[Job opening] PhD and Master positions in GIScience and GeoAI

Dr. Liping Yang is offering two (1 Ph.D. and 1 Master) funded, full-time graduate scholarships in geospatial data science and geospatial artificial intelligence. Particular topics of interest include

    • Novel methods for analyzing structured and unstructured geospatial data (such as text and images),
    • Information and image retrieval for geographic and historical data,
    • Exploratory search user interfaces powered by computer vision
      and machine learning,
    • Artificial intelligence for geographic knowledge discovery,
    • Spatial representation and reasoning.

Enthusiastic candidates with interests related to these topics are highly encouraged to apply; working experience with at least one programming language (Python, Java, C/C++, JavaScript etc.) is a prerequisite for the positions, but the most important quality is a desire to do creative original research at the intersection of GIScience, computer science, and mathematics.

Students working with Dr. Liping Yang, will have great opportunities for summer interns and/or graduate assistantships at Los Alamos National Laboratory (LANL); after graduation (with proper qualification), can be recommended to work at LANL.

To apply and for more Ph.D. and M.S. positions in GIScience and geography available at the Department of
Geography and Environmental Studies at the University of New Mexico, please check out the PDF here.

Note that Application Deadlines for Fall Admissions:

MS:  February 1 (check here)

PhD:  January 15 (check here)

We look forward to reviewing your applications!

Liping Yang

Postdoctoral Research Associate

Information Sciences group

Los Alamos National Laboratory

 

Assistant Professor of Geographic Information Science  (starting January 2020)

Department of Geography and Environmental Studies

University of New Mexico

 

email: liping.yang@lanl.gov & lipingyang@unm.edu

web: http://www.lipingyang.org/

research blog: http://deeplearning.lipingyang.org/

[Paper published] Novel representation and method for effective zigzag noise denoising

Annoyed by  persistent zigzag noises that cannot be removed after trying many existing denoising methods and techniques?

Check out our newly published ICCV 2019 SGRL paper (SGRL page on CVF) [A PDF of the paper can be found at the CVF website  or HERE] for a novel image representation and method, along with algorithms built upon the representation, for effective denoising of such types of zigzag noises introduced by the digitizing process such as scanning. This type of noise is very common in scanned documents, as well as in some images such as roads with worn road markings.

 

 

 

Check out this page for Liping’s more publications.

 

[Job opening] Outstanding postdoc position for Computer vision and machine learning

I recently received an exciting research grant in computer vision and machine learning. We have an outstanding postdoctoral research associate position. Check the link below for how to apply. We are looking forward to your application.

See HERE on LinkedIn or HERE at LANL website (PDF here if it is not retrievable).

 

 

Quotes About Data and Information

This post provides some quotes about data and information.

“We are moving slowly into an era where big data is the starting point, not the end.” – Pearl Zhu, author of the “Digital Master” book series.

 

“We’re entering a new world in which data may be more important than software.” – Tim O’Reilly, founder, O’Reilly Media.

 

“Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard, senior vice president, Gartner Research.
“Getting information off the internet is like taking a drink from a firehose.” – Mitchell Kapor, founder of Lotus Development Corporation and designer of Lotus 1-2-3, co-founder of the Electronic Frontier Foundation.
Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.” – Mike Loukides, editor, O’Reilly Media.

 

“A data scientist is someone who can obtain, scrub, explore, model, and interpret data, blending hacking, statistics, and machine learning. Data scientists not only are adept at working with data, but appreciate data itself as a first-class product.” – Hillary Mason, founder, Fast Forward Labs.

 

“Think analytically, rigorously, and systematically about a business problem and come up with a solution that leverages the available data.” – Michael O’Connell, chief analytics officer, TIBCO.
“Errors using inadequate data are much less than those using no data at all.” – Charles Babbage, mathematician, engineer, inventor, and philosopher.
 “Without big data, you are blind and deaf in the middle of a freeway.” – Geoffrey Moore, management consultant and theorist.

 

Data are just summaries of thousands of stories–tell a few of those stories to help make the data meaningful.” – Chip and Dan Heath, authors of “Made to Stick” and “Switch.”
The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.
You can have data without information, but you cannot have information without data.” – Daniel Keys Moran, an American computer programmer and science fiction writer.
When we have all data online it will be great for humanity. It is a prerequisite to solving many problems that humankind faces.” – Robert Cailliau, Belgian informatics engineer and computer scientist who, together with Tim Berners-Lee, developed the World Wide Web.
“Data is a precious thing and will last longer than the systems themselves.” Tim Berners-Lee, inventor of the World Wide Web.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” Geoffrey Moore, author and consultant.
“Things get done only if the data we gather can inform and inspire those in a position to make [a] difference.” Mike Schmoker, former school administrator, English teacher and football coach, author.
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” Jim Barksdale, former Netscape CEO
Passion provides purpose, but data drives decisions. 
 -- Andy Dunn

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. 

-- Alvin Toffler

“The world is one big data problem.” – Andrew McAfee, principal research scientist, MIT.
Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital.” – Aaron Levenstein, business professor at Baruch College.
“Big data will replace the need for 80% of all doctors.” – Vinod Khosla, co-founder of Sun Microsystems and founder of Khosla Ventures.
“Every company has big data in its future, and every company will eventually be in the data business.” – Thomas H. Davenport, American academic and author specializing in analytics, business process innovation, and knowledge management.
“Everything we do in the digital realm—from surfing the web to sending an email to conducting a credit card transaction to, yes, making a phone call—creates a data trail. And if that trail exists, chances are someone is using it—or will be soon enough.” – Douglas Rushkoff, author of “Throwing Rocks at the Google Bus.”

 

“You happily give Facebook terabytes of structured data about yourself, content with the implicit tradeoff that Facebook is going to give you a social service that makes your life better.” – John Battelle, founder, Wired magazine.

 

“It’s so cheap to store all data. It’s cheaper to keep it than to delete it. And that means people will change their behavior because they know anything they say online can be used against them in the future.”- Mikko Hypponen, security and privacy expert.

 

 

References

 

 

Model-free vs. Model-based Methods

As Kahneman (2011) pointed out in his book “Thinking, fast and slow’’, we have two modes of thinking: fast and slow. For example, we do not need to think much about how to walk, how to eat; but we do need to think slowly for some complex tasks such as planing our travel routes.

In reinforcement learning, there are two main categories of methods: model-free and model based.

  • Model-free methods: never learn task T and environment E  explicitly. At the end of learning, agent knows how to act, but doesn’t explicitly know anything about the environment. Deep learning algorithms are model-free methods.
  • Model-based methods: explicitly learn task T. (see model-based reasoning to get a sense of it.)

AlphaGo involves both model-free methods (Convolutional Neural Network (CNN)), and also model-based methods (Monte Carlo Tree Search (MCTS)). In fact, AlphaGo is pretty similar to how we humans think: involving both fast intuition (i.e., cost function by CNN) and also careful and slow thinking (i.e., MCTS).

Combining model-free and model-based methods should probably be the way to go for the solutions to many real-world problems (fast intuition + careful planing).

 

References:

Kahneman, Daniel. Thinking, fast and slow. Macmillan, 2011.