Scholarships 2019 - Positions

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Institutions: Rice University & NCSR Demokritos

Field: Artificial Intelligence / Bioinformatics
1. Computational modelling and analysis of biological interaction

This PhD thesis aims to introduce novel techniques and algorithms for computational modelling and analysis, focusing on the interactions of various molecules (e.g. RNA or proteins) with proteins. Research in this context may involve a number of cutting-edge approaches, ranging from structure-based binding prediction to hypergraph analysis methods for complex biological networks.
Why it is important: Biological systems and processes are based on various types of interaction among biological entities. At the lowest level, one finds interactions among individual molecules or complexes thereof, such as protein-ligand interactions. Such elementary interactions become the building blocks of more complex pathways, such as metabolic ones, and complex biological networks associated with particular cell functions, e.g. interactions among proteins in muscle cells. The computational modelling of biological interactions has become essential in understanding biological functions at various levels of abstraction. For instance, such modelling helps predicting the binding potential among proteins and ligands, based on structural, thermodynamic or chemical properties of the entities. At a higher level of abstraction, it also helps identifying functional associations among proteins or protein complexes, based on their interactions in a corresponding network.

Field: Artificial Intelligence / Robotics
2. Exploiting past experiences for robotic exploration

The goal of this PhD project is to transform the way robots explore and interact with a new environment by adapting prior knowledge gained through experiences in similar or relevant contexts. Two topics will be explored. The first is how to traverse rugged terrain and the second is how to manipulate objects. Possible research directions include strategies for acquiring and storing earlier experiences, autonomously experimenting with the environment in order to decide if an analogy holds, methods for reasoning about the physical underpinnings of a past experience and inferring if it applies to the new environment or not, and methods for reducing the experimentation needed by identifying the most informative data-points needed to adapt prior knowledge or to refine generic knowledge to a new environment.
Why it is important: No matter how alien and unexplored an environment is, there are always analogies and generalizations that a robot can use in order to exploit past experiences to inform future actions, so that it operates more robustly and improves its performance over time. Motion planning, as it is largely practiced today, focuses on solving one problem at a time and makes limited use of past history. Part of the problem, this project will try to tackle, is the adaptation of past experiences to the current situation, so that analogies hold and generalizations are sound and controlled.

Institutions: University of Texas at Arlington & NCSR Demokritos

Field: Artificial Intelligence / Human-Robot Collaboration
3. Robotic social intelligence for human-robot collaboration

The aim of this PhD project is to develop motion planning methods that embed in the motion plan cues used in human-human collaboration, to evaluate their eligibility and predictability in user studies, and to compare their efficiency and speed against plans that only take the latter into consideration. One example use case can be providing appropriate cues in a handover motion that the robot is about to release the object, even if this reduces the object-handing throughput. Another example use case can be that a robot that needs to reposition a number of objects does this in an order that is sub-optimal, but guarantees that it is at all times obvious which object it is going for next. This PhD project can involve socially assistive robots, arm robots and different multi-sensing devices to interact, including virtual and augmented reality, to enable the robot to learn.
Why it is important: The ability of both humans and robots to understand each other and collaborate effectively and safely is essential for achieving intuitive and seamless human-robot collaboration, in applications such as intelligent manufacturing or industry 4.0: how humans can collaborate with robots in the factory of the future. In order to achieve that the human collaborator is able to correctly interpret the robot's motion and for the robot to understand and learn from the human's actions, we need advanced machine learning algorithms. In addition, robot motion planning is needed that can reproduce cues and signals used in human-human collaboration. Naturally, making robot motion legible and predictable by humans might run counter to other qualities, such as the efficiency and speed of the motion.

Institutions: Houston University & NCSR Demokritos

Field: Artificial Intelligence/ Machine Learning
4. Deep Learning for the analysis of multimedia content

The aim of this PhD thesis is to adopt state-of-the-art machine learning methodologies focusing on deep supervised and semi-supervised learning and transfer learning, for content-based analysis of multimedia. This requires leveraging knowledge from all types of information, such as faces and facial expressions, objects, voices, music, audio events, speech emotions, user comments, web pages, video text, low level visual cues, accelerometer measurements, EEGs and user/expert-generated annotations.
Why it is important: Modern multimedia databases can contain millions of files such as videos, digital music collections, image archives and measurements from wearables. High-level semantic descriptions of such multimedia content are crucial for several application domains such as hybrid recommender and personalization systems, multimodal security applications, environmental monitoring and health monitoring systems.

Field: Artificial Intelligence / Deep Learning / Big Data / Linked Data
5. Towards using linked data for interpreting deep learning models

The proposed PhD thesis will extend current state-of-the-art research at the intersection of big data and deep learning, with a focus on the wider security domain. It will make use of heterogeneous big data, for example earth-observation and weather data, to define predictive models based on deep learning. In addition, it will define novel semantic structures for linking inputs and human concepts to the latent features of these models, eventually creating knowledge. This work will build both on advances in deep learning as well as on metadata schemas and ontologies.
Why it is important: Despite the widespread availability of big data and analytics, artificial intelligence systems to predict adverse events remain underutilised. This is in part due to the difficulty to associate learned features with human factors, which can be communicated and argued about. One such example would be to interpret the role of specific weather patterns in a classification outcome.

Field: Computational Physiology / Artificial Intelligence
6. Computationally-Driven Contact-Free Physiological Measurements

This PhD thesis aims to conceptualize, develop, and test the next generation of contact-free physiological measurement methods developed by the University of Houston’s Computational Physiology Lab (CPL). The thesis will also exploit the expertise of NCSR Demokritos' SKEL Lab on data management and data analytics, using machine learning. Such contact-free methods are expected to be the standard physiological measurement methods in the next decade.
Why it is important: Research on contact-free physiological measurements is an area carrying significant scientific, social, and economic impact. The CPL has pioneered these measurements, reporting for the first time methods to measure heart rate, breathing rate, and electrodermal activity through thermophysiological imagery of the face. In addition, CPL has developed a state of the art tissue tracking algorithm to render all these measurements valid even in the presence of facial motion. Since then, this area has received tremendous attention, which is amplified by the advent of miniature and cheaper thermal imaging sensors.

Field: Artificial Intelligence/ Text Mining / Bioinformatics
7. Extracting knowledge from multiple modalities in biomedical publications

In this PhD thesis we will study methods and algorithms for extracting, analysing and fusing knowledge from multiple modalities in biomedical publications.
Why it is important: Biomedical text mining deals with the analysis of biomedical text, such as publications. Most of the work done so far in this area examines text in isolation, ignoring the non-textual parts of the publications (like tables and images), which are often very knowledge-rich. Potential applications of such a text mining process include, among others, biological semantic indexing and/or semantic Question Answering.

Field: Artificial Intelligence / Natural Language Processing / Argument Mining
8. Content Analysis in natural language texts

This PhD thesis aims to adopt state-of-the-art machine learning approaches on tasks related to the identification of communication and rhetoric devises on natural language texts. The field of application can range from a relatively new and exciting challenge in corpus-based discourse analysis, argument mining, which tries to automatically identify human reasoning in unconstrained natural language texts, to the identification of elements of populistic rhetoric, hate speech or types of misinformation (such as fake news), according to the interests of the applicant. However, the focus will remain on how to automatically detect these linguistic devices in texts through the use of machine learning. The PhD candidate will be able to explore theories developed in the humanities for describing relevant rhetoric devices and approaches, based on machine learning and statistical techniques from computational linguistics.
Why it is important: Content analysis of texts and especially discourse analysis has recently emerged as a research area that can help in the analysis, understanding and comprehension of a wide range of societal phenomena, as these are expressed in the social web and especially in user generated content that is ubiquitous, mainly driven by the constant increase in the use of social media. This largely uncontrolled means of communication has brought the opinion of citizens to the forefront, but at the same time it has facilitated the spread of misinformation, “fake news”, conspiracy theories, populistic rhetoric, hate speech, and has amplified public opinions that are ill-founded, misinformed, misguided and in error; elements that can damage, rather than benefit, the society. As a result, the analysis and automated identification of such communication devices in texts has become a necessity with increasing areas of applicability.

Institutions: Dalhousie University & NCSR Demokritos

Field: Big data/ Machine Learning / Knowledge representation
9. Big data analysis for precision medicine

This PhD thesis aims to introduce methods and algorithms to bring together disparate data sources (e.g. scientific literature, demographic, cognitive, images, genomics, EHRs of patients) and fuse the knowledge that will be extracted, to help realize the vision of precision medicine.
Why it is important: Large amounts of heterogeneous medical data have become available nowadays. This introduces an opportunity and a challenge, namely to bring together disparate data sources and fuse the knowledge that will be extracted. Such integrated knowledge could prove particularly valuable for improving the diagnosis and treatment of various diseases. Innovative data analysis and knowledge extraction methods could even allow the design of effective personalised treatments, in the wider context of precision medicine. While data are becoming ever easier to obtain, they are difficult to process using common database management tools or traditional data processing applications. The challenges include capturing, storing, integrating, searching, sharing, and analysing.

Field: Artificial Intelligence / Data Mining / Big Data
10. Data mining for forecasting anomalies in maritime data

The main objective of this PhD proposal is to develop data mining models that are able to anticipate anomalous deviations in the maritime domain, combining vessel trajectories with other useful dynamic and static information, such as weather forecasts, bathymetry data and geographical areas of interest (such as protected areas). The work to be carried out will involve 3 main questions/topics: (1) which properties of maritime data should be used/derived in order to facilitate the task of detecting and forecasting anomalies; (ii) which methods should we use to face the fact that anomalies will be rare in these data and thus the forecasting models will have difficulties in facing this imbalanced distribution; (iii) how to properly obtain forecasting models for maritime data streams, and which methods should be used to estimate the performance of these models, given the existing dependencies among the data observations.
Why it is important: Trajectory data is becoming ubiquitous due to the widespread use of devices with geo-reference capabilities and also the profusion of cheap sensors. All these technological advances have led to a considerable growth in the number of data sets with information on the trajectories of different moving objects, like humans, animals or ships. In many application domains, being able to forecast anomalous deviations is of very high importance. In particular, being able to anticipate these deviations with sufficient lead time to allow for preventive actions to be put in place can be highly rewarding. Examples include monitoring elderly people with critical health conditions living at home, monitoring trajectories of endangered marine species, or monitoring the behavior of ships in the Ocean.

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