Twice the Difference of a Number and 5
Thither’s a calculus to knitting. An untamed batch of wool gets twisted and fed into a spinning wheel, a wooden contraption well-nigh equally high-tech as an abacus, that binds the fibers into a single strand of yarn. That yarn, in turn, is woven into geometric designs comprised of equations: A certain number of rows combined with sure stitches yield something functional and beautiful. In the right hands, knitting produces a precise but almost magical abracadabra–anarchy into lodge.
You lot can see why it would appeal to Brenda Dietrich.
Dietrich, 47, runs the math sciences department at
renowned Thomas J. Watson Research Heart–the height math manager at arguably the biggest and virtually important math section in corporate America. She loves math’s beauty and complication. Yet she oftentimes spends conference calls and meetings spinning yarn on the cycle adjacent to her ThinkPad. And she knits endlessly–a scarf, coat, shawl, and hat in progress simultaneously. That exquisite blue and purple cashmere shawl in her part? “This was terminal twelvemonth’s research software strategy coming together,” she says. “I sat in the dorsum row knitting for three days.”
Dietrich, who has coauthored 13 patents and has twice been named i of IBM’due south tiptop inventors, likes to make stuff–tangible stuff, not simply theorems. Equally a mathematician, she has a rare ability to travel between two very different worlds, says Paul Horn, caput of IBM research. She can listen to a customer describe the messy details of a business organisation, then interpret those specs into math problems for her squad to solve. And she thinks mathematicians should live in that existent globe, the world of customers. When she took over the math department in 2001, she encouraged researchers to venture exterior Watson, which she calls “that lovely stone edifice on the colina,” and work with IBM consultants in the field.
These days, her team is, in fact, venturing out from years of behind-the-scenes, mostly theoretical research to tackle an impressive assortment of real-world problems at IBM and beyond. How to assemble a project squad from consultants dispersed around the world. How to fight vast wood fires more effectively. How to place the best sales leads in the pipeline. OnTarget, sales-prediction software that grew out of math research, generated $100 meg in new revenue equally a pilot program in Canada. Last year, it delivered about $500 million in worldwide use, a sum that makes Dietrich giggle as if she tin can’t quite believe it.
Dietrich’s 160 researchers are, in fact, increasingly among the most valuable problem solvers at IBM. “Historically, the stars here have been the physicists who made the applied science that went into chips and systems, and so it was the computer scientists and engineers,” Horn says. “Now we’re seeing the emergence of mathematicians. They’re embedded everywhere.” This is partly due to IBM’s shift from hardware to software and services. And part of it, certainly, is a function of Dietrich’southward marketing and political savvy: A geek, but a far cry from the personality- challenged stereotype, she understands how to win attending and resources in an organization of 330,000 people.
More than that, her department’s growing impact reflects a bigger real-world shift. A generation agone, businesses called on mathematicians, at best, to optimize product lines and possibly to support pricing decisions. What more than could they maybe contribute to the bottom line? Today, companies measure nearly every attribute of what they exercise, and computers are fast enough to crisis the numbers in fourth dimension for execs to human activity on the analysis. In the hands of talented mathematicians, data create an invaluable advantage. Elaborate algorithms reveal a company’s inefficiencies and opportunities–unseen bottlenecks in the supply chain or customers’ hidden buying patterns. Entire companies–think
–are being built almost entirely around math. And others, like IBM, are integrating math into operations and determination making in ways never before seen. This is what the Industrial Age must accept been like for mechanical engineers. “It’southward a great time,” Dietrich says, “to exist a computational mathematician.”
number-theory class at the University of N Carolina at Chapel Colina changed Dietrich’south mind about becoming a md. Math was a revelation, like hearing music for the first fourth dimension. “There’s structure and symmetry and the most gorgeous theory,” she says. “It made me believe in some underlying order in the globe.”
Dietrich, whose hubby is an IBM software architect, joined the company in 1984 after earning her PhD in operations enquiry and industrial engineering at Cornell, and she applied that “gorgeous theory” to designing more-efficient flake-manufacturing lines. It was thrilling to come across how useful math could be. In the mid-1990s, she grew bored between projects–“a unsafe situation,” she laughs–and pursued a new prepare of bug, spending half-dozen months in the field alongside IBM consultants and customers. “They couldn’t tell you the dependent and independent variables,” she says. But she could, and that ability to interpret the practical into the theoretical (and dorsum) was powerful. In some ways, her feel was the footing for how her research department now operates.
If you’re not a mathematician, the deep math that Dietrich and her squad perform sounds utterly strange–combinatorial auctions, integer programming, conditional logic, and so on. Their whiteboard scribbles at Watson look incomprehensible, like Farsi or Greek (then again, many of the symbols are Greek). But these mysterious equations represent the real globe and how it works. When mathematicians “model” a problem, they’re creating a numerical snapshot of a dynamic system and its variables.
Take the forest-burn project Dietrich and the researchers are working on. Extinguishing fast-spreading flames over tens of thousands of acres is an expensive and complicated undertaking. In 2000, a particularly devastating yr, the federal government spent more $1 billion and still lost more and so viii million acres. Its fire planners desire to reduce the cost and the damage through better coordination among the 5 agencies involved.
Armed with seven years of information, IBM’s mathematicians are creating an enormous model that shows how the resources–every firefighter, truck, airplane, etc.–take been used in the past, how much each effort cost, and how many acres burned. The algorithms describe the likely costs and results for whatsoever number of strategies to combat a given burn down. “How many bulldozers and buckets do y’all go on in Yellowstone Park?” Dietrich asks. “And if yous need to move them elsewhere, how much will information technology price and how long will it have?” She’due south talking fast, describing the unruly variables that math makes sense of. “It’s a prissy project. Complicated, huh?”
Uh, yeah. For years, mathematicians were and so focused on bones research that they wouldn’t go nigh projects like this–and they weren’t asked to, either. “Information technology was like working at a university without even the load of education,” says longtime researcher Baruch Schieber. “When yous decided what to piece of work on, the first consideration wasn’t, how will this impact the company?” If researchers wanted to, they could shut their function door and focus on the virtually esoteric research, uninterrupted–and isolated.
At first, Horn says, putting math specialists in front of clients made everyone nervous, not least of all the clients. The researchers are undeniably brilliant, he says, chuckling, but “yous wonder how some of them get dwelling house at night.” Watson, located an 60 minutes north of New York, has a laid-dorsum, collegiate feel; sneakers and jeans, along with the occasional bushy bristles and ponytail, are the norm. Opinionated, professorial types fit right in. Dietrich may seem genial and charmingly quirky, but when she holds forth on the intricacies of math, she can exist intimidating. She doesn’t suffer fools and relishes a expert fence.
Simply Dietrich has learned to soften her arroyo to avert undermining the consultants’ relationships with clients. She helped create a form for researchers that explains the consulting process and culture. A mathematician’s perfectionism has to give way to deadlines. The smartest-person-in-the-room vibe is considered off-putting, rather than an invitation to friction match wits. “Instead of forcing an statement on logic, which nosotros’re trained to exercise–information technology’s a scrap adversarial–you accept to continue your mouth shut and listen,” she says. “And yous’ve got to stay out of the technical muck.”
Some longtime mathematicians initially worried that research would endure nether Dietrich. Instead, they lead a double life. In fact, says researcher Robin Lougee- Heimer, projects like the i she is working on now, a nationwide distribution puzzle for a brand-name customer, uncover fertile inquiry topics. “I’m getting exposed to dandy problems,” she says, “with nasty details and complexity.”
It used to be that Schieber, a senior manager in optimization, would hear well-nigh a project inside IBM and occasionally attain out to consultants. They rarely returned his calls. Now, he says, “I am the 1 existence selective.”
“When we offset started request what resources consultants use on projects, they said every projection was different. That merely collection me crazy.”
The word is out: The math team can assist. Dietrich fields a few dozen requests a calendar month, half of which she turns downwards because the trouble has already been solved or is non challenging enough. “We want to push button the frontiers of what’s solvable,” she says. “Otherwise, what’south the point?”
In a sense, Dietrich is doing what she enjoyed as a young math whiz–solving word bug. Here’s a doozy: After IBM’southward sales team signs a consulting contract, the company often has to assemble the projection team on deadline–say, 50 Java developers in Chicago by the post-obit Monday. Information technology tin choose from 190,000 consultants around the world with diverse skills, personalities, and availability. It must practise this for thousands of projects a twelvemonth for clients of all sizes in every imaginable manufacture. Meanwhile, the mix of projects and available consultants is constantly changing.
“When we get-go started request what resources consultants use on projects, they said every project was dissimilar,” says Dietrich. “That merely drove me crazy.” By poring over two years of projection data, the mathematicians identified which skills were about often applied in certain types of assignments. “You may not know exactly what the customer wants, but now y’all have a rough thought who yous need for a $5 meg project versus a $50 one thousand thousand project,” says Dan Connors, optimization manager for the Workforce Management programme. That staffing-assay tool helped managers anticipate demand and schedule accordingly, boosting the consultants’ productivity 7% and reducing travel expenses and the use of outside contractors. The savings exceeded $500 million. So do the math: Add in sales from the OnTarget forecasting tool, and that’southward a $one billion contribution by Dietrich’s math whizzes.
The brainiacs are tackling another problem whose solution could be only as valuable: how to pick the best teams. Project managers tend to select the most talented developers and engineers available, or the ones they already know. That may work well for the projection at hand, just in the long run, it doesn’t necessarily benefit IBM as a whole; meliorate to spread the talent around. Researchers are also creating a social- networking analysis that would assess trails of email, instant messaging, and phone calls to place which teams operate every bit flat organizations and which ones are hierarchical–who works well together and who doesn’t.
But the trouble that’southward really grabbing Dietrich involves predicting the workforce of the future. By analyzing population trends, employee demographics and skills, and demand for certain technologies, her researchers hope to identify labor shortages in diverse functions and professions earlier they happen.
That piece of work, most unthinkably complex and far-reaching, is nowhere nigh complete. Each reply generates new questions, and that’south fine. That’s adept. Even mathematicians don’t have all the answers. Dietrich won’t get bored, and she’ll plough out some lovely knitting. Eventually, she’ll have numbers that help us remember differently near the world and where it’s headed–and IBM and its customers will hire or train employees accordingly.
It may well plough out, of course, that what they demand are more than mathematicians.
Twice the Difference of a Number and 5