Αναλυτικές πληροφορίες για Υποτροφίες 2021
Institution: Dalhousie University
Field: Big data/ Machine Learning / Knowledge representation
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
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.
Institution: University of Houston
Field: Artificial Intelligence/ Machine Learning
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 / Machine Learning
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: Artificial Intelligence/ Text Mining / Bioinformatics
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.
Institution: Rice University
Field: Artificial Intelligence / Robotics
Considering human behaviour ambiguities in robot motion planning
The goal of this PhD project is to transform the way robots collaborate with humans by enabling robots to take appropriate action even when human behaviour is ambiguous. Possible research directions include strategies for interpreting ambiguities given different modalities of human behaviour, e.g. speech and movement, methods for accumulating past experiences and resolving ambiguities based on the environmental context and previous experience and methods grounding such ambiguities into feasible robot actions.
Why it is important: Human behaviour can often have multiple interpretations, especially if each behavioural modality is treated in isolation. However, if multiple modalities are taken into account along with environmental context and history, such behavioural ambiguities could be eliminated to a degree that allows robots to plan feasible alternative actions and choose the most appropriate among them.
Institution: University of Texas at Arlington
Field: Artificial Intelligence / Human-Robot Collaboration
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.
ΕΝΤΥΠΟ ΑΙΤΗΣΗΣ ΥΠΟΤΡΟΦΙΩΝ 2021