Skip to main content

2015 IBM Fellows

Dr. Jing Shyr

Dr. Jing Shyr

IBM Analytics
Chief Statistician

Maybe it’s time to finally retire the silly cliché, “curiosity killed the cat.” In reality, curiosity solves problems. Newly named IBM Fellow Jing Shyr, a PhD in statistics, constantly thinks about ways to use data to solve problems. From predicting human behavior to help cities provide better services, to building smarter devices, like a vacuum that knows what kind of floor it’s cleaning, Jing asks: “If we have data, then we can find a pattern. So, how can it help solve a problem?”

Jing came to the US from Kaohsiung, Taiwan in 1979 to study statistics at Purdue University. After a brief stint in academia, she joined analytics firm SPSS in Chicago. “I was working on how software could be smart enough to make our work easier.

“Machines can be smarter, and help us make better decisions. And that shouldn’t sound scary. Software should do the grunt work on our data. Today, working with the raw data can take between 70 and 80 percent of the time spent solving a problem. Smarter software should give us a better starting point – and more time – to solve a problem,” Jing said.

Jing saw this battle over time and understanding when she taught people how to use SPSS’s software. The rich data would require a statistician and a data mining expert to understand it. And even those who knew how to crunch the numbers wouldn’t know how to apply the results to the problem they were trying to solve.

“I would teach people how to use our software, but they often couldn’t remember, later. I could feel their pain,” Jing said.

That’s when she came up with the idea for “data scientist in a box”. The software would be the expert, cleaning the data and producing results according to a business problem outlined by the users. Users could even ask the software questions about their business problem to better understand the data.

Then IBM acquired SPSS in 2009.

“I had worked at SPSS for 23 years; was a senior vice president; had built a lab from scratch in China. Did IBM care? I didn’t want to start over!

“So, I decided to find out: what was IBM really doing to improve society? Because that’s what I was trying to do with my work,” Jing said.

Machines can be smarter, and help us make better decisions. And that shouldn’t sound scary.

She didn’t have to worry. Deepak Advani, IBM’s vice president of business analytics at the time, knew of her work and reached out, helping her step into a Distinguished Engineer’s role (a technical rank second only to IBM Fellow). Others also recognized the promise of her work and began helping build data scientist in a box.

And in 2011 Jing took an opportunity to apply her work to a societal problem as part of an IBM Smarter Cities Challenge tackling Syracuse, New York’s growing number of vacant buildings. The team spent three weeks developing “a property vacancy prediction model” that equipped Mayor Stephanie Miner and the city with an intervention model to prevent, even reverse, neighborhood decline.

All the while, Jing continued to build her data scientist in a box. Finally in 2013, after several versions, several pitches – including two presentations to two different IBM CEOs – and a name change to Analytics Catalyst, it was ready for real world data.

“I ran the same data from the Smarter Cities challenge through Analytics Catalyst in three minutes! What we analyzed and agonized over for three weeks was complete in 180 seconds,” Jing said. The 16 patents worth of innovative statistical algorithms have already led to new modeling and data preparation for distributed and high performance systems, and are the foundation of IBM’s predictive everywhere analytics tools.

Jing now takes her idea of smarter software to universities. She helps develop curriculum to equip budding data scientists – and their professors – with skills they’ll need outside the classroom. From revamping tests to creating joint academic and business projects, she sees the next generation of data scientists using smarter software to solve entirely new problems (or maybe that age-old problem of vacuum cleaners recognizing carpet, versus tile).

Dr. Jing Shyr in her own words...

What was the best advice you’ve ever received?

It was from my mom. Even with our culture saying that women should be in a support role to their families, she always told me to have something that defined myself. That I could have my own thoughts about how I can do something – make something better. To find out what I can do and accomplish it. That’s good advice for anyone!

What does it mean to be named an IBM Fellow?

The title won't change what I do. It matters, but it's about the work. And I do think it could help me gain more support for my dream of smarter software.

Where do your best ideas come from?

When I was younger, I would be consumed by a problem until I solved it. Sometimes I would solve the problem while I was asleep or in the shower. Today, I’m inspired by the interaction with others. Maybe it’s a discussion at a conference in Hong Kong. Or maybe it’s meeting with colleagues. That interaction always spurs an idea.

What do you like to do when you’re not solving a problem?

If I need to get my mind off work, I watch crime or mystery shows like Criminal Minds, CSI, or 20/20. I still want to see how a problem gets solved!

What was the last book you read?

I just finished Outliers by Malcolm Gladwell.

What is on your iPod right now?

All kinds of different music. I have some Chinese traditional music. But I also have some Josh Groban and Adele, even some Bee Gees. When I travel, I like to listen to what I’m comfortable with – it’s like comfort food for my ears.


Watch the video

Watch how the Fellows program impacts IBM and the world

IBM Fellows by the numbers

This year's class

Year by year