|NAme and Last Name||Department||Project Title (in English)||Research Center or Lab Affiliated (if applicable)||Dates (Expected start and end date)||Can the students participate online?||Specific Requirements (if any)||Project Description (in English)|
|Begüm Kübra Tokyay||Biomedical Science and Engineering/Mechanical Engineering||A label-free electrochemical biosensor based on screen-printed electrodes modified with bioconjugate for rapid detection of Human IgG||DxBiotechnology Laboratory (PI: Assoc. Prof. Savaş Taşoğlu)||July/August||No||This work focuses on developing bioconjugate modified screen-printed carbon electrode as an electrochemical immunosensor for the label-free detection of human immunoglobulin G (hIgG). To selectively detect immunoglobulin G, the anti-hIgG antibody with high affinity to hIgG is conjugated with the AuNP and they were deposited screen-printed carbon electrode. The [Fe (CN)6]4-/3- redox probe is applied to measure hIgG through the immunocomplex formation that is quantitatively related to the level of the cyclic voltammetry and impedance spectroscopy results.|
|Sarp Kaya||Chemistry||Electrode Development for Green Hydrogen Production||KUTEM||TBD||No||Synthesis and characterization of photoactive electrodes for hydrogen generation. A variety of electrochemical methods will be used to identify the activities of the photoactive electrodes|
|Metin Sezgin||Computer Engineering||Intelligent User Interface Design||Intelligent User Interfaces Laboratory||June-August||Yes||We are interested in enabling natural human-computer interaction by combining techniques from machine learning, computer vision, computer graphics, human-computer interaction and psychology. Specific areas that we focus on include: multimodal human-computer interfaces, affective computing, pen-based interfaces, sketch-based applications, intelligent user interfaces, applications of computer vision and machine learning to solving real world problems.|
|Fatma Guney||Computer Science and Engineering||End-to-End Learning of Driving||Autonomous Vision Group, KUIS AI||June-August||Yes||A good understanding of linear algebra, probability, and other Computer Science courses including programming, data structures, algorithms, and OS.||We will develop deep learning methods for learning driving from videos to control outputs. We wıll fırst devleop future prediction algorithms to learn the dynamics of the world and then use them for model-based reinforement learning.|
|Aykut Erdem||Computer Science and Engineering||Medical Vision-Language Pre-training||TBD||Yes||Strong background im deep learning, preferably in large language models||In this project, students will work on the design and development of multimodal large language models on medical image and text data.|
|Alptekin Küpçü||Computer Science and Engineering||Blockchain||Cryptography, Cyber Security, and Privacy Research Group||TBD||Yes||Interest in cryptography and security. Programming experience (Java, C++). Completed Data Structures and Algorithms type of coursework. Knows basic probability and discrete mathematics. Network Programming or Solidity Programming is a plus.||Multiple blockchain related tasks are available, including implementation on Ethereum, implementing a Blockchain simulator, as well as preparing materials for course slides.|
|Alptekin Küpçü||Computer Science and Engineering||Privacy Preserving Machine Learning||Cryptography, Cyber Security, and Privacy Research Group||TBD||Yes||Interest in cryptography, security, machine learning. Programming experience (Java, C++, Python, Go). Completed Data Structures and Algorithms type of coursework. Knows basic probability and discrete mathematics. Has taken a course on machine learning, knows gradient descent, backpropagation, etc. Go programming language knowledge is a plus.||Tasks related to adversarial machine learning, implementing attacks and defenses, employing cryptographic techniques such as homomorphic encryption.|
|Gözde Gül Şahin||Computer Science and Engineering||Automatic Learning of Procedural Language from Natural Language Instructions for Intelligent Assistance||KUIS AI Lab||TBD||Yes||Basic background in NLP and ML required||A programmable personal assistant requires, at minimum, the ability to understand the procedural language to be able to follow and execute the instructions. This project aims to enable it by a) introducing robust and generalizable techniques to map natural language utterances to well-formed procedures, b) creating annotated corpora of complex instructions and procedures to foster research in understanding human-written procedures, c) developing suitable evaluation measures to assess progress in this novel research direction. Intern(s) will work on one of the three aforementioned problems. The details will be discussed later.|
|Didem Unat||Computer Science and Engineering||Sparse Tensor Reordering Algorithms||Parallel and Multicore Computing Laboratory||June 2023||Yes||C programming, knowledge of graph algorithms||In this project, your goal is to implement sparse tensor reordering algorithms to improve cache utilization of CPD and other sparse tensor operations. You will also compare the performance of these algorithms on state-of-the-art multicore architectures.|
|Didem Unat||Computer Science and Engineering||Sparse Matrix and Tensor Pre-Processing Library||Parallel and Multicore Computing Lab (https://parcorelab.ku.edu.tr)||June 15- September 15||Yes||C++ programming (required), familiarity with sparse matrices/graphs is preferred but not required||In this project, you will help develop a pre-processing library we are developing together with other 5 partners in our SparCity project funded by EuroHPC. This library performs partitioning, reordering, feature extraction for sparse matrices, tensors and graphs. Your responsibility is to integrate algorithms and methods into this library (https://github.com/sparcityeu/sparsebase), perform unit-tests, and performance tests.|
|Öznur Özkasap||Computer Science and Engineering||Internet of Energy with Structured Blockchain||Distributed Systems and Reliable Networks Research Lab||June – August 2023||yes||Preferably junior/senior students in Computer Engineering or Computer Science. Excellent programming skills and Computer Networks background.||In this project, the objective is to do research and develop techniques and algorithms by applying innovative distributed blockchain, smart contract and distributed system principles to key research problems and areas such as (i) Distributed energy trading and sharing, (ii) Smart microgrid energy networks, and (iii) Electric and connected vehicle management. In different simulation and deployment scenarios, the project would involve comparison of algorithms based on various performance metrics of interest.|
|Öznur Özkasap||Computer Science and Engineering||Federated Learning via Client Selection||Distributed Systems and Reliable Networks Research Lab||June – August 2023||yes||Preferably junior/senior students in Computer Engineering or Computer Science.
Excellent programming skills and Computer Networks background.
|This project addresses client selection in federated learning in order to enhance the communication efficiency of the system. The aim is to develop a general client selection software platform that should include different client selection strategies and various aggregation methods.|
|Öznur Özkasap||Computer Science and Engineering||Decentralized Healthcare Systems||Distributed Systems and Reliable Networks Research Lab||June – August 2023||yes||Preferably junior/senior students in Computer Engineering or Computer Science.
Excellent programming skills and Computer Networks background.
|This project addresses usage of blockchain and key distributed system technologies in decentralized healthcare systems.The aim is to classify the existing solutions in the literature and develop a prototype decentralized healthcare system implementation using blockchain.|
|Seda Ertaç||Economics||Improving Non-Cognitive Skills and Achievement Outcomes in Education: An Experimental Study||TBD||Yes||None||sedaertac.com/field-experiments|
|Beren Semiz||Electrical and Electronics Engineering||Wearable System Design and Biomedical Signal Processing for Health Monitoring||Physiological Analysis and Wearable Systems Research Lab||Start Date: Any time around May-June||Yes||There are two main branches under our research:
1) Development of wearable systems and related algorithms for cardiovascular health monitoring,
2) Development of wearable systems and related algorithms for treatment assessment and tracking following total knee arthroplasty.
Project details will be clarified after talking with students.”
|Hakan Ürey||Electrical and Electronics Engineering||Augmented Reality Summer||Optical Microsystems Lab (OML)||TBD||Yes||Hakan Urey’s lab will be hosting several high school and University students. Students work in teams of 4-6 and develop Augmented Reality and metaverse software and apps using 3D rendering tools, AI, and machine learning.|
|Zafer Doğan||Electrical and Electronics Engineering||Developing tractable deep learning models for image enhancement and restoration problems||KUIS Artifical Intelligence Center||29.05.2023-28.07.2023 (or to be confirmed with the PI)||Yes||Decent programming skill in Python, C/C++/ A good understanding of signal and image processing concepts||“Here we focus on image enhancement and restoration problem. Deep learning has become the method of choice for image enhancement and restoration. However, prior to the DL revolution, non-local image restoration methods, such as non-local means, and BM3D, were the dominant algorithms in the field. Here, the goal is to reconcile DL with non-local algorithms and come up with a new generation of powerful image enhancement and restoration methods. The topics include, but are not limited to:
– Image/video super-resolution
– Image/video denoising
– Image/video inpainting
– Image/video dehazing
– Image/video deblurring
|Zafer Doğan||Electrical and Electronics Engineering||Classification of EEG microstates among PNES patients before and after nonepileptic seizures using machine learning methods||KUIS Artificial Intelligence Center||May 31, 2021 – July 16, 2021 (or to be confirmed with the PI)||Yes||Decent programming skill in MATLAB/ A good understanding of signal processing and machine learnig methods/ Experience with the EEG data is a plus||The Psychogenic Nonepileptic Seizures (PNES) are attacks that may look like epileptic seizures but are not epileptic and instead are cause by psychological factors. The only reliable test to positively make the diagnosis of PNES is video electroencephalography (vEEG) monitoring, which can be very long, and time consuming. Instead, in this work, we focus on the analyzing the changes observed in EEG signal before and after the seizures among the PNES patients. In EEG, microstates emerge as distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Hence, microstate analysis will be used as a way of utilizing EEG as both temporal and spatial imaging tool. Finally, the EEG microstates of PNES patients will be classified into groups representing different time information of the seizures.|
|M. Erdem Kabadayı||History||GeoAI_LULC_Seg: A GeoAI-based Land Use Land Cover Segmentation Process to Analyse and Predict Rural Depopulation, Agricultural Land Abandonment, and Deforestation in Bulgaria and Turkey, 1940-2040||UrbanOccupationsOETR (https://urbanoccupations.ku.edu.tr/)||01.03.2023-31.03.2024||No||Advanced GIS knowledge and/or experience in coding in Python, deep learning and computer vision applications would be necessary||Rural depopulation, agricultural land abandonment, and deforestation are massive concerns for Europe and elsewhere today and our planet’s future. These interlinked phenomena can be analysed using land use and land cover (LULC) maps combined with dynamics of population geography, especially regarding urban sprawl. Modern LULC and spatially disaggregated population datasets go back to the 1980s and 1970s. Although we have earlier population data, these are not geomatched to locations in LULC maps. Earlier LULC maps are either not very reliable (extracted from historical maps) or limited in their geographical coverage (based on selected aerial photos or satellite imagery). These are severe limitations to developing longer and deeper perspectives and understanding the root causes of these detrimental changes in population geography and land use practices in large territories.
GeoAI_LULC_Seg will develop an advanced, modular, and customizable geospatial artificial intelligence-based land use land cover segmentation process to accurately map LULC conditions for around 30,000 km2 in a border region between Bulgaria and Turkey, including the cities Edirne, Istanbul, and Plovdiv, from historical aerial photographs and early reconnaissance satellite images (dating back to the 1950s and the 1970s respectively) by pairing them with geotagged historical population census data.
Our methodological novelties are not limited to GeoAI-based object segmentation and super-resolution applications for panchromatic imagery for our research area. Our project will create transferable knowledge and scalable methods for global applications for the 1970s, thanks to worldwide coverage of high-spatial-resolution satellite imagery we will process. Furthermore, we will build long-term LULC maps series commensurable with current satellite data (1950-2020), allowing us to improve predictions for future population geography and LULC changes.
|Merih Angin||International Relations||IMF Decides, Machine Learns: An AI Approach to IOs||MA-CSSL||03.07.2023 – 20.08.2023||Yes||The International Monetary Fund (IMF), which is considered as “the most powerful international institution in history”, is frequently argued to be an agent of its most powerful shareholders. Challenging the common belief that the strategic allies of the United States and/or G5 countries always get better deals from the IMF, whereas it is the IMF staff who has the main leverage over the design of conditionality when low-income countries are borrowing from the Fund, this project will develop a novel framework, drawing upon a rich body of literature on the IMF, in order to present a comprehensive model that takes into account all actors having an impact on IMF program design. The following questions will be at the core of this research: What factors influence the terms (loan size, number of conditions, and conditionality waivers granted) of an IMF program? And how do those factors play into shaping the design of the programs? In this context, the project will focus on three particular aspects of IMF lending: (1) the size of IMF loans; (2) the number of conditions attached to the loans; and (3) the number of condition waivers granted to borrowing countries during program implementation. By inventing a comprehensive novel methodology for understanding IMF program design, this project will shed light on the processes leading to variation in IMF lending. Through creating an original framework, this project aims to provide an indispensable and extensible tool for international political economy (IPE) researchers, policymakers, governments and IMF staff to model the program design and implementation process with high predictive power of the outcomes. The project aims to make a major contribution to the literature by creating a comprehensive machine learning (ML) model for predicting the loan size, number of IMF conditions and waivers during program implementation, which complements traditional statistical models by integrating a larger number of variables and accuracy of prediction. The research will also create a natural language processing (NLP) tool for automated, fast analysis of the IMF’s Executive Board meeting minutes, which is able to capture elements including individual board member sentiments, alliance between representatives of different countries and G5 stance.|
|Cem Veziroğlu||Law School||Sustainability and Commercial Law||law.ku.edu.tr/arastirma/arastirma-alanlari/||TBD|
|Aykut Coskun||MAVA/KUAR||The identification of behavioral and contextual factors affecting food waste in the food service industry and the acceptance of technological interventions to reduce this waste through design-oriented research||KUAR||No||An in-depth investigation of the causes of food waste in the food service industry, which was the second biggest contributor of food waste after households, and the development of new design interventions to prevent this waste is an essential need in the field. The project aims to obtain a design guide that considers the different behaviors, intentions, and attitudes of food service industry stakeholders and assists in developing technological interventions that effectively reduce waste in different types of food service. Semi-structured interviews and diary studies will be used to examine food waste in various food service companies in Istanbul. Co-creation workshops will be used to create new design interventions with stakeholders (managers, chefs, cooks, service personnel, and consumers). The project will pave the way for new ideas, prototypes, and products to reduce food waste in the food service industry.|
|Aykut Coskun||MAVA/KUAR||Digital infrastructures for sustainable consumption: Redirecting, reorganizing, reducing and reimaging consumption||KUAR||3 July – 11 August||No||Global consumption and production volumes have increased for years and are now at a non-sustainable level. This has resulted in over-exploitation of natural resources, loss of biodiversity and climate change. DISCo aims to produce knowledge on how consumption can move in a more sustainable direction by applying digital technologies. While many would agree that it is important to transition to more sustainable lifestyles to address the problems we are facing, changing current consumption patterns has proved difficult. Part of this has to do with the difficulties associated with being a sustainable consumer. Lack of information, multiple sustainability labels, and green washing create confusion among consumers. Furthermore, practices such as repairing, reusing, purchasing second hand and recycling require knowledge and can be time consuming. Digital technologies can solve many of these problems. Smartphone applications and digital platforms can assist consumers to choose sustainable products in-store, support peer-to-peer sharing networks, and facilitate the reselling of goods. Digital technologies contain thus great promise for promoting sustainable consumption. However, if this potential is to be realised, we must have a better understanding of how and with what environmental consequences these “greening” technologies become part of households’ everyday practices. What is involved in the successful introduction of these digital technologies? Do they really bring about more sustainable modes of consumption? DISCo draws on theories of practice and makes use of multiple methods to systematically investigate digital efforts reconfigure consumption in the areas of food and mobility. Ethnographic methods are put to use to understand everyday life, life cycle analysis to understand the impacts of proposed solutions, and design methods to instigate positive change. Empirical focus will be on the consumption of food and mobility. These areas are of interest because of their interlinking significant carbon footprints and their central importance to everyday consumption.|
|Cagatay Basdogan||Mechanical Engineering||Modeling of Human-Robot Interaction Behavior||Robotics and Mechatronics Laboratory||25.07.2023 – 25.08.2023||Yes||Some background in programming (Matlab/Python) is necessary||Human and robot, working together, are expected to play an important role in the foreseeable future. The increasing demand for high-quality and more flexible systems to carry out complex tasks put this dyad firmly in the field. Integrating human’s dexterity and problem-solving skills along with robot’s precision, strength, and repeatability into tasks involving physical interaction between them, pHRI, could be quite beneficial. Such a collaborative interaction may result in significant improvements in task performance and reduction in physical human effort. In order to make pHRI more effective, we need robots to anticipate the human partner’s behavior and act accordingly. For example, in collaborative manipulation of heavy objects, if the robot anticipates the human motion trajectory, it can contribute to the acceleration/deceleration of the manipulated object more effectively. In this project, you will develop model to estimate the motion trajectory and force response of the human collaborating with a robot.|
|Ismail Lazoglu||Mechanical Engineering||Artificial Heart Pump||Manufacturing and Automation Research Center||No||Strong interest and motivation in Biomedical, Mechatronics, Design and Manufacturing||Development of an artificial heart pump (Left Ventricular Assist Device)|
|Ismail Lazoglu||Mechanical Engineering||Additive Manufacturing||Manufacturing and Automation Research Center||No||Strong interest and motivation in 3D Printing Technologies||3D Printing Technologies|
|Füsun Can||Medical Microbiology||Emerging viral infections||Emerging viral infections||No|
|Ahmet İçduygu||MiReKoc||The Continuous Reporting System on Migration (SOPEMI Report),||MiReKoc||3 July 2023-4 August 2023 (5 weeks)||Yes||–||SOPEMI Report provides the OECD member countries with a mechanism for the timely sharing of information on international migration, the collect of migration statistics as well as the improvement of their comparability, and to serve the basis for an annual OECD report on international migration. KUSRP members will contribute the program with researching annual statistics and reports for Turkey for the next SOPEMI Report.|
|Ahmet İçduygu||MiReKoc||Bridging the Migration and Urban Studies Nexus (BROAD-ER)||MiReKoc||3 July 2023- 5 August 2023 (5 weeks)||Yes||–||BROAD-ER is a research network project aiming to enhance the scientific, technological and development-driven management capacities of Koc University (KU), Migration Research Center at Koç University (MiReKoc) in Istanbul, Turkey, by linking it with two internationally leading research institutions in the themes of migration and urban studies. BROAD-ER brings together leading experts in the field of migration and urban studies. KUSRP members will participate the project with research papers, desk based research and in preparation of BROAD-ER MiReKoc Summer School (a 2 week training program)|
|Ayse Koca Caydasi||Molecular Biology and Genetics||Cell cycle regulation||Cell cycle and genome stability laboratory||July-August||No||Students should come to the lab in person every day.||Using budding yeast as a model, we aim to discover novel mechanisms that regulate cell cycle.|
|Serkan Kır||Molecular Biology and Genetics||Investigation of muscle atrophy mechanisms in mice||15.06.2023-15.08.2023||No||Investigation of muscle atrophy mechanisms in mice|
|İsmail K. Sağlam||Molecular Biology and Genetics||Genomics architecture and evolution of lentil domestication||July – September||No||Researchers will be tasked with comparing genomes of wild and domesticated lentils in order to understand genomic architecture of domesticated lentil and discover functional genes and regions under strong selection leading to adaptation of lentil to changing environments.|
|Sami Gülgöz||Psychology||Memory transmission across generations||Koç University Research in Applied Memory (KURAM)||June 19 – August 4||Yes||Native speakers of Turkish||We will study the transmission of memories from one generation to the next, their functions for the sharing and shared generations, their usability for the younger generations, and the consistency in the interpretation of memories by different generations.|
|Tilbe Göksun||Psychology||Language and Thought Interactions||Language & Cognition Lab (https://lclab.ku.edu.tr/)||03.07.2023 – 20.08.2023.||Yes||Turkish native speakers||In Language and Cognition Lab (https://lclab.ku.edu.tr/), we examine the relation between language (multimodal – gesture and speech) and many cognitive domains. We work with different populations and age groups. We also examine young children’s early language and cognitive development using techniques such as eyetracking, parent-child interaction, standardized tasks, and experiments for specific cognitive domains. For these lines of research, the project assistants will help running participants, coding and analyzing data as well as designing new studies.|
|Yasemin Kisbu||Psychology||Evidence generation for child and youth well-being – multiple projects||Independent Evaluation Laboratory (IEL)||TBD||Yes||Graduate students or junior/senior level undergraduate students.||Multiple ongoing projects in our lab involving quantitative survey, large scale data analysis, and impact evaluation studies. More information can be found at the lab website www.evalresearchlab.com or iel.ku.edu.tr|
|Tamer Onder||School of Medicine||Identification of epigenetic factors in naive pluripotent stem cells||https://scl.ku.edu.tr/||1 July – 15 August||No||It will soon be possible to generate human naive stem cell lines in a personalized and disease-specific manner through epigenetic reprogramming of somatic cells. However, almost none of the epigenetic factors that play a role in this process is known. In the EpiKök, we will investigate the function of three specific chromatin factors (DOT1L, P300/CBP, BRD9) in naïve reprogramming using novel chemical inhibitors. In doing so, we will test the hypothesis that these factors prevent reprogramming to naïve pluripotency. Using these inhibitors, a high-efficiency protocol to generate naïve stem cells will be established.|
|İrem Durmaz Sahin||School of Medicine||Ovarian cancer, drug resistance, anti-cancer agents||https://kuttam.ku.edu.tr/kadromuz/akademik-kadro/fakulte-uyeleri/irem-durmaz-sahin/||TBD||Investigation of drug-resistance mechanisms in ovarian cancer and the role of miRNAs on drug-resistance; and characterization of novel anti-cancer agents in ovarian cancer|
|Erdem Yörük||Sociology||Politus||the Center for Computational Social Sciences||June 1st – August 1st||Yes||data and programming skills, in particular, Phyton||The ERC-funded Politus Project is based on an interdisciplinary multi-method approach, which will combine social scientific, computational, ethical and legal perspectives to deliver an innovative breakthrough technology that will radically transform public opinion research. Politus will develop a new approach to public opinion research, which will be based on a fusion of different data analytics methods and data types: a range of online platforms; Natural Language Processing, multimodal deep learning for image and text data; computational network analysis; survey poll; advanced post-stratification methods for representativeness. This would allow us to attain a high-resolution, fine-grained, longitudinal and representative measurement of the societal trends in different countries and different languages. The first step will be data collection. Image and textual content from online platforms will be collected using API tools. Then, an annotation process will build a gold standard corpus, which will feed the supervised machine learning models to predict ingredients of public opinion. For model building, Politus will pursue a second strategy, the so-called amplified learning, as well, following in the footsteps of Blumenstock et al. (2015) and connecting survey data and user-generated content. An online survey will ask consent-giving respondents questions about demography and public opinion. Then a model will be built to predict these responses using the respondents’ online platform contents. These online surveys will be periodically conducted to keep the models updated, essentially because of the time-variant nature of the indicators in question – ideology, belief, value, topics, emotion, stance, and electoral behavior. Network analysis will then be applied to enrich the dataset. An analysis of the network structure surrounding individual users will provide deep insights into this public opinion. For example, an individual user’s political ideology or religiosity can be inferred from this user’s network interactions.|
|Ayşen Üstübici||Sociology||Cash aid in forced displacement||MİREKOC||03.07.2023 – 20.08.2023||Yes||excellent command of English. Prior knowledge on social assistance, Turkey and migration in an asset.||Initiated by the EU-Turkey statement of March 2016, the Emergency Social Safety Net (ESSN) is a major cash transfer program funded by the EU to improve the living conditions of refugees in Turkey. The ESSN is one of the largest programs of its kind and entails a modest, but regular, cash transfer to vulnerable refugee families in Turkey. ESSN cash assistance is also complemented with conditional cash transfers for education (CCTE) funded by UNICEF. There are also other less comprehensive initiatives providing assistance in cash and aid in-kind to enhance the integration of refugee communities in Turkey. This project will assess the ESSN program in Turkey from the beneficiaries’ perspectives. A comparative approach will be embraced by doing a literature review on similar cash aid programs aroun|