Empowering users to communicate their preferences to machine learning models in Visual Analytics

Recent visual analytic (VA) systems rely on machine learning (ML) to allow users to perform a variety of data analytic tasks, e.g., biologists clustering genome samples, medical practitioners predicting the diagnosis for a new patient, ML practitioners tuning models' hyperparameter settings, etc. These VA systems support interactive construction of models to people (I call them power users) with a diverse set of expertise in ML; from non-experts, to intermediates, to expert ML users. Through my research, I designed and developed VA systems for power users empowering them to communicate their preferences to interactively construct machine learning models for their analytical tasks. In this process, I design algorithms to incorporate user interaction data in machine learning modeling pipelines. Specifically, I deployed and tested (e.g., task completion times, user satisfaction ratings, success rate in finding user-preferred models, model accuracies) two main interaction techniques, multi-model steering, and interactive objective functions to facilitate specification of user goals and objectives to underlying model(s) in VA. However, designing these VA systems for power users poses various challenges, such as addressing diversity in user expertise, metric selection, user modeling to automatically infer preferences, evaluating the success of these systems, etc. Through this work I contribute a set of VA systems that support interactive construction and selection of supervised and unsupervised models using tabular data. In addition, I also present results/findings from a design study of interactive ML in a specific domain with real users and real data.Ph.D

Co-ordination of cell shape changes during ventral furrow formation in Drosophila embryo

The formation of the ventral furrow in the Drosophila embryo has served as one of the major paradigms for how large-scale morphogenetic events are initiated, controlled and mediated by cellular behavior. The furrow is formed by the inward folding of the mesoderm epithelium on the ventral side of the early embryo. While it is well established that the onset of gastrulation is initiated by the apical constriction of the central mesoderm cells (CM), a subpopulation about 8-10 rows wide, it has recently become clear that furrow internalization can only be completed with the cooperation of the lateral mesodermal (LM) cells, a subpopulation about 3-4 rows wide on each side of the mesoderm that, instead of constricting, expand their apical areas at the same time. In this thesis we have developed a method to reconstruct 3D cell volumes in the entire embryo to study the coordination of cells shape changes during ventral furrow formation. We find that the cell shape changes in LM cells are passive and depend on the forces generated during apical constriction in the CM cells. A twist induced gradient of molecular cascade leading to apical MyosinII recruitment in the mesoderm results into a ‘tug-ofwar’ between the adjacent cells. Due to high amount of apical MyosinII recruitment, the CM cells constrict stronger and causes the LM cells to expand apically

XML for ETDs

The main objective of this project was to devise a tool/procedure to aid students at Virginia Tech in developing their electronic theses and dissertations (ETDs) in eXtensible Markup Language (XML) and to document properly all the work that was done at Virginia Tech in this regard. The project began by studying the other ETD-XML projects done earlier. Both the approaches (DTD and XSD) explored at Virginia Tech were studied and an attempt was made to improve the XSD approach using VBA (Visual Basic for Applications). The proposed approach was completely implemented and documented in a way that should be easy for the students to comprehend. This should help ease student efforts to prepare theses in XML

Development of a pattern library and a decision support system for building applications in the domain of scientific workflows for e-Science

Karastoyanova et al. created eScienceSWaT (eScience SoftWare Engineering Technique), that targets at providing a user-friendly and systematic approach for creating applications for scientific experiments in the domain of e-Science. Even though eScienceSWaT is used, still many choices about the scientific experiment model, IT experiment model and infrastructure have to be made. Therefore, a collection of best practices for building scientific experiments is required. Additionally, these best practice need to be connected and organized. Finally, a Decision Support System (DSS) that is based on the best practices and enables decisions about the various choices for e-Science solutions, needs to be developed. Hence, various e-Science applications are examined in this thesis. Best practices are recognised by abstracting from the identified problem-solution pairs in the e-Science applications. Knowledge and best practices from natural science, computer science and software engineering are stored in patterns. Furthermore, relationship types among patterns are worked out. Afterwards, relationships among the patterns are defined and the patterns are organized in a pattern library. In addition, the concept for a DSS that provisions the patterns and its prototypical implementation are presented