[Colloquium] TODAY, 2PM: Data Science/CS Candidate Talk - Alex Kale (U. of Washington)

[Rob Mitchum]

*Data Science Institute/Computer Science Candidate Seminar*

*Alex Kale*
*PhD Candidate*
*University of Washington*

*Thursday, March 3rd*
*2:00 p.m. - 3:00 p.m.*
*In Person: John Crerar Library, Room 390*
*Remote: Live Stream <http://live.cs.uchicago.edu/alexkale/> or Zoom
<https://uchicago.zoom.us/j/91009304949?pwd=MUg1bFp3ay96SFljNWs0NllzQlFTUT09>
(details
below)*


*Data Interfaces for Reasoning with Uncertainty*

The need to think with data permeates our world today, yet failures to
account for how people reason with uncertainty undermine some of our
current approaches to thinking with data. For example, the status quo in
public-facing visualizations (e.g., of Covid-19, election forecasts, or
economic indicators) is to present data without making uncertainty
information explicit, catering to people’s natural tendencies to ignore or
downplay uncertainty. Even when we do quantify uncertainty and show it in
data visualizations—e.g., as error bars depicting a range of expected
outcomes—lay audiences and statistically savvy analysts alike tend to
misinterpret these visual representations. I will describe how I address
these problems: (1) by creating formalisms and experiments to capture what
makes reasoning with uncertainty difficult in the context of real-world
tasks like decision making, and (2) by building prototypes of data analysis
software to test design hypotheses derived from empirical findings and
theories of data cognition. Together, these formalisms and tools advance
theories of what makes a visualization effective, beyond the ability to
easily read numbers from charts. I will demonstrate how visualizations and
data analysis tools can promote new ways of thinking with uncertainty that
are less error prone. Throughout my talk, I will challenge conventional
assumptions about how people use visualizations, opening the door to new
ways of designing and building software for data analysis.

*Bio*: Alex Kale <http://students.washington.edu/kalea/> is a PhD candidate
at the University of Washington Information School and a visiting scholar
in Computer Science at Northwestern University. He studies how people
reason with uncertainty in data visualizations and analysis software,
developing formalisms to bridge software evaluation and design. Alex
publishes in top Human-Computer Interaction and Visualization venues such
as ACM CHI and IEEE VIS, where his work won Best Paper and Honorable
Mention Awards. Alex’s work demonstrates gaps in dominant theories and
models of what makes a visualization effective for inferences and decision
making. Alex will finish his PhD in June of 2022. He holds a B.S. in
Psychology with Honors (2015) and a M.S. in Information Science (2020) from
the University of Washington.

*Host*: Blase Ur

*Zoom Info:*
https://uchicago.zoom.us/j/91009304949?pwd=MUg1bFp3ay96SFljNWs0NllzQlFTUT09
Meeting ID: 910 0930 4949
Password: ds2022


-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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