Krist Wongsuphasawat

krist.wongsuphasawat

resume | vita

“I can transform data into
beautiful interactive graphics
that enable you to see
and visually analyze them.”

I am a Data Visualization Scientist at Twitter. I received my PhD from Department of Computer Science at the University of Maryland, College Park under the supervision of Dr. Ben Shneiderman. I was also a member of the Human-Computer Interaction Lab.

I am interested Human-Computer Interaction (HCI), especially in Information Visualization, Visual Analytics and Graphical User Interfaces. My PhD dissertation title is "Interactive Exploration of Temporal Event Sequences. One of the main contributions is a visualization technique called LifeFlow. LifeFlow was designed to support users' exploration of event sequences by aggregating and displaying a compact overview of large amount of event sequences. Please visit the LifeFlow project page for more details and feel free to contact me if you are interested in trying the software.

You can contact me at moc.liamg do not copyta zgnow.tsirk

Data Visualization Scientist, Twitter, San Francisco, CA, USA (2012-Present)
Ph.D. Computer Science, University of Maryland, College Park, MD, USA (2007-2012)
Research Intern, IBM T.J. Watson Research Center, Hawthorne, NY, USA (2011)
Research Intern, Microsoft Research, Redmond, WA, USA (2010)
M.S. Computer Science, University of Maryland, College Park, MD, USA (GPA 3.90) (2007-2009)
System Analyst, ITOne Co.,Ltd., Thailand (2007)
B.Eng. Computer Engineering, Chulalongkorn University, Thailand (3.96, 1st-Class Honor) (2003-2007)
Software Developer Intern, The Stock Exchange of Thailand (2006)

Outflow (2011-present) - under the supervision of David H. Gotz at IBM Research

Electronic Medical Record (EMR) databases contain a large amount of temporal events such as diagnosis dates for various symptoms. Analyzing disease progression pathways in terms of these observed events can provide important insights into how diseases evolve over time. Moreover, connecting these pathways to the eventual outcomes of the corresponding patients can help clinicians understand how certain progression paths may lead to better or worse outcomes. In this paper, we describe the Outflow visualization technique, designed to summarize temporal event data that has been extracted from the EMRs of a cohort of patients. We include sample analyses to show examples of the insights that can be learned from this visualization.

LifeFlow (2010-present) - under the supervision of Ben Shneiderman and Catherine Plaisant

Event sequence analysis is an important task in many domains: medical researchers study the patterns of transfers within the hospital for quality control; transportation experts study accident response logs to identify best practices. In most cases they deal with more than thousands of records. While previous research has focused on searching and browsing, overview tasks are often overlooked. We introduce a novel interactive visual overview of event sequences called LifeFlow. LifeFlow scales to any number of records, summarizes all possible sequences, and highlights the temporal spacing of the events within sequences.

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Similan (2008-present) - under the supervision of Ben Shneiderman and Catherine Plaisant

Similan is an interactive data analysis tool that helps users find records of event sequences that are similar to the target event sequence. Similan implements a customizable similarity measure which computes how two records are similar or dissimilar. Similan provides a user-interface for the users to select record from the database as a target or create a custom target record, and customize search parameters. After the users perform the search, the similarity scores against the target record for all records are computed and all records are ranked by their similarity scores. The results are visualized on the screen with additional filters that allow users to explore the results.

[ more info ]