Ongoing Projects

In ENSURE, the goal is to understand the dynamics of driving with different types of uncertainty to achieve safe self-driving in complex real world situations. These include uncertainties due to unknown intentions of other agents such as negotiation scenarios at intersections as well as uncertainties due to modelling errors, for example failing to predict future correctly due to an unknown object on the road. The 5-year project prioritizes safety and explainability of self-driving technology to increase its applicability in real world with state of the art deep learning techniques.

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Lorem ipsum dolor sit amet consectetur adipiscing elit. Optio odit, neque molestiae voluptatum illo, ducimus quo cupiditate vitae assumenda ex debitis ullam necessitatibus deleniti sapiente repellendus quae eligendi, corporis eos.

Lorem ipsum dolor sit amet consectetur adipiscing elit. Optio odit, neque molestiae voluptatum illo, ducimus quo cupiditate vitae assumenda ex debitis ullam necessitatibus deleniti sapiente repellendus quae eligendi, corporis eos.

Sketching is a natural and intuitive means of communication for expressing a concept or an idea. Sketches have various application areas such as problem-solving, design, and art. The increase in the usage of touch and pen-based devices has enabled sketches to be used in human-computer interaction and made sketch recognition an active research area. Our team believes that perception-based models can contribute to the sketch recognition task To be able to integrate perception into our model, we approach the sketch recognition problem with an interdisciplinary perspective.

Perfectly aligned with the vision of the EuroHPC Joint Undertaking, the SparCity project aims at creating a supercomputing framework that will provide efficient algorithms and coherent tools specifically designed for maximising the performance and energy efficiency of sparse computations on emerging HPC systems, while also opening up new usage areas for sparse computations in data analytics and deep learning.  The framework enables comprehensive application characterization and modeling, performing synergistic node-level and system-level software optimizations. By creating a digital SuperTwin, the framework is also capable of evaluating existing hardware components and addressing what-if scenarios on emerging architectures and systems in a co-design perspective. To demonstrate the effectiveness, societal impact, and usability of the framework, the SparCity project will enhance the computing scale and energy efficiency of four challenging real-life applications that come from drastically different domains, namely, computational cardiology, social networks, bioinformatics and autonomous driving. By targeting this collection of challenging applications, SparCity will develop world-class, extreme scale and energy-efficient HPC technologies, and contribute to building a sustainable exascale ecosystem and increasing Europe’s competitiveness.

We are experiencing a data-driven revolution at the moment with data being collected at an unprecedented rate. In particular, there is an increasing excitement toward autonomous systems with learning capabilities. Several data-driven applications have already shown significant practical benefit revealing the power of having access to more data, e.g., health care systems, self-driving cars, instant machine translation, and recommendation systems. However, large acceptance of such systems heavily depends on their stability, tractability and reproducibility, where current applications fall inadequate in providing such features. The scale and complexity of modern datasets often render classical data processing techniques infeasible, and therefore, several new algorithms are required to address new technical challenges associated with the nature of the data.

This project focuses on developing efficient and tractable solutions for large-scale learning problems encountered in machine learning and signal processing. Apart from theoretical aspects, the project bears specific goals targeted to applications in principal subspace estimation, low-rank matrix factorization, tensor decomposition and deep learning for largescale systems. Specifically, this novel approach brings together several attractive features:

• The emerging concept of online-learning will be adapted to a distributed setting across a decentralized network topology.
• The exact dynamics of the algorithms will be extracted by a stochastic process analysis method; which current state-of-the-art methods are not able to deliver.
• Studying the extracted dynamics, the learning capabilities and performances of large-scale systems will be improved to match the current needs and challenges of the modern data-driven applications.

The interface between physics and machine learning is a fast-growing field of research. While most studies in this frontier involve using deep learning methods to extract physical knowledge from experimental data or theoretical models, relatively little is known about the nontrivial dynamics of training on artificial neural networks. The analytical framework of nonequilibrium physics developed in the last two decades provides versatile tools that find a novel application in this context. The dynamics of machine learning displays some interesting features not found in physical systems, such as a nontrivial noise structure and a resulting non-thermal steady state, as we observed in our preliminary investigations. The proposed study aims to apply the know-how existing in the nonequilibrium physics literature to this modern problem and explore the implications of various universal laws (originally devised for microscopic systems and expressed in the language of statistical physics) for machine learning. We plan to employ well-known machine learning problems, such as MNIST or CIFAR, as well as some toy models as the testground for analytical predictions. The research team is composed of Dr. Deniz Yuret (Koç Univ) who is an expert in machine learning and the developer of the deep learning package (Knet) for the increasingly popular Julia platform, Dr. Michael Hinczewski (CWRU, USA) who has made important contributions to the literature on nonequilibrium aspects of biological systems, and Dr. Alkan Kabakçıoğlu (Koç Univ, PI), a computational statistical physicist whose recent studies focus on fluctuations and scaling properties of nonequilibrium processes in biomolecules. The proposed research will be conducted in the Department of Physics at Koç University and is expected to last two years.

The researcher Dr. Fatma Guney will carry out a fellowship to create the technology needed to understand the content of videos in a detailed, human-like manner, superseding the current limitations of static image understanding methods, and enabling more robust perception for autonomous driving agents. This fellowship will be carried out at Koc University under the supervision of Prof. Deniz Yuret. Understanding surrounding scene with a detailed and human-level reliability is essential to address complex situations in autonomous driving. State-of-the-art machine vision systems are very good at analysing static images by detecting and segmenting objects on a single image but relating them across time in a video still remains a challenge. Our goal in this proposal is to extend the success of static image understanding to the temporal domain by equipping machines with the ability to interpret videos by exploiting both appearance and motion cues with a human-like ability. There are several challenges which make it difficult for machines such as cluttered backgrounds, the variety and complexity of motion, and partial occlusions due to people interacting with each other.