My research focuses on using data visualization and human-computer interaction to translate complex, high-dimensional, and often large-scale data into actionable insights and discoveries.
In today’s world, data is everywhere around us, and it’s becoming bigger and more varied every day. In many cases, purely automatic approaches are insufficient to understand and analyze this data. In these scenarios, data visualization — the process of using visual representations to show abstract data — can provide a key avenue for sensemaking. This is because visualization takes advantage of our most powerful sense (our sight!), making it a tremendously powerful technique for interpreting and understanding data.
To promote insight, visual tools must enable effective data representation, interactive exploration, and the power to apply desired algorithms and action-based pipelines. These efforts are firmly grounded in current and real-world problems, with solutions often requiring close collaboration with domain experts and integrating techniques from related fields including data science (machine learning, data mining, etc.) and user-centered design. As a result, visualization is applicable to an enormous array of fields that deal with real-world data:
- social network and graph analysis
- big data and high-dimensional analysis
- health informatics and medicine
- genomics and phylogenetics
- education and interactive learning applications
- immersive rendering and VR
- business and finance data
- weather modeling and other predictive scenarios
- data storytelling and computational journalism
- scientific computing and simulation
- explainable NLP, ML, and AI
- and much more…