Big Understanding
Networks, Sensors, & Mobility
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Big Understanding
February 17-18, 2016
(Opening reception/dinner Feb 16)
Austin, Texas

Texas Advanced Computing Center (TACC) and University of Texas
Feb 19, 2016

Library Selection
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015)
By Pedro Domingos



“Successful engineering is all about understanding how things break or fail.”
— Henry Petroski

“The noblest pleasure is the joy of understanding.”
— Leonardo da Vinci

conference overview
Science is dramatically expanding our understanding of the world around us and thereby improving the technologies by which we advance human progress. Computing is being harnessed into that service in ways both straightforward (Big Data) and subtle, yielding Big Understanding through large scale simulation, complex statistical and logical reasoning, finding needles (e.g., long chains of cause and effect which form scenarios of interest) in haystacks of big data, and other means of doing what human computational and analytical skills cannot accomplish alone. Sixty years ago the referent of the word “computer” shifted from people to machines; in the coming years we may witness a sea change in which the primary meaning of “computer scientist” will be a program that designs and carries out experiments.

list of speakers
Manuel Aparicio, Saffron Technology, Intel
Big Experience: The Human Source of Knowledge, Captured and Shared by Cognitive Computing

Scott Clark, SigOpt
Using Optimization to Build Systems with Less Trial and Error

Greg Dobler, NYU
Urban Informatics: Better Cities Through Imaging

Erin Dolan, University of Texas at Austin
Big Undergraduate Research: The Freshman Research Initiative

Barbara Han, Cary Institute
The Algorithm That's Hunting Ebola

Paul Hofmann, Space-Time Insight
Augmented Intelligence—Machine Learning on Sparse Graphs

Sumant Kawale, SparkCognition
Imbuing the Industrial Internet with Intelligence

Doug Lenat, Cyc
50 Shades of Understanding: Why The Veneer of Intelligence is Not Enough

Pietro Michelucci, Human Computation Institute
Crowd AI

Erik Mueller, Symbolics AI
Building Systems that Reason and Explain Like People

Scott Niekum, University of Texas
From Robot Learning to Embodied Understanding

Jessica Richman, uBiome
Understanding Our Microbiome

D Sculley, Google
Machine Learning: The High Interest Credit-Card of Technical Debt

Dafna Shahaf
Information Cartography

Josh Stuart, University of California, Santa Cruz
Predicting Driver Mutations Across Cancers Using Pathway Logic

Kalyan Veeramachaneni, Research Scientist, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
To Teach a Computer to Be a Data Scientist

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