This talk has been rescheduled to
Cody Dunne, PhD, Assistant Professor, Northeastern University
Tuesday, March 13, 2017 at 6:30 PM
At Audible, 101 Main Street, 6th Floor, Cambridge, MA 02142
The modern world is awash in complex data that can contain the keys to improving our lives. The scope of this data has rapidly outpaced our capabilities to analyze and comprehend, so we turn to computers to help. However, state-of-the-art technology can only supplement the human element. People assist in each stage of data science, whether it’s data cleaning, understanding algorithm design, exploring computed results, or collaborating and sharing for decision-making. To present complex information to humans, we use visualizations that leverage our extraordinary perceptual system which can detect trends, clusters, gaps, and outliers almost instantly.
A challenging and increasingly important type of data is networks of entities and their relationships. Networks are widely used across diverse disciplines to reason about complex behavior. These analyses involve understanding relationships, as well as associated attributes, statistics, or groupings. The omnipresent node-link visualization excels at showing topology and features simultaneously, but many are difficult to extract meaning from due to poor layout or shoehorning inherent complexity into limited space. Some networks have geospatial components and are temporally evolving – which further complicates visualization design. The first part of my talk will detail techniques for measuring the readability of node-link visualizations and strategies to help users create more effective and understandable visualizations in several domains.
Temporal datasets may also have interrelated variables that can make temporal inference difficult. As the volume and frequency of data grows it becomes difficult to see the subsets of data relevant to an event, or set of events, of significance to the domain problem. These issues are compounded when there is poor data quality such as missing data and uncertainty in values or timestamps. The second part of my talk will describe ongoing research in my group on methodologies for visualizing long streams of temporal event sequences with multidimensional, interrelated data. I will present a case study on using these techniques for type 1 diabetes clinical decision support.
Professor Cody Dunne works at the intersection of information visualization, network science, human-computer interaction, and computer science. He focuses on techniques for making data easier to analyze and share, as well as the application of visualization techniques to real-world problems. Dr. Dunne is currently researching the next generation of techniques for visually exploring, sharing, and collaborating around data.
Some domains Dr. Dunne has worked on include visualizing events and concepts from medical records, the spread of infectious diseases, citations in academic literature, interactions of people and organizations, relationships in archaeological dig sites, news term co-occurrence, thesaurus category relationships, municipal energy use, and computer network traffic flow.
Prior to joining Northeastern in 2016, Dr. Dunne was a research scientist in IBM Watson Health, IBM Watson, and IBM Research. Dr. Dunne received his PhD and MS degrees in computer science under Ben Shneiderman at the University of Maryland Human-Computer Interaction Lab in 2013 and 2009, respectively. He earned a B.A. degree in computer science and mathematics from Cornell College in 2007.
6:30 – 7:00 Networking over pizza and beverages
7:00 – 8:30 Meeting
8:30 – 9:00 CHI Dessert and more networking
Thank you to our generous sponsors. Interested in sponsoring BostonCHI? Let us know!
Audible is hosting us and sponsoring pizza.
Izotope is sponsoring dessert.
Audible is located at 101 Main Street, 6th Floor, Cambridge, MA 02142. The entrance to the building is right next to Tatte Bakery on Main Street. The best way to get there is to take the Red Line to Kendall Square. If you’re driving, here’s a list of parking locations.