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President's Dream Colloquium on Engaging Big Data
People, Data, and Analysis
Watch the Recorded Web Stream
Recorded on February 3, 2016
Talk Highlights
Click for timestamps 鈫�
00:00 - Welcome by Fred Popowich
02:49 - Opening Remarks by Chancellor Anne Giardini
03:05 - Acknowledgment and introduction to President鈥檚 Dream Colloquium
07:10 - Speaker Introduction by Lyn Bartram Pat Hanrahan鈥檚 Lecture
09:20 - Introduction
10:02 - Your Typical Data Scientist
11:14 - Application of Data in Jeopardy
12:05 - Convolutional Neural Network, trained in image recognition
14:20 - Predictive algorithm and Hurricane Sandy
16:28 - Data Analysis, Predictive algorithms and serious questions
17:30 - Why we need people in data analysis
19:48 - We need the people in data analysis but what kind?
25:13 - 鈥淭here are a lot of Data Enthusiasts鈥�
28:05 - Analytical thinking definition
35:11 - Analytical Process
36:17 - First Job
37:18 - Design of Tableau
44:49 - Refining strains for designing Tableau - what to visualize
49:28- The language for visualization
50:17 - Refining strains for designing Tableau
56:44 - The semiology of graphics
58:51 - Principle #1 - Use Semantics
1:00:00 - Principle #2 颅- Good defaults, not rigid rules
1:01:15 Principle #3 颅- Meta Automation
1:04:20 Wrap Up
1:07:21 Peter Chow-颅White Acknowledgment and appreciation talk
1:08:18 Dorcas Yeung鈥檚 questions on big data
1:09:32 Dr Hanrahan鈥檚 answer to Dorcas Yeung鈥檚 questions
1:13:07 Sepideh Hashtroodi鈥檚 question on big data
1:13:38 Dr Hanrahan鈥檚 answer to Sepideh Hashtroodi鈥檚 questions
1:15:19 What鈥檚 an analytical assistant
1:17:39 Zahra Zohrevand鈥檚 question on big data
1:18:14 Dr Hanrahan鈥檚 answer to Zahra Zohrevand鈥檚 questions
1:20:12 Audience Q & A
1:36:16 Peter Chow-颅White & Dorcas Yeung wrapping up the session
Lecture Topics
About the Lecture
Big data is a hot topic in computing. Most research has focused on automatic methods of data processing such as machine learning and natural language processing. Another important direction of research is how to build systems that can store and process massive data sets. An example here is map-reduce and Hadoop.
Unfortunately, what has been lost in the discussion is how people should use data to perform analysis and make decisions. It is unlikely that people will be replaced completely by automate decision making systems in the near future. Hence, an important question to ask is what should people do and what should computers do? In this talk, Hanrahan will discuss promising approaches for building interactive tools that allow people to perform data analysis more easily and effectively.
Research Questions
- When faced with an question involving data, what operations should computers perform and what operations should people perform?
- What are some of the most important tasks in data analysis? How much time and effort is allocated typically to each task?
- More and more tasks as being automated. What problems can come up when people rely on automation? How would you design interfaces that make it easier for people to use smart systems?
About the Speaker
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Professor at the Stanford Computer Graphics Laboratory Cofounder and Chief Scientist of Tableau Founding member of Pixar |
Pat Hanrahan is the CANON Professor of Computer Science and Electrical Engineering at Stanford University where he teaches computer graphics. His current research involves visualization, image synthesis, virtual worlds, and graphics systems and architectures. Before joining Stanford he was a faculty member at Princeton.
Pat has also worked at Pixar where he developed volume rendering software and was the chief architect of the RenderMan(TM) Interface - a protocol that allows modeling programs to describe scenes to high quality rendering programs. In addition to PIXAR, he has founded two companies, Tableau and PeakStream, and served on the technical advisory boards of NVIDIA, Exluna, Neoptica, VSee and Procedural.
Professor Hanrahan has received three university teaching awards. He has received two Academy Awards for Science and Technology, the Spirit of America Creativity Award, the SIGGRAPH Computer Graphics Achievement Award, the SIGGRAPH Stephen A. Coons Award, and the IEEE Visualization Career Award. He was recently elected to the National Academy of Engineering and the American Academy of Arts and Sciences.