phd thesis visual analytics

VISUAL ANALYTICS AND INTERACTIVE MACHINE LEARNING FOR HUMAN BRAIN DATA

Degree type.

• Doctor of Philosophy
• Computer Science

Campus location

• Indianapolis

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, usage metrics.

• Computer graphics
• Software engineering not elsewhere classified

Welcome to the CVAST Homepage!

The research unit "Visual Analytics" # 193-07 (Centre for Visual Analytics Science and Technology (CVAST)) is part of Vienna University of Technology (TU Wien) , Faculty of Informatics , Institute of Visual Computing and Human-Centered Technology . CVAST conducts research and provides teaching in Visualization (Information Visualization, Visual Analytics). Read More

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Visual Analytics of Spatial Events: Methods for the Interactive Analysis of Spatio-Temporal Data Abstractions

I’m happy to share that I’ve successfully defended my Ph.D. thesis with the title “Visual Analytics of Spatial Events: Methods for the Interactive Analysis of Spatio-Temporal Data Abstractions”.

Technological advances, especially in remote sensing, GPS sensors, and computer vision and camera-based tracking, now allow for a collection of spatio-temporal data on an unprecedented scale. These massive datasets raise the problem of how subject matter experts can derive useful knowledge from them and how these datasets can be visualized without leading to overcrowded and cluttered displays. For that, suitable data abstractions are required on the one hand and the integration of subject matter experts in the analysis process instead of solely relying on automatic methods on the other hand.

My dissertation addresses both of the aforementioned problems two-fold: First, spatial events, which are objects with a limited temporal existence with an additional associated spatial position, are identified as a suitable data abstraction for visualization and further analysis. Additionally, complex spatial events are introduced, which occur in domains where events not only have a spatial and temporal location, but where the events can have semantic interrelationships, have interdependencies with other objects on other objects, or are constricted by outside rules and influences. Finally, Visual analytics is employed to ensure the integration of subject matter experts in the analysis process of (complex) spatial events via a combination of automatic and visual analysis methods with a tight coupling through human interaction.

The suitability of visual analytics to analyze spatial events is successfully demonstrated in a diverse range of domains and the current state of the art is broadened by several contributions:

• Via traditional methods such as glyph-based map representations that are extended to enable the subject matter experts to use their insights from the visualizations to steer the model building process further.
• With completely newly developed methods such as query-by-sketch interfaces based on real-world metaphors that offer model visualizations that enable the subject matter experts to evaluate findings of the underlying models.
• Furthermore, new approaches are presented that enable the users to offload the complexity of defining complex spatial events and query construction unto the system with the help of visual query languages.

Our visual query interface is based on the real-world metaphor of a magnetic tactic board, widely used in practice for team coaching and analysis in team sports including soccer. Users efficiently and effectively specify team situations by dragging & dropping magnets representing players, such as G oalkeeper or D efender, or the ball to pitch positions and simplified sketch interactions. The above example shows two aggregated slow build-up play situations retrieved with our approach. Aggressive playing behavior of the F orward to immediately pressure the defender is clearly visible in both.

You can find my thesis in KOPS: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-2vs55lce7cin0

Daniel Seebacher

Im a postdoctoral researcher at the Data Analysis and Visualization group of Daniel Keim at the University of Konstanz since 2022. My research interests include the visual analysis of spatio-temporal events and their context. Concrete research examples include the analysis of the spread of invasive species, the study of intra-city meteorological phenomena, or the study of actions and interactions in sports such as soccer or tennis.

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A visual analytics approach for visualisation and knowledge discovery from time-varying personal life data

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PhD Position in AI and Visual Analytics for Multi-Modal Summarization

University of Amsterdam

• Department: Faculty of Science
• City: Amsterdam
• Country: Netherlands
• Posted on: Friday, 08 December 2023
• Application Deadline: Thursday, 01 February 2024

Job Description

Are you interested in performing cutting-edge research in Artificial Intelligence (AI) and visual analytics for social good? Visual analytics is the science of making sense of data using visualization and modeling techniques. The Informatics Institute at the University of Amsterdam is looking for an ambitious PhD student to integrate AI and visual analytics in summarizing multi-modal data in the public health domain. Your research is part of the Multimedia Analytics (MultiX) lab with a strong focus on dealing with multi-modal data.

Nowadays, there is a lot of multi-modal data related to public health in local regions, such as citizen reports/complaints, social media streams, camera monitoring footage, and environmental sensor readings. There is a strong need to build AI-driven visual analytics tools that can summarize these data to help local stakeholders understand the patterns and events that are currently happening, such as air pollution, disease outbreaks, and flooding. Besides, summarization can be used to encourage citizens to monitor their surroundings, which can lead to a sense of community, autonomy, and empowerment. For example, when citizens receive an event notification via apps/emails, they can click on the notification to go to an AI-driven visual analytics tool that shows the summary with different ways for users to provide feedback interactively.

This research topic poses at least the following challenges:

• Summarizing evolving multi-modal data is difficult.  Public health data are often multi-modal. Traditional techniques typically use different pipelines for different types of data, making it difficult to connect findings. Moreover, public health data is dynamic and evolving, which means the patterns and events can shift quickly over time (i.e., concept drift). Most techniques deal with a fixed dataset, which may not be suitable for evolving data.
• Improving summarization based on stakeholder feedback is hard.  Public health data depends on context. Thus, the summarization model must consider knowledge and feedback from stakeholders. However, stakeholders can provide different feedback interactively (e.g., labels, comparisons, scalars, rankings, corrections, natural language). It is hard to let the system learn from those and have the right balance between their richness and efficiency.
• Representing diverse stakeholder values is challenging.  Public health data can be biased due to limitations in sampling strategies or stakeholders’ prior beliefs. Moreover, stakeholders can have misaligned and even conflicting values when providing feedback. There is a need to provide bias and fairness measurements for the patterns and events in the summarization for laypeople who have limited technical backgrounds.

What are you going to do?

You will conduct research, experiments, and empirical studies to address the challenges that are mentioned above (or other related challenges).

Your tasks and responsibilities:

• Conduct research and experiments in integrating AI and visual analytics to summarize evolving multi-modal data;
• Create and deploy a web-based visual analytics tool that can show the summarized insights to stakeholders and enable them to provide different forms of feedback interactively;
• Conduct research and experiments in investigating human-in-the-loop AI techniques to improve the summarization model using continuous stakeholder feedback;
• Inspect how AI and visual analytics can be used to help stakeholders identify bias and fairness issues in the summarization;
• Conduct empirical studies using a mix-method approach (i.e., both qualitative and quantitative) to evaluate the visual analytics tool with stakeholders;
• Publish and present research in international peer-reviewed conferences (e.g., ACM WWW, AMC MM, IEEE VIS, ACM CHI, ACM KDD, ACM IUI, AAAI) and/or journals (e.g., ACM TIIS, IEEE TVCG);
• Pursue and complete a PhD thesis within the appointed duration of four years;
• Assist in teaching activities, such as teaching labs/tutorials in courses and supervising bachelor/master students;
• Carry out administrative tasks in the research group, such as scheduling and planning activities for group meetings.

What do you have to offer?

Your experience and profile:

• A relevant master’s degree to the PhD topic of interest;
• Research experiences in artificial intelligence, visual analytics, or related topics;
• Solid programming skills with experience using Python and machine learning frameworks;
• The willingness to work collaboratively with other researchers and external stakeholders;
• Professional command of English (both verbal and written).

Our ideal candidate has an artificial intelligence and/or visual analytics background. It is a preference if you additionally speak professional Dutch, have experience in developing/deploying web-based applications, or have co-designed tools with stakeholders.

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The preferred starting date is as soon as possible. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.

The gross monthly salary, based on 38 hours per week, ranges between € 2,770 in the first year to € 3,539 in the last year (scale P). UvA additionally offers an extensive package of secondary benefits, including 8% holiday allowance and a year-end bonus of 8.3%. The UFO profile PhD Candidate is applicable. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The  Collective Labour Agreement of Universities of the Netherlands  is applicable.

Besides the salary and a vibrant and challenging environment at Science Park we offer you multiple fringe benefits:

• 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January;
• multiple courses to follow from our Teaching and Learning Centre;
• a complete educational program for PhD students;
• multiple courses on topics such as leadership for academic staff;
• multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses;
• 7 weeks birth leave (partner leave) with 100% salary;
• partly paid parental leave;
• the possibility to set up a workplace at home;
• a pension at ABP for which UvA pays two third part of the contribution;
• the possibility to follow courses to learn Dutch;
• help with housing for a studio or small apartment when you’re moving from abroad.

Are you curious to read more about our extensive package of secondary employment benefits, take a look  here .

The  University of Amsterdam  (UvA) is the Netherlands' largest university, offering the widest range of academic programmes. At the UvA, 42,000 students, 6,000 staff members and 3,000 PhD candidates study and work in a diverse range of fields, connected by a culture of curiosity.

The  Faculty of Science  (FNWI) has a student body of around 8,000, as well as 1,800 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain.

The mission of the  Informatics Institute  (IvI) is to perform curiosity-driven and use-inspired fundamental research in Computer Science. The main research themes are Artificial Intelligence, Computational Science and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component.

The  Multimedia Analytics Lab Amsterdam  (MultiX) performs research on multimedia analytics by developing AI techniques for getting the richest information possible from the data, visualizations, and interactions surpassing human and machine intelligence. We blend multi-modal data in effective interfaces for applications and social impact in public health, forensics and law enforcement, cultural heritage, and data-driven business.

Want to know more about our organisation? Read more about  working at  the University of Amsterdam.

Any questions?

Do you have any questions or do you require additional information? Please contact:

• E:  Yen-Chia Hsu , Assistant Professor
• E:  Marcel Worring , Full Professor

Application Instructions

If you feel the profile fits you, and you are interested in the job, we look forward to receiving your application. You can apply online via the button below. We accept applications until and including 1 February 2023.

Applications should include the following information (all files apart from your CV should be submitted in  one single pdf file ):

• A detailed CV including the months (not just years) when referring to your education and work experience;
• A letter of motivation (at most two pages) explaining why you are interested in this position, how your experience fits into this position, and how you would approach the PhD project;
• A complete transcript of records for all university Bachelor and Master courses that you have taken (including grades and explanation of the grading system);
• A link to your master’s thesis if it is available online (else, please include an abstract);
• A list of projects or publications you have worked on, with brief descriptions about them and your contributions (at most one page);
• A link to one writing sample available online (e.g., in the university library, open online repository, Google Drive), such as your master’s thesis, term paper, or publication.

We do not ask for a referee list or reference letters in the initial application (please do not include them in your application document), but we may ask for them at the later stage of the interview.

Please make sure to provide ALL requested documents mentioned above. You can use the CV field to upload your resume as a separate pdf document. Use the Cover Letter field to upload the other requested documents, including the motivation letter, as  one single pdf file .

A knowledge security check may be part of the selection procedure. (for details:  National knowledge security guidelines ) .

Only complete applications received within the response period via the link below will be considered. Please don’t send any applications by email.

We will invite potential candidates for interviews soon after the expiration of the vacancy.

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Visual Analytics for Data-centric Machine Learning

Last updated on 2024-05-22

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• Chair of Cartography and Visual Analytics
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Map-based Dashboard for Social Environment Understanding (2022)

Contact: Dr.-Ing. Chenyu Zuo

Electronic Version of PhD Thesis

This thesis aims to support stakeholders in understanding social environments with map-based dashboards, representing an overview of the related factors and their spatial distributions and enabling users to explore for insights. To design effective and efficient dashboards, the author proposed a design framework including five components: design goals, users' cognitive tasks, data, interface, and users' feedback.

Data-driven design and analysis of map-based storytelling (2022)

Contact: Dr.-Ing. Edyta Bogucka

This thesis is dedicated to the development of map-based storytelling. It involves two essential parts of data-driven explorations. The first part explores the most and the least prevalent patterns in map-based storytelling in several representative news media. The second part investigates the practical implications of storytelling theories through hands-on design. These two directions of top-down and bottom-up exploration demonstrate a synergetic effect between cartography and journalism.

Spatial Learning with Mixed Reality-based Navigation (2022)

Contact: Dr.-Ing. Bing, Liu

The emerging mixed reality (MR) is promising for navigation, especially indoor navigation. However, the detrimental effect of navigation apps on spatial learning is criticized. This thesis explores and confirms the possibility to balance navigation efficiency and spatial learning during MR-based navigation from users’ perception, the interface design, and the environment’s influence perspectives.

Social Sensing (2020)

Perception of social-event-induced human behavior from geotagged social media data.

Contact: Dr.-Ing. Ruoxin Zhu

The population coverage of public transit systems is an important indicator of public transit accessibility. Traditionally, the assessment of transit catchment areas is mainly focused on walking as the access mode. The recent emerging dockless shared bikes are widely used for connecting with public transit systems and provide new chances to expand the population coverage of public transit. This project aims to assess how dockless shared bikes could expand the transit catchment areas using massive bike trajectories. To achieve this aim, the project has three objectives: 1) proposing a fast method to generate network-based transit catchment areas for non-motorized transport; 2) proposing methods to measure biking distances from high-detailed bike trajectories; 3) conducting cases studies to evaluate the effectiveness of the proposed methods and discussing policy implications for the planning of public transit and dockless shared bikes.

Modeling of Public Transit Accessibility Driven by Spatial Movement Data (2020)

Contact: Dr. Ing. Diao Lin

Abstract: 

This dissertation focuses on investigating the bike-metro integration using spatial movement data and supporting a systematic assessment of accessibility to public transit. Specifically, it serves three research objectives: 1) exploration of biking distances at individual transit stations from trajectory and smart card data, 2) investigation of transit catchment area to raise the public awareness of the transit accessibility at a general level, and 3) inspection of accessibility constrained by crowdedness at a fine-grained level.

Approaching a collective place definition from street-level images using deep learning methods (2019)

Contact: Dr. Ing. Hao Lyu

This work addresses the challenge of understanding a place in GIScience by investigating visual and spatial (semantic) property in voluntarily collected images with deep learning methods. Two place representations are proposed to unify these properties in a probabilistic perspective of understanding places. The proposed computational models which are based on comparative learning and variational autoencoder are proved to be able to learn the probabilistic place representations from image data.

Visualizing Uncertainty in Reasoning - A Bayesian Network-enabled Visual Analytics Approach for Geospatial Data (2019)

Contact: Dr. Ing. Ekaterina Chuprikova

The growing importance of data-driven science and advances in computational capacity offer new opportunities for the analysis and visualization of geospatial and heterogeneous data. This dissertation addresses the challenges of analytical reasoning under conditions of uncertainty when working with spatial data. It serves three research objectives: (1) to evaluate the feasibility of the Bayesian Network in representing conditional dependencies among heterogeneous spatial data; (2) to implement visual analytics scenarios that can demonstrate human-data discourses; (3) to build a prototype of a Bayesian Network-enabled visual analytical system dedicated to geospatial data classification tasks.

Conflation of Road Networks from Digital Maps (2016)

Contact: Dr. Ing. Andreas Philipp Hackelöer

Road Network Conflation is concerned with finding accurate mappings between geographical structures of road networks. This thesis establishes Road Network Conflation within a taxonomy of georeferencing methods and classifies and compares common approaches to the problem. A novel approach called Iterative Hierarchical Conflation (IHC) is introduced, which systematically accounts for the resolution of ambiguities. The IHC is evaluated using several samples from digital maps of different vendors. Results show that the IHC offers advantages especially in terms of correctness, speed and complexity.

Labeling Spatial Trajectories in Road Network using Probabilistic Graphical Models (2016)

Contact: Dr. Ing. Jian Yang

Labeling spatial trajectories, such as map matching, activity recognition, can ease the utilization of the imprecise and semantic poor spatial trajectories for location-aware applica-tions. This thesis studies the problem from a unified perspective using map matching and taxi status inference. Comprehensive probabilistic models are learned from the training data using a chain structure graphical model with feature selection, which are tested to be effective and feasible on a real world dataset.

Visual Analysis of Large Floating Car Data - A Bridge-Maker between Thematic Mapping and Scientific Visualization (2016)

Contact: Dr. Ing. Linfang Ding

This thesis aims to bridge the gaps between thematic mapping and scientific visualization and to achieve their synergetic effects for the visual analysis of big data. Firstly, a systematical comparative study of thematic cartography and scientific visualization is conducted. The study shows that these two disciplines reveal different visual analytical levels  and are mutually complementary. Next, extensive experiments of visually analyzing massive real-world taxi floating car data (FCD) have been carried out. The experiment results demonstrate that the techniques from thematic mapping and scientific visualization can strongly support users to win insight into the movement data.

Event Cartography: A New Perspective in Mapping (2016)

Contact: Dr.-Ing. Nina Polous

In this research, the concept of mapping goes beyond the principle of mapping an object as a conceptual geographic entity with a distinct spatial, temporal and attributive identity. The main goal is to present a conceptual model for managing geo-knowledge which handles real world dynamisms. It uses a generic event-oriented perspective to implicitly represent causal relationships among different components of a Spatio-Temporal Information System. From this new perspective, the objects in space and time are considered merely as information elements of the events, which are connected to other event elements through internal or external processes.

Dynamics of spatially extended phenomena (2014)

Contact: Dr.-Ing. Stefan Peters

This thesis focuses on the visual exploration of a specific type of moving geoobjects, namely the spatially extended objects or phenomena. Visual analytical approaches are developed and implemented to study the dynamics of the spatio-temporally evolving polygons. The lightning data are chosen as a real-world case. In addition to a generic concept for the movement analysis of spatially extended objects, the thesis put forward a number of synchronized cartographic and non-cartographic visual analytical approaches for the clusters.

Concise Image Maps - A Design Approach (2014)

Contact: Dr.-Ing. Christian E. Murphy

An image map is a composition of remote sensing imagery and cartographic symbolisation. This work revisits the concept of image maps and shows that the conventional two-tiered structure can be extended by the concise image map design, according to which a differentiated visual hierarchy can be established. Therefore, design strategies are developed that address the radiometric design of raster imagery in the same manner as the graphical design of map symbols. User tests evaluate several concise image map design strategies that prove to be more effective and user friendly.

A Congruent Hybrid Model for Conflation of Satellite Image and Road Database (2013)

Contact: Dr.-Ing. Jiantong Zhang

This thesis is devoted to the conflation of two heterogeneous data sources - road vectors and geo-referenced images. The contributions of the Congruent Hybrid Model (CHM) include:1) a linear feature extraction approach, which consists of Elastic Circular Mask (ECM) algorithm and the Genetic Algorithm (GA)-based grouping approach;2) a Sparse Matching Algorithm (SMA) approach; and 3) a performance evaluation of two transformation functions. The CHM model can be used directly in the geovisualization applications, and with some modifications it is also suitable for the classical georeferencing problem.

Nicht-Photorealismus in der Stadtmodellvisualisierung für mobile Nutzungskontexte (2013)

Contact: Dr.-Ing. Mathias Jahnke

Die Visualisierungen dreidimensionaler Stadtmodelle erschließen bisher nicht das Potential, der kombinierten geometrisch semantischen Informationsdarstellung. Der aus dem Nicht-Photorealistischen-Rendering bekannte Ansatz der Informationsreduzierung durch Abstraktion lässt sich mit kartographischen Gestaltungsprinzipien kombinieren und liefert unter Einbeziehung von Nutzerpräferenzen neue Formen der Geo-Informationskommunikation auf der Basis von Stadtmodelldaten.

Enrichment of routing map and its visualization for multimodal navigation (2012)

Contact: Dr.-Ing. Yueqin Zhu

This thesis is dedicated to a novel approach for the design of customized routing maps which demonstrate the route along with the fast rendering of a right amount of routing-relevant information that matches the cognitive capacity of the user on route. Given a route with mono- or multimodality, the proposed approach first estimates the salience of individual mapping objects by combining the passive salience from internal characteristic of spatial data and the active salience from the participant. The achieved results are visualized in multi-scale routing map with a dynamic labeling algorithm. The proposed approach is implemented as web-based services. Its feasibility has been verified in several experiments.

Kartenicons im interkulturellen Vergleich (2011)

Contact: Dr. rer. nat. Stephan Angsüsser

Electronic Version of PhD Theses

Das übergeordnete Ziel dieser Arbeit ist ein Beitrag zur kulturvergleichenden Kartographie. Ausgehend von der Annahme, dass Unterschiede zwischen den Karten verschiedener Kulturen bestehen, ist eine bestimmte Art von Kartenzeichen als Beispiel herangezogen worden. Dabei handelt es sich um Kartenicons, deren Gemeinsamkeit normalerweise in ihrer geringen Größe und weitgehenden Isoliertheit besteht. Zu deren Analyse wurde auf der Basis bestehender Ansätze ein neues Zeichenmodell entwickelt. Für jedes der 1016 Kartenicons (540 deutsche und 476 chinesische) wurden 8 Attribute bestimmt, deren Vergleich schließlich 25 wesentliche Unterschiede zwischen den beiden Länderauswahlen ergab. Um diese zu erklären, wurde versucht, kulturelle Eigenschaften heranzuziehen. Schließlich führte dies zur Formulierung von 38 Hypothesen über mögliche Beziehungen zwischen Eigenheiten der deutschen bzw. chinesischen Kultur und Eigenheiten der in diesen beiden Ländern produzierten Kartenicons.

Kartographische Anreicherung von Gebäudefassaden mit thermalen Bilddaten (2011)

Contact: Dr.-Ing. Holger Kumke

Die vorliegende Arbeit behandelt die visuelle Anreicherung thermaler Daten auf Gebäudefassaden imstädtischen Raum. Wärmestrahlung, messtechnisch als bildhafte Thermogramme erfasst, liefern neuenicht sichtbare Informationen über den Gebäudezustand und dienen als Rohdaten für diekartographische Aufbereitung zu thermalen Fassadenkarten für planare 2D wie auch kartenverwandteräumlich virtuelle 3D Darstellungen. Aus einer Kombination der bekannten Gestaltungsregeln und derneuen kartographischen Perspektive entstanden system-unabhängige thermale Fassadenkarten, dieals Anregung, Denkanstöße und Ausblick auf temperaturbezogene Darstellungsformen im städtischenRaum und Grundlage für weitere Forschungsarbeit dienen sollen.

Distributed geo-services based on Wireless GIS - a case study for post-quake rescue information system (2011)

Contact: Dr.- Ing. Yimei Liu

A useful application of Wireless GIS is the handling of natural disasters such as earthquakes. This thesis is dedicated to the construction and implementation of a post-quake rescue information system based on open source data and software programs. The emphasis is laid on the assessment of losses in the disaster area, estimation of collapsing buildings and trapped population, and efficient transmission of all the rescue-relevant information. The realized workflow of using open source data and software programs to develop distributed geo-services for rescue purposes is independent of official data sources, therefore, flexible enough to react on emergency situations.

Generalization of Road Network for an Embedded Car Navigation System (2011)

Contact: Dr.- Ing. Hongbo Gong

Automatic map generalization serves to reduce the amount of data to speed up the mapping process or to ensure the legibility of small scale maps. This thesis deals with the task of automatic selection of road networks for the application of visualization and route planning in an embedded car navigation system. Based on an intensive analysis of the embedded system in terms of storage capacity, the display screen and the necessary computing power in real time, two special constraints - connectivity and network density – are introduced. A concept for the semantic-driven path selection for the map display and the optimal route planning is developed and implemented with test data from Germany and China.

Data Model and Algorithms for Multimodal Route Planning with Transportation Networks (2011)

Contact: Dr.- Ing. Lu Liu

Determining a best route in highly developed complex transportation networks is not a trivial task, especially for those who are unfamiliar with the local transportation system. Multimodal route planning that aims to find an optimal route between the source and the target of a trip while utilizing several transportation modes is essential to intelligent multimodal navigation services. Although the task originates from the field of transportation, it can be abstracted as a general form independent of the domain-specific details on the underlying data model and algorithms. This research work is therefore dedicated to a general approach of modeling the multimodal network data and performing optimal path queries on it. The weighted digraph structure can well represent the fundamental static networks. For each mode, there is one corresponding mode graph. These graphs constitute the Multimodal Graph Set as a key component of the overall multimodal network data model. In comparison with the traditional mono-modal problem, another key component necessary in the modeling of multimodal route-planning problem is mode-switching actions. In this work, such actions are described by Switch Points which are somewhat analog to plugs and sockets between different mode graphs. Consequently, it is possible to plug-and-play a Multimodal Graph Set by means of Switch Points. On the basis of the multimodal network data model, the multimodal route-planning problem is categorized into two types and formalized as the multimodal shortest path problem on the Plug-and-Play Multimodal Graph Set. It turns out that the solutions for these two types of problem are equivalent if the input mode list for the first type is transformed into its matrix expression. When applying the general multimodal route-planning approach to a specific application domain, a rule-based inferring process is necessary to determine whether a mode sequence is reasonable or not. Performance evaluations on the integrated navigation dataset have verified the efficiency of the proposed approach.

Driver Behaviors on Different Presentation Styles of Traffic Information (2010)

Contact: Dr.- Ing. Masria Mustafa

Road traffic information has been one of the important elements for traffic information systems. The information can be found on the road where Variable Message Sign (VMS) and other platforms have been widely utilized for such application. These overwhelming majorities of traffic information sources provide real-time traffic information, aiming at helping drivers make better decisions on choosing a correct traffic route on the basis of current traffic state. However, it is an unusual sight to view the full scenarios of the system with the view from the drivers themselves. Therefore, smartly presenting the information to the road user has a potential to support the road user to receive and interpret the information in a more effective way. Traffic information presented in different styles may enhance the attractiveness of the information itself. Considering this, we design this study to find out whether there is a refined relationship between the specific presentation style and the driving behavior.  As a starting point, this study tested the assumption that the probe vehicle could provide reliable and sufficient amount of data that could represent travel time information which then can be transformed into various presentation style. VISSIM 5.0 is used to generate travel time data on a hypothetical network. Average travel time on links are analyzed for various percentages of probe vehicles and compared to the ‘true’ average travel time using ‘bootstrapping’ technique. ArcGIS designed for use by transportation professionals to display the results of travel time provided by probe in a more understandable visual fashion (color coded design). Later, user testing upon preference of the drivers towards different types of traffic information presentation style is conducted. A picture is often cited to be worth a thousands words and, for some tasks it is clear that a visual presentation such as map is dramatically easier to be used than other textual or spoken description. Visual displays become even more attractive to provide orientation or context, to enable selection of regions and to provide dynamic feedback for identifying change such as dynamic traffic congestion map. In what concerns the visual information, systems can present information using graphics, symbols or even written messages. A stated preference user test is conducted and questionnaires with different types of traffic information presentation style are distributed to the respondents. An underlying question is basically about whether and how the presentation styles of traffic information affect the driver in making their decision. The study addresses a wide range of alternative styles of in-vehicle traffic information as well as stationary information in different driving scenarios (stop and go and congested). The analysis is carried out which contains various trip variables, including route selection characteristics, travel purpose and actual observable traffic conditions en route such as level of congestion, variables pertaining to the information to which is being displayed and also psychological factors based on personal attributes and the experience of the individual drivers. It is assumed that drivers are influenced by these variables and factors of making decisions whether to acquire and refer to traffic information in choosing their route. Our results revealed that in case of in-vehicle information, presentation style of traffic information does not play a significant role for driver’s behavior. As to the preference of presentation style, ‘map with detail building’ came out to be the highest rank. The main reason for this preference is the presence of the buildings which provides additional orientation information. Different behavior patterns could be observed when confronted with more realistic situations. Our observations demonstrate that the drivers are more likely to divert their route only in rush trip. In case of stationary information, again, we found no evidence that presentation style of traffic information does play a significant role for driver’s behavior. As to the preference of presentation style, ‘combination of graphic and text information’ came out to be the highest rank. Our observations demonstrate that the drivers are more likely to divert their route only in rush trip and congested route.

Integration of time-dependent features within 3D city model (2010)

Contact: Dr.- Ing. Hongchao Fan

This thesis presents an object-oriented event-state spatiotemporal data model for storage and management of both semantic and geometric changes of 3D building objects in a city. The data model is mainly composed of two parts: an event model that describes events happened to building objects; and a hierarchical spatial data model that describes 3D geometries and semantics of building objects including their valid time span. In this way, histories of building objects are modeled. The data model can be “double indexed” by events happened to objects and by objects involved in events. Correspondingly, queries can be triggered by both events and objects. On this base, a set of spatiotemporal queries are proposed. The spatiotemporal data model proposed in this work combines the advantages of event-based model and object-based spatiotemporal data model. On one hand, dynamic processes are modeled as events with their types/classes, locations, time points/durations, modes of the processes, and the involved city objects. On the other hand, the life of an object is represented by a time-ordered sequence of its states and the dynamic processes indicating how the object changes from one state to another. The approach of storing events and city objects separately reveals a number of benefits: (i) the multiple storage due to n-to-m relations among events and objects are avoided, (ii) the spatiotemporal data model is double-indexed. Events and 3D objects can be queried independently and efficiently. In addition, the proposed spatiotemporal data model takes the hierarchy and inherent relations between events and objects into account, so that both events and 3D objects can be represented at different levels of detail. 

Methods and Implementations of Road-Network Matching (2009)

Contact: Dr.-Ing. Meng Zhang

Data matching is one of the fundamental measures that helps make different data sets interoperable. This thesis is devoted to a new contextual matching approach for road networks. This automatic matching process is based on the Delimited-Stroke-Oriented algorithm and flanked by three assistant methodologies: matching guided by 'structure', matching guided by 'semantics', and matching guided by 'spatial index': Being supported by the extendable delimited strokes, network-based matching and the three assistant methodologies, the contextual matching approach is able to handle geometrical, topological and semantic information in a large matching environment and provide a considerably improved matching performance in terms of ‘automatic matching rate and certainty', 'high computing speed', and 'robustness and generic nature'. Due to its large potentials of enriching mega data sets, the contextual matching approach is being commercialized.

Attention-Guiding Geovisualisation: A cognitive approach of designing relevant geographic information (2008)

Contact: Dr. rer. nat. Olivier Swienty

It is a delicate task to design suitable geovisualisations that allow users an efficient visual processingof the depicted geographic information. In digital era, such a design task is subject tothree major challenges: the ever growing amount of geospatial data at various levels of detail,the diversified applications of that data, and the continuously expanding range of display sizes.These challenges are guided by the same cognitive scope. Users face an increasing level ofcognitive workload that has a substantial impact on decision-making while processing complexvisual environments.This work tends to enhance the visualisation of relevant geographic information by proposing aconceptual framework for the development of attention-guiding geovisualisation. The mainchallenge is to stimulate a users decision-making and to reduce the cognitive workload by providinghigh responsiveness in specific visual brain areas that are involved in visual geographicinformation processing. Based on theories and research findings in GIScience and cognitiveneuropsychology the research basis of this work is formed by combining utility and usabilityissues of system engineering.The relevance of information is considered as an utility criterion and its cognitively adequatevisualisation as an usability criterion of a system’s acceptability. To enhance utility, irrelevantinformation is separated from relevant information by implementing relevance as a filter. Toenhance usability the design of attention-guiding geovisualisation is adapted to internal visualcharacteristics of visual information processing.Based on the internal structure of visual information processing and biological mechanismsinvolved in visual attention, appropriate cognitive principles and a design methodology arepresented and applied to pixel-based remote sensing satellite image and vectorised maps. Apre-evaluation with a computational attention-model serves as a knowledge base for designingvectorised attention-guiding geovisualisations that are evaluated with a paper and pencil testand the eye-movement recording method.The evaluation results reveal that the proposed attention-guiding design approach significantlyenhances visual geographic information processing and contribute to the overall acceptabilityof geographic information systems and geovisualisations that are needed for fast and accuratedecision-making processes.

Recognition of 3D Settlement Structure for Generalization (2005)

Contact: M.Sc., M.Tech. Jagdish Lal Raheja

This thesis aims at recognizing 3D settlement structures for automatic generalization, an innovative extension to 2D and their simplification based on scale-spaces. The recognition procedure has been divided into three levels namely micro, meso and macro and is based upon individual buildings, buildings in neighborhood and buildings at cluster level having similar properties such as settlement blocks as well as psychophysically perceived groups. Any of these three levels of structure recognition demands that comprehensive information about the buildings should be known a-priori. These buildings, simple as well as complex, are recognized using an Artificial Neural Network (ANN) in a bottom-up approach. It starts with recognizing ground plans of buildings and which in turn, along with other information, are used to recognize different roof types and finally entire buildings are recognized in a similar way. After building recognition, their structure description has been studied in detail, which gives rise to various measurable parameters of individual as well as buildings in neighborhood. These parameters not only characterize individual building but also many spatial relations among them. Structure recognition at clustered level is studied next and it involves the recognition of group of buildings as a whole. The human visual system can detect many clusters of patterns and significant arrangements of image elements. Perceptual grouping refers to the human visual ability to extract significant image relations from lower-level primitive image features without any knowledge of the image content and group them to obtain meaningful higher-level structure. Various perceptual grouping principles have been applied to identify these clusters of groups. After a comprehensive study of structure recognition, their findings are then applied to 3D generalization. Among the various generalization algorithms such as aggregation, displacement, simplification, exaggeration, typification, aggregation is chosen here as it almost uses most of the results from structure recognition. Various constraints resulting from spatial relations have been already found in 2D aggregation. However, unlike in 2D, where there is only one view, the third dimension leads to many additional views and these different views become the source of additional conflicts. Apart from various views, color, texture and other small parts (window, chimney, balcony etc.) of the building also add to the existing constraints. Various additional rules have been obtained based upon these constraints. These rules along with the results of structure recognition have been used to trigger the aggregation operation.

Mobile Cartography - Concepts for Adaptive Visualisation of Spatial Information on Mobile Devices (2004)

Contact: Dr. rer. nat. Tumasch Reichenbacher

This PhD project developed the theoretical and conceptual framework of mobile cartography. The main focus is on the elaboration of adaptive methods for the visualisation of spatial information for mobile usage, i.e. on mobile devices (e.g. PDA). The starting point for the adaptation is the mobile user, his activities and goals, as well as the situation these three are placed in. Usage scenarios helped to implement a prototype geo-service for mobile users based on open-standard formats such as XML, GML, and SVG, which serves as a proof of concept.

EuroVis PhD Award

Eurovis annual award for best phd thesis.

The EuroVis Best PhD Dissertation Award recognizes outstanding dissertations in academic research and development over topics relevant to visualization. The intent of this award is to recognize excellent young researchers in their early career and to highlight visualization research. The award is managed by the Best PhD dissertation committee, constituted by a Chair appointed by the EuroVis Steering Committee.

The current chair is Thomas Ertl.

EuroVis PhD Award 2024: Nominations can be submitted until January 31st, 2024. See below for details.

Best PhD Awards 2024

2024 Committee

Beatriz Sousa Santos, University of Aveiro, PT Rosane Minghim, University College Cork, IR Guy Melançon, University of Bordeaux, F Hans-Jörg Schulz, Aarhus University, DK Torsten Möller, University of Vienna, A Thomas Ertl (Chair), University of Stuttgart, DE

Best PhD Awards 2023

2023 Committee

Rita Borgo, Kings College London, UK Barbora Kozlíková, Masaryk University, CZ Alexandru Telea, Utrecht University, NL Tobias Günther, University of Erlangen, DE Markus Hadwiger, KAUST, SA Thomas Ertl (Chair), University of Stuttgart, DE

Best PhD Awards 2022

2022 Committee

Natalia Andrienko, Fraunhofer IAIS and City University of London Lars Linsen, University of Münster Min Chen, University of Oxford Thomas Ertl, University of Stuttgart (Chair) 

Best PhD Awards 2021

2021 Committee

Jean-Daniel Fekete, Inria, Saclay, France Thomas Ertl, Univ. of Stuttgart, Germany Heike Leitte, Univ. of Kaiserslautern, Germany Jessie Kennedy, Napier University, Edinburgh, Scotland, UK (for now) Ross Maciejewski, Arizona State University, USA Andreas Kerren, Linköping University and Linnaeus University, Sweden Christophe Hurter, French Civil Aviation University, Toulouse, France

Best PhD Awards 2020

2020 Committee

Jean-Daniel Fekete, Inria, FR (chair) Stefan Bruckner, University of Bergen, NO Ingrid Hotz, Linköping University, SE Robert S Laramee, University of Nottingham, UK Gerik Scheuermann, University of Leipzig, DE Julien Tierny, Sorbonne University, FR

Best PhD Awards 2019

2019 Committee

Jean-Daniel Fekete, Inria, FR (chair) Niklas Elmqvist, Univ. of Maryland, US Miriah Meyer, Univ. of Utah, US Berhard Preim, University of Magdeburg Jarke van Wijk, Eindhoven University of Technology, NL

Best PhD Awards 2018

2018 Committee

Jean-Daniel Fekete, Inria, FR (chair) Helwig Hauser, Univ. of Bergen, NO Niklas Elmqvist, Univ. of Maryland, US Hans Hagen, Univ. of Kaiserslautern, DE Heidrun Schumann, Univ. of Rostock, DE Jarke van Wijk, Eindhoven University of Technology, NL

The student’s advisor should nominate the candidate using the online form . The package must include:

• A nomination letter written by the student’s advisor, which includes:
• the name, email address, and phone number of the advisor,
• the name, email address, and CV of the candidate, and
• a one-page summary of the significance of the dissertation (references to papers should be provided on an extra sheet)
• A copy of the dissertation.
• Optional additional letters of recommendation or assessments on the candidate thesis, such as reviewing or defense reports, can also be attached to the submission.

For questions, contact Thomas Ertl ( [email protected] )

Submission deadline

January, 31st 2024

Eligibility

All PhDs from the European visualization community (e.g., through contributions to the EuroVis conference) that defended and get awarded the degree of Doctor between Jan 1, 2022 and Dec 31, 2023 are eligible for the 2024 competition.

There is no limitation on the number of nominations that may be made by a university.

Invited STAR

New, starting with the 2023 award : Aligned with the Eurographics PhD Award, the awardees of the EuroVis PhD Award will offered the opportunity to publish the state of the art section of their thesis as a STAR in the Computer Graphics Forum Journal.

Selection Procedure

Four to six recognized members of the EuroVis community, selected by the Chair, will form a review committee to thoroughly review and assess the dissertation submissions. The committee will judge the dissertations based on their intellectual merit, technical depth, and presentation quality.

The committee is selected with prominent members of the EuroVis community. Their names are public to foster transparency and attest of the value of the awards. The selection is done after all the applications are received to avoid hard conflicts, i.e. advisors of applicants in the committee. The chair cannot be conflicted either so none of his former PhD students can apply during his term. Soft conflicts are possible: a committee member can be part of the institution of an applicant, either during the PhD or after, or have co-authored with the applicant. In that case, the committee member will not be able to speak about the application during the discussion and can decide or be asked to leave the discussion when it concerns the applicant in soft conflict. Prominent researchers are used to handling these cases when they participate in a selection or prize jury.

The selection is handled in two to three meetings, usually conducted through a videoconferencing system:

• They agree on the selection process, on the eligibility of the applicants, and get assigned four PhDs, each PhD being reviewed by two committee members. They will have to read and score them according to the scientific contribution, difficulty of the problem addressed, originality of the solution, quality of the writing/presentation, potential/effective impact, number of EuroVis publications. The citations according to Google Scholar are also collected, as well as the duration of the PhD, that varies widely throughout the institutions and countries. The information gathered is used to provide factual information to the debate, no automatic ranking or scoring is used to filter out  applicants.
• The committee meets again and discusses each of the PhDs. In the end, a shortlist of 2-6 PhDs is selected from intense discussions. A major rule is to remain positive about all the PhDs. Each member of the committee needs to complete their reading of all the shortlisted PhDs for the next meeting and provide a full ranking (except for the soft conflicts obviously).
• The committee meets and compares the ranking, discussing the differences in the ranking to achieve a consensus. It can decide to select up to 3 PhDs to be awarded.

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We continuously and permanantly offer topics for Bachelor and Master theses within the field of Visualization and Computer Graphics with a focus on  Interactive Visual Data Analysis   (incl. Scientific Visualization, Medical Visualization, Information Visualization, Visual Analytics, and Visual Data Science). For sample topics, please have a look at the list of completed theses below or check out our LearnWeb site listing currently available topics and fields of potential topics. If you are interested in pursuing a thesis in this field, please contact Prof. Linsen .

If you are writing a thesis with us, please consider using our template . Here is the LaTeX source code .

• Quynh Quang Ngo: Visual Similarity Analysis for Dynamic Systems, WWU, 2020.
• Muhammad Jawad: Interactive Visual Analysis of Spectroscopy Imaging Data, WWU, 2020.
• Ali Sheharyar: Visual Analysis of Regional Myocardial Motion Data, WWU, 2020.
• Bojan Kocev: Modeling, Estimating, and Visualizing Spatial and Temporal Uncertainty for Image-guided Therapy, Jacobs University, 2020.
• José Matute: Visual Analysis of Heterogeneous Medical Data for Cohort Studies, WWU, 2020.
• Gordan Ristovski: Uncertainty in Medical Visualization, Jacobs University, 2017.

Andra Pascale: Physically Accurate Chewing Simulation for Analyzing Restorations, Jacobs University, 2017.

Muhammad Laiq Ur Rehman Shahid: Upper Airway Segmentation and Interactive Analysis to Investigate Obstructive Sleep Apnea in a Cohort Study, Jacobs University, 2016.

Philipp Last: Analysis of Automatic Identification System Data for Maritime Safety, Jacobs University, 2016.

Alexey Fofonov: Visual Analysis of Multi-run Spatio-temporal Simulation Data, Jacobs University, 2016.

Ahmed Al-Taie: Uncertainty Estimation and Visualization in Segmenting Uni- and Multi-modal Medical Imaging Data, Jacobs University, 2015.

Ronak Etemadpour: Human Perception in Using Projection Methods for Multidimensional Data Visualization, Jacobs University, 2013.

 Sherin Al-Shbat: Decoupling Mesh and Data Representation for Geo-spatial Data Visualization, Jacobs University, 2012.

Steffen Hauth: Tool Path Generation for Finishing Processing, Jacobs University, 2011.

Tran Van Long: Visualizing High Density Clusters in Multidimensional Data, Jacobs University, 2010.

 Tetyana Ivanovska: Efficient Multichannel Image Partitioning: Theory and Application, Jacobs University, 2009.

 Paul Rosenthal: Direct Surface Extraction from Unstructured Point-based Volume Data, Jacobs University, 2009.

• Alexander Kumpf: Data-driven Ensemble Visualization, TU Munich, 2020.
• Nils Lichtenberg: Abstraction of Bio-medical Aurface Data for Enhanced Comprehension and Analysis, Universität Koblenz-Landau, 2019.
• Florian Weiler: Computational Tools for Objective Assessment in Neuroimaging, Jacobs University, 2019.
• Matthew Anthony Thomas van der Zwan: Visual Analytics of Multidimensional Time-dependent Trails with Applications in Shape Tracking, University of Groningen, Netherlands, 2018.
• Aaron Scherzinger: Data Exploration in Natural Sciences using Machine Learning and Scientific Visualization, Westfälische Wilhelms-Universität Münster, 2018.
• Christian Schumann: Visualization and Heuristic Modeling for Planning Minimally- and Non-invasive Tissue Ablation, Jacobs University, 2016.
• Mihaela Jarema: Visual Analysis of Statistical Aspects of Ensemble data, Technische Universität München, 2016.
• Renato Rodrigues Oliveira da Silva: Visualizing Multidimensional Data Similarities Improvements and Applications, University of Groningen, Netherlands, 2016.
• Teodora Chitiboi: Myocardium Segmentation and Motion Analysis from Time-varying Cardiac Magnetic Resonance Imaging, Jacobs University, 2016.
• Danilo Barbosa Coimbra: Multi-dimensional Projections for the Visual Exploration of Multi-media Data, University of Groningen, Netherlands, 2015.
• Alireza Rezaei Mahdiraji: A Query Language for Scientific Meshes, Jacobs University, 2015.
• Kai Lawonn: Illustrative Visualization of Medical Data Sets, Otto-von-Guericke-Universität Magdeburg, 2014.
• Eva Monclus Lahoya: Advanced interaction techniques for medical models, Universitat de Barcelona, Spain, 2013.
• Christian Rieder: Interactive Visualization for Assistance of Image-guided Interventions, Jacobs University, 2013.
• Christian Hansen: Software Assistance for Preoperative Risk Assessment and Intraoperative Support in Liver Resection Surgery, Jacobs University, 2012.
• Steffen Brasch: Interactive Visualization for the Exploration of Aligned Biological Networks and Their Evolution, Ernst-Moritz-Arndt-Universität Greifswald, 2011.
• Suthambhara N: Visual Analysis of Interactions in Multifield Scientific Data, Indian Institute of Science, Bangalore, India, 2011.
• Stanislav Harizanov: Analysis of nonlinear subdivision and multi-scale transforms, Jacobs University, 2011.
• Iulian V. Ilieş: Cluster Analysis for Large, High-dimensional Datasets: Methodology and Applications, Jacobs University, 2010.
• Eui Chul Kang: Efficient Data Reduction Techniques for 3D Model Generation in Reverse Engineering, Gwangju Institute of Science and Technology, Republic of Korea, 2007.
• Sung W. Park: Discrete Computational Methods for Volume Data Processing in Scientific Visualization, University of California, Davis, U.S.A., 2007.

Master Theses

• Johannes Fincke: Integrating Categorical Data into Star Coordinates, WWU 2022.
• Karl Heimes: Visual analysis of tissue properties and their effects on radiofrequency ablation simulations , WWU 2022.
• Hennes Rave: Region-based Visual Analysis of 3D Ensemble Data Using Hierarchical Clustering, WWU 2021.
• Clemens Hesse-Edenfeld: Visual Analytics of Groups in Streaming Trajectory Data, WWU 2021.
• Marcel Fischer: Zusammenfassung und Darstellung multidimensionaler Kurven-Ensembles, WWU 2021.
• Timo Hoth: Effects of induced latency on performance and perception in video games, WWU 2021.
• Steffen Flagge: Effekte der sozialen Interaktion auf das Präsenzgefühl bei graduellen Transitionen, WWU 2021.
• Valerie Müller: Visualisierung von Unsicherheiten in Topic Modellen, WWU 2020.
• Arthur Giberlein: Entwicklung eines eTextile Data Glove mithilfe neuronaler Netzwerke und genetischer Algorithmen, WWU, 2019.
• Karim Huesmann: Real-time Layer-wise Analysis of Convolutional Neural Networks, WWU, 2019.
• Simon Leistikow: Visualisierung von Simulationsensembles zur 4D-Phasenkontrast-MR-Datenassimilation WWU, 2019.
• Carlos Jansen: Hybrid Isosurface Rendering, WWU Münster, 2019.
• José Alejandro Matute Flores: Investigating Vascular Structures: Automatic Generation and Interactive Visual Exploration, Jacobs University, 2015.
• Bojan Kocev: Projector-based Surgeon-Computer Interaction on Deformable Surfaces, Jacobs University, 2013.
• Teodora Chitiboi: Merging Strategies Based on Super-structure Information in Object Based Image Analysis, Jacobs University, 2013.
• Gordan Ristovski: Interactive Visualization of Eye Tracking Data, Jacobs University, 2011.

Petar Dobrev: Compact Representation of the Isosurface Spectrum for Efficient Isosurface Rendering, Jacobs University, 2010.

Teodor Cioaca: CNC Machining with Bounded Dynamics, Jacobs University, 2010.

Daniel Cernea: Graphical Methods for Online Surface Fitting on Range Sensor Point Clouds, Jacobs University, 2009.

Steffi Benthin: Messverfahren für Augen-Deformation: Konzept und rechnerexperimentelle Erprobung, Ernst-Moritz-Arndt Universität Greifswald, 2007.

Olexej Lazarevich: Volume Modeling Using Multiresolution Filtering, Jacobs University, 2006.

Sabine Behrendt: Visualisierung von hierarchischen Strukturen mit 3D Treemap-Nodelink Layouts, Ernst-Moritz-Arndt Universität Greifswald, 2006.

Robert Hermann: Automatische Segmentierung von Haifischdaten basierend auf anatomischem Wissen, Ernst-Moritz-Arndt Universität Greifswald, 2006.

Julia Löcherbach: Visualization of LCMS Data, Ernst-Moritz-Arndt Universität Greifswald, 2005.

Stephan Preuß: Von Punktwolken zu Dreiecksnetzen, Universität Karlsruhe (Prof. Hartmut Prautzsch), 2002.

Gerd Schäfer: Adaptive hierarchische Geländedarstellung,  Universität Karlsruhe (Prof. Hartmut Prautzsch), 2000.

Matthias John: Methoden zur Visualisierung und Verarbeitung von Punktwolken,  Universität Karlsruhe (Prof. Hartmut Prautzsch), 1999.

Bachelor Theses

• Nico Pohlmeier: Visuelle Analyse der Auswirkungen von Blutflusssimulationsparametern auf die Hämodynamik, WWU 2022.
• Moritz Kaiser: Visuelle CNN Hyperparameter-Analyse für Bildsegmentierung durch vortrainierte Netzwerkfeatures, WWU 2022.
• Johannes Hegselmann: Pairwise Combinations of One-dimensional Projection Methods, WWU 2022.
• Hamza Saeed: Visuelle Analyse der Evolution von Topics in Text Corpora, WWU 2022.
• André Himmelspach: Erweiterung von Sternkoordinaten für fehlende Daten, WWU 2021.
• Malte Heuser: Transformer Neural Networks for the Visual Analysis of Spatio-temporal Ensembles, WWU, 2020.
• Sergey Demidov: Interaktive visuelle Analyse von Korrelationen  in Deep Convolutional Neural Networks, WWU 2020.
• Hennes Rave: Axis Bundling and Brushing in Star Coordinates for Multi-dimensional Data Visualization, WWU, 2019.
• Joshua Esselmann: Ein Tool zur semiautomatischen Erstellung von Morphanimationen für 3D Meshes, WWU, 2019.
• Sagad Hamid: Visuelle Analyse vom Einfluss der Hyperparameter beim Trainieren neuronaler Netze, WWU, 2019.
• Marvin Dario Brütt: Kurven-Boxplot-Visualisierung von nicht synchronisierten Kurvenensembles, WWU, 2019.
• Tobias Johanning: Ein heterogenes Virtual Reality Publikum mit manipulierbarem Verhalten, WWU, 2018.
• Alexander Gerwing: Model-based Tracking for Immersive Virtual Environments, WWU, 2018.
• Marcel Fischer: Hauptkurven aus Skeletten für die Analyse multi-dimensionaler Daten, WWU, 2018.
• Maria Karin Herick: Analyse der Veränderung topologischer Strukturen in Simulationsensembles, WWU, 2018.
• Tayfun Honluk: Unsicherheitsvisualisierung bei der Blutflussanalyse, WWU, 2018.
• Marina Evers: Uncertainty-aware Prediction in Spatio-temporal Simulation Ensemble Visualizations, WWU, 2018.
• Stephan Boshe-Plois: Visual Analysis of Billiard Dynamics Simulation Ensembles, WWU, 2018.
• Johannes Weidner: Farbiges stochastisches Shadow Mapping für direktes Volumenrendern, WWU, 2017.
• EIke Gebauer: Visuelle Kodierung von 2D-Skalarfeldensembles, WWU, 2017.
• Steffen Johannes Flagge:  Graduelle Immersion - Die Vorteile eines Einstiegs in eine virtuelle Umgebung, als kontinuierlicher Prozess, , WWU, 2017.
• Bharadwaj Rabindranath Bharath: On The 3-Dimensional Rendering of Contour Trees, Jacobs University, 2016.
• Sara Todorovikj: Time-coherent Image-space Point Cloud Rendering, Jacobs University, 2016.
• Mina Khairy Nesseim Khalil Salib: Fracture of 3D Elastic-Plastic Deformable Objects, Jacobs University, 2016.
• Tinashe Matate: Time-coherent image-space point cloud rendering, Jacobs University, 2015.
• Muhammad Hassaan Farooq: Visual Analysis of MR Spectroscopy Data, Jacobs University, 2015.
• Desislava Decheva: Distance Encoding and Visual Clutter Reduction in the Interactive Visualization of Large Multidimensional Data Sets, Jacobs University, 2015.
• Georgian Besleaga: Modeling physically-based deformations using Mass Spring Systems, Jacobs University, 2014.
• Georgi Gyurchev: Crack Detection in Photographs of Concrete Surfaces and Visualization in Point Clouds, Jacobs University, 2014.
• Andrei Militaru: Intrinsic Decomposition of 3D Laser Scans, Jacobs University, 2014.
• Manuela Miteva: Visualization of Stress and Strain of Bridges in Point Cloud Representation, Jacobs University, 2014.
• Andreea-Ioana Popa: Compact Point-Cloud Representation of Isosurface Spectrum for Efficient Surface Rendering, Jacobs University, 2014.
• Prabal Poudel: Uncertainty in Electromagnetic Tracking: Estimation, Visualization and Correction, Jacobs University, 2014.
• Teodor Rupi: Animation of fracture on physically-based deformation of elastoplastic materials using Finite Element Method, Jacobs University, 2014.
• Muhammad Omer Saeed: Efficient Collision Detection in a Scene with Static and Dynamic Objects (and its Application to Chewing Simulation), Jacobs University, 2014.
• Sudhashree Sayenju: Visualization of similarity of hierarchically organized objects, Jacobs University, 2014.
• Valeriu Schneider: Multi-touch user interaction in navigation through point-based renderings, Jacobs University, 2014.
• Samit Vaidya: Topological landscapes for time-varying volume data, Jacobs University, 2014.
• Titiruck Nuntapramote: Re-lighting of scanned 3D scenes, Jacobs University, 2013.
• Jan-Wilken Dörrie: Map-based Visualization of Categorizations of Mathamatical Articles, Jacobs University, 2013.
• Victor Savu: Compact Representation of Isosurface Spectrum for Efficient Rendering, Jacobs University, 2012.
• Nurazem Kaldybaev: CAD Methods for Shoe Last Modeling, Jacobs University, 2011.
• Aygul Shugaeva: 3D Layout Algorithms in OpenCobalt, Jacobs University, 2011.
• Adriatik Bedjeti: Interactive Visual Exploration of Photo Collections with Coordinated Views, Jacobs University, 2011.
• Richelle Fosu: Multiresolution Approach to Large City Models, Jacobs University, 2011.
• Dimo Stoyanov: Visualizing Network Data: Visual Encoding and Interaction, Jacobs University, 2010.
• Keshab Neupane: Visualizing Network Data: Visual Data Analysis, Jacobs University, 2010.
• Sebastian Trofin: Interactive Visual Exploration of Photo Collections, Jacobs University, 2010.
• Andra Pascale: Tree Growth Modeling: Obstacles, Jacobs University, 2010.
• Kirila Adamova: Tree Growth Modeling: Sketch-based CAD, Jacobs University, 2010.
• Cristina Stancu-Mara: Gesture-based Interactions in Virtual Environments: Depth Information, Jacobs University, 2010.
• Bojan Kocev: Gesture-based Interactions in Virtual Environments: Gestures and Functionality, Jacobs University, 2010.
• Maja Grintal: Topology-based Isosurface Representation, Jacobs University, 2009.
• Alen Stojanov: Gesture-based Human-computer Interaction, Jacobs University, 2009.
• Gordon Ristovski: Detail-preserving Animation, Jacobs University, 2009.
• Petar Dobrev: Modeling of Large Cities - Visualization, Jacobs University, 2008.
• Nikolay Kazmin: Modeling of Large Cities - Software Engineering, Jacobs University, 2008.
• Rouslan Dimitrov: Solid Environment Reconstruction from a Depth Map on the GPU, Jacobs University, 2007.
• Karsten Müller: Fotorealistische Methoden für Punktwolken, Ernst-Moritz-Arndt Universität Greifswald, 2005.
• Matthias Braun: Hierarchische Geländedarstellung durch Punktwolken, Universität Karlsruhe (Prof. Hartmut Prautzsch), 2001.
• Tobias Kunzelmann: Interaktives Modellieren mit Punktwolken, Universität Karlsruhe (Prof. Hartmut Prautzsch),  1999.

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Visual Sheet Music Analytics - PhD Thesis - 2023/2024

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• Matthias Miller

Date created: 2023-12-03 08:40 PM | Last Updated: 2023-12-09 08:41 PM

Category: Project

Description: Additional material and appendices for the PhD thesis presented by Matthias Miller in 2023/2024

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This repository contains additional resources regarding the Ph.D. thesis titled "Visual Sheet Music Analytics" submitted by Matthias Miller at Data Analysis and Visualization Group at the University of Konstanz at the end of 2023.

The Visual Musicology Graph was used for categorizing research projects at the intersection of Musicology and Information Visualization.

The three major applications tha…

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Visual Analytics Research Group

With a university professorship in Visual Analytics, TIB addresses research into visual analysis, search and presentation methods. Within the professorship, a wide range of challenging research issues in visual analytics are pursued in the areas of digital libraries, research data as well as media archives and databases. In addition, TIB's extensive and heterogeneous data stock and users' needs generate application-related research topics.

Research focus and projects in the field of Visual Analytics

Members of the Visual Analytics Research Group

Courses and Bachelor and Master Thesis at Leibniz Universität Hannover

Publications

List of publications of the Visual Analytics Research Group

We are looking for student assistants to support our research and teaching activities. If you are interested, please contact  Prof. Ewerth .

Best Student Paper Award at JCDL 2022

The paper "Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers" by Arthur Brack, Anett Hoppe, Pascal Buschermöhle, and Ralph Ewerth won the Best Student Paper Award at the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2022)

We are currently offering PhD scholarships in the PhD network  LernMINT (LearnSTEM) !

PhD positions in LernMINT:

• Semi-automatic classification of student drawings and texts in Science Education
• Data Analytics for informal learning in school settings
• Ontology-based modelling of learner profiles and curricula

If you are interested, please contact Prof. Ewerth .

Nomination for Best Full Paper Award at ICALT 2020

The paper "A Recommender System For Open Educational Videos Based On Skill Requirements" by Mohammadreza Tavakoli, Sherzod Hakimov, Ralph Ewerth, and Gabor Kismihok has been nominated for competing the Best Full Paper Award at the 20th IEEE International Conference on Advanced Learning Technologies (ICALT 2020). 

• Of horses and media – Marstall building at Welfengarten reopened
• TIB-PITCH with Matti Stöhr
• Our year 2023 – the TIB Report is online
• 10 Years of the TIB AV-Portal – 10 Years of Science in Video Format

Further information

• Faculty of Electrical Engineering and Computer Science
• Institute of Distributed Systems - Knowledge Based Systems Section
• L3S Research Center 

I am a professor in the School of Software, Tsinghua University. I received a B.S. and M.S. from Harbin Institute of Technology, a Ph.D. from Tsinghua University. Before I joined Tsinghua, I worked as a lead researcher at Microsoft Research Asia and a research staff member and research manager at IBM China Research Lab. I was named an IEEE Fellow in 2021.

• Explainable artificial intelligence Visual analytics techniques for machine learnig, including 1) understand, diagnose, and refine a machine learning model; 2) improve data quality and feature quality.
• Visual text analytics Combine the advantages of text mining and interactive visualization to facilitate visual analytics of large-scale textual data.
• Text mining Develop statistical text mining methods to understand complex text data, including evolutionary text clustering, topic modeling, and sentiment analysis.
• Jun Yuan , 2019~
• Weikai Yang , 2019~
• Zhaowei Wang , 2020~
• Zhen Li (co-advised w/Hui Zhang), 2021~
• Yukai Guo , 2022~
• Haoze Wang , 2022~
• Jiangning Zhu , 2023~
• Duan Li , 2023~
• Changjian Chen , 2017~2022, Assistant Professor at Hunan University
• Mengchen Liu , 2013~2018, Senior Researcher at Microsoft Remond
• Xiting Wang , 2011~2016, Assistant Professor at Renmin University of China

Honors and Awards

• 2022, IEEE VGTC Visualization Technical Achievement Award
• 2021, IEEE Fellow
• 2020, IEEE Visualization Academy
• 2020, Best Mentor Award of Tsinghua University
• 2019, Excellent Class Teacher of Tsinghua university
• 2016, Best Associate Editor Award of IEEE TVCG
• 2012, Microsoft Ship-It Award
• 2011, Microsoft Ship-It Award
• 2010, IBM Research accomplishment
• 2006, IBM Master Inventor

Publications

Professional activities.

• IEEE VIS , 2020~
• VINCI, 2012~2013
• IEEE Transactions on Visualization and Computer Graphics , 2019~
• Visualization in Data Science (VDS) at IEEE VIS, 2018
• IEEE VIS (VAST) 2016, 2017
• Artificial Intelligence , 2021~
• IEEE Transactions on Visualization and Computer Graphics , 2015~2018
• IEEE Transactions on Big data , 2018~
• ACM Transactions on Interactive Intelligent Systems , 2019~
• Information Visualization , 2015~
• Journal of Visualization , 2018~
• Organizing Committee (Tutorials Chair): IEEE VIS 2015
• PacificVis 2015
• Organizing Committee (Meetup Chair): IEEE VIS 2014
• Workshop Co-Chair: IEEE VIS 2013 Workshop on Interactive Visual Text Analytics
• Poster Co-chair: PacificVis 2013
• Workshop Co-chair: IEEE VisWeek 2012 Workshop on Interactive Visual Text Analytics
• Program Co-chair: Visual Information Communication - International Symposium 2012
• Workshop Co-chair: IEEE VisWeek 2011 Workshop on Interactive Visual Text Analytics for Decision Making
• Workshop Co-chair: Intelligent Visual Interfaces for Text Analysis, 2010
• Tsinghua Science and Technology: Special issue on visualization and computer graphics , 2012
• ACM Transactions on Intelligent Systems and Technology: Special Issue on Intelligent Visual Interfaces for Text Analysis, 2012
• InfoVis 2020, 2015, 2014
• VAST 2019, 2018, 2015, 2014
• KDD 2015, 2014, 2013
• PacificVis 2013, 2012, 2011, 2010, 2009, 2008
• ACM IUI 2011, 2009
• VISAPP 2012, 2011
• ACM Multimedia 2009
• Big Data Foundations and Applications, 2014, Tsinghua course, Information Visualization: Connecting the Abstract and Visual Worlds .
• PacificVis 2012, Tutorial, Interactive Visual Text Analytics and its Evaluation .
• VINCI 2011, Keynote, Interactive Visual Text Analytics for Decision Making .
• Xiting Wang, Topic Mining and Visual Topic Analysis of Rich Text Corpora , PhD thesis, 2017.
• Mengchen Liu, Visual Analytics of Machine Learning Models , PhD thesis, 2018.