Irregular algorithms such as graph algorithms, sorting, and sparse matrix multiplication, present numerous programming challenges, including scalability, load balancing, and efficient memory utilization. In this age of Big ...

With the increasing amount of collected data, large-scale high-dimensional data analysis is becoming essential in many areas. These data can be analyzed either by using fully computational methods or by leveraging human ...

While artificial intelligence (AI) has led to major breakthroughs in many domains, understanding machine learning models remains a fundamental challenge. How can we make AI more accessible and interpretable, or more broadly, ...

Data-driven paradigms now solve the world's hardest problems by automatically learning from data. Unfortunately, what is learned is often unknown to both the people who train the models and the people they impact. This has ...

Large graphs are now commonplace, amplifying the fundamental challenges of exploring, navigating, and understanding massive data. Our work tackles critical aspects of graph sensemaking, to create human-in-the-loop network ...