside research labs. With the advent of low-cost collaborative robots, bringing (relatively) general purpose robots to end-users is becoming a possibility. The advances in machine learning and artificial intelligence are a major contributor to this potential as well. However, there is a lot to be solved to get there.

      This presentation will include challenges, research and ideas about how to make this possible using learning from demonstration and robotic self-learning. The main idea will concentrate how humans are goal-oriented and the ways that we can leverage this for learning. The following scenario will be the main motivator: "You buy a robot from the tech store, bring it home, open its box, look at its app store and realize that some of the things you want are missing! What do you do?" The talk will also include a learning approach for how a robot can express its own goals kinematically for more fluent collaborative interaction


      Barış Akgün is an Assistant Professor at the Koç University Computer Engineering Department. Prior to joining the faculty at KU on September 2016, he was a Post-Doctoral Fellow at the Electrical And Computer Engineering Department of The University of Texas at Austin. He earned his PhD Degree in Robotics from the Georgia Institute of Technology in 2015. He received his MSc Degree from the Computer Science Department and BSc. Degree with an extra-curricular Mechatronics minor from the Mechanical Engineering Department of Middle East Technical University in 2010 and 2007 respectively. His MSc and PhD work was on artificial intelligence, robot learning and human-robot interaction.

      His research interests lie at the intersection of Human-Robot Interaction, Artificial Learning for Robotics and Intelligence. His main research is about robots that learn from people and by themselves, and experimentally verified algorithmic human-robot interaction. He is also interested in applied machine learning for predictive maintenance, wearable devices, recommendation systems and medicine.

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  1. CmpE Seminar: Accurate and Scalable Processing of Big Data in Earth Observation by Begüm Demir
    • Start time: 11:00am, Thursday, December 20th
    • End time: 12:00pm, Thursday, December 20th
    • Where: AVS Conference Room, BM
    • Title: Accurate and Scalable Processing of Big Data in Earth Observation
      Speaker: Prof. Begüm Demir (Technische Universität Berlin)

      Abstract: During the last decade, a huge number of earth observation (EO) satellites with optical and Synthetic Aperture Radar sensors onboard have been launched and advances in satellite systems have increased the amount and variety of EO data. This has led to massive EO data archives with huge amount of remote sensing (RS) images, from which retrieving useful information is challenging. In view of that, content based image retrieval (CBIR) has attracted great attention in the RS community. In this lecture, a general overview on scientific and practical problems related to RS image characterization, indexing and search from massive archives will be initially discussed. Then, recent developments that can overcome the considered problems will be introduced by focusing on semantic-sensitive hashing based scalable and accurate RS CBIR systems.

      Short Bio of Prof. Begüm Demir: Begüm Demir is a Professor and Chair of the Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin (TU Berlin), Germany. From 2013 to 2017, she was an Assistant Professor at the Department of Computer Science and Information Engineering, University of Trento, Italy, while in 2017 she became an Associate Professor at the same department. Her research interests include image processing and machine learning with applications to remote sensing image analysis. She was a recipient of a Starting Grant from the European Research Council (ERC) with the project "BigEarth-Accurate and Scalable Processing of Big Data in Earth Observation" in 2017, and the "2018 Early Career Award" presented by the IEEE Geoscience and Remote Sensing Society. She is a senior member of IEEE since 2016. Dr. Begüm Demir is a Scientific Committee member of the Conference on Big Data from Space, Living Planet Symposium and SPIE International Conference on Signal and Image Processing for Remote Sensing. She is the founder and the co-chair of Image and Signal Processing for Remote Sensing Workshop organized within the IEEE Conference on Signal Processing and Communications Applications since 2014.

      Contact Person: F. Başak Aydemir

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  2. PILAB Seminar: Parallel sequential Monte Carlo for stochastic optimization by Omer Deniz Akyildiz, Carlos III University of Madrid, Spain
    • Start time: 12:00pm, Thursday, December 20th
    • End time: 01:00pm, Thursday, December 20th
    • Where: A5
    • Title: Parallel sequential Monte Carlo for stochastic optimization
      Speaker: Omer Deniz Akyildiz, Carlos III University of Madrid, Spain

      In this talk, I will talk about parallel sequential Monte Carlo optimization method to minimize cost functions which are computed as the sum of many component functions. The proposed scheme is a stochastic zeroth order optimization algorithm which uses only evaluations of small subsets of component functions to collect information from the problem. The algorithm consists of a bank of samplers and generates particle approximations of several sequences of probability measures. These measures are constructed in such a way that they have associated probability density functions whose global maxima coincide with the global minima of the cost function. The algorithm selects the best performing sampler and uses it to approximate a global minimum of the cost function. We prove analytically that the resulting estimator converges to a global minimum of the cost function almost surely as the number of Monte Carlo samples tends to infinity. We show that the algorithm can tackle cost functions with multiple minima or with wide flat regions.


      Omer Deniz Akyildiz received his BSc and MSc degrees from the Dept. of Electronics and Communications Engineering, Istanbul Technical University in 2010 and 2012, respectively. He then worked within the Statistical Inference Group of the Dept. of Computer Engineering in Bogazici University as a research assistant between 2012-2015 and as a quantitative researcher in algosis between 2014-2015. Since 2015, he has been working towards his PhD based on the Dept. of Signal Theory and Communications, Carlos III University of Madrid, Spain. During his PhD studies, he has held visiting researcher positions at Imperial College London, the Alan Turing Institute in London, and Fraunhofer Heinrich-Hertz Institute in Berlin. His research interests include computational statistics and machine learning, focusing specifically on stochastic filtering and optimization.

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Friday, December 21st

  1. CmpE Seminar: Quantifying Urban Social Well-Being using Mobile Phone Data by Didem Gündoğdu
    • Start time: 11:00am, Friday, December 21st
    • End time: 12:00pm, Friday, December 21st
    • Where: AVS Conference Room, BM
    • Title: Quantifying Urban Social Well-Being using Mobile Phone Data
      Speaker: Dr. Didem Gündoğdu

      Abstract: Today, more than half of the world population is living in cities, which has been doubled in the last 50 years. The reason for that attraction is not only economical, but also security, education, and health. While people migrate to cities to reach improved life conditions, several issues raised by the increasing population. Recent studies have shown the importance of ethnic and cultural diversity of urban population to encourage tolerance, and to foster creativity and economic growth. Facing the urban growth challenges, we search for the key formulas to obtain healthy societies under the light of new type of data sources, such as mobile phone usage datasets. To this end, first we build up a tool to identify security related incidents from a country, which unstable political conditions held. Then we trace the formulas of healthy societies with examples from both developing and developed countries. We check the individual interaction and communication pattern effects (bridging and bonding) for the existence of social capital. Then we analyze aggregated ethnic diversity, and associate segregation scores with census data, and different ethnic groups preferences to move in the city, existence of any pattern for specific nation. The current studies are mainly hypothetical, with the absence of large-scale real life data sources. This talk aims to provide an insight to policy makers for building healthy societies, for the benefit of urban well-being.

      Short Bio of Didem Gündoğdu: Didem Gündoğdu has completed her Ph.D. with the thesis title, "Quantifying Urban Social Well-Being Using Mobile Phone Data" from Information & Communication Technologies at University of Trento, Italy. During her PhD, she was a member of the Mobile and Social Computing Lab (Mobs) at Fondazione Bruno Kessler (FBK) in Trento. Her research focuses on human behavior understanding from aggregated mobile phone usage data in the context of social well-being. She collaborated with Intelligent Social Systems Lab at University College London, for understanding segregation in cities and in the School of Business and Management, Queen Mary University of London, engaged in analyzing rural social capital through mobile phone usage data. She completed her MSc in Computer Science at Boğazici University, Turkey where she focused on anomalous event detection in telecommunication data using stochastic methods, and holds an Eng. Diploma in Computer Science and Engineering from the Yildiz Technical University, Istanbul, Turkey. She is working as a data scientist in Kantar, marketing research company.

      Contact Person: F. Başak Aydemir

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Contact us

Department of Computer Engineering, Boğaziçi University,
34342 Bebek, Istanbul, Turkey

  • Phone: +90 212 359 45 23/24
  • Fax: +90 212 2872461

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