Happy Nice Mall

The puzzle Daubechies solved was how to take a recent wavelet progression made by French mathematicians Yves Meyer and Stéphane Mallat, but technically impractical, and make it suitable for application. Daubechies used to say “preoccupy” but without making it ugly. As Guggenheim puts it in his statement: “It’s something mathematicians often take for granted, a mathematical framework can be really elegant and beautiful, but to use it in a real application you have to break it down: Well, they shrug, that’s life – applied math is always a bit dirty I did not agree with this view.”

By February 1987, it formed the basis of what grew into a wavelet “family” of Daubechies, each suited to a slightly different task. One important factor made his breakthrough possible: For the first time in his career, he had a computer terminal at his desk so he could easily program his equations and graph the results. By that summer, Daubechies had written an article and landed a job at AT&T Bell Labs, avoiding the hiring freeze. It started in July and moved into a house she had recently bought with Calderbank, which she married after asking the question the previous fall. (Calderbank had reported it was a permanent offer, but resisted making the offer, out of respect for the Daubechies’ declared opposition to the institution of marriage.)

The ceremony was held in Brussels in May. Daubechies cooked the entire wedding meal (with the help of her fiancé), a Belgian-English chicken feast with chicory and Lancashire stew, chocolate cake, and (among other offerings) for 90 guests. He thought 10 days of baking and baking would be manageable, but then realized he didn’t have enough pots and pans for preparation or enough space for refrigerator storage, let alone other logistical difficulties. His algorithmic solution was as follows: Have friends lend him the necessary containers; fill the said containers and return them for refrigerated storage and transport to the wedding. More gourmets encouraged guests to bring hors d’oeuvres rather than gifts. His mother stamped her foot and bought an army salt shaker.

Daubechies continued Wavelet research at AT&T Bell Labs paused in 1988 to have a baby. It was a restless and confusing time because for several months after birth she lost her ability to do research-level math. “Mathematical ideas don’t come,” he says. This scared him. She didn’t tell anyone, not even her husband, until her creative motivation slowly returned. Occasionally, he’s warned young female mathematicians since then about the baby-brain effect, and they’ve been grateful for the tip. “I couldn’t imagine having trouble thinking,” says Lillian Pierce, a colleague at Duke. But when that happened, Pierce reminded himself, “Okay, that’s what Ingrid was talking about. It’ll pass.” Daubechies’ female students express their gratitude for her willingness to insist on childcare at conferences and sometimes even take on babysitting duties. “My advisor volunteered to entertain my toddler while I was giving a talk,” a former PhD student said. student, Yale mathematician Anna Gilbert remembers. “It seamlessly included all aspects of work and life.”

In 1993, Daubechies was appointed to the faculty at Princeton, becoming the first woman to become a professor in the mathematics department. He was tempted by the prospect of mingling with not only electrical engineers and mathematicians, but historians, sociologists and the like. He designed a course called “Math Alive” for non-mathematics and non-science disciplines and gave public talks on “Surfing with Wavelets: A New Approach to Analyzing Sound and Images”. The ripples used by the FBI to digitize the fingerprint database were taking off in the real world. A wavelet-inspired algorithm was used in the animations of movies like “A Bug’s Life”.

“Daubechies wavelets are smooth, well-balanced, not overly dispersed, and easy to implement on a computer.” Terence Tao, Says a mathematician from the University of California, Los Angeles. He was a Princeton graduate in the 1990s and took lessons from the Daubechies. (He won the Fields Medal in 2006.) Daubechies says his wavelets can be used “out-of-the-box” for a wide variety of signal processing problems. In class, Tao recalls the Daubechies had a knack for seeing pure mathematics (for curiosity’s sake), applied mathematics (for practical purpose), and physical experience as a unified whole. “For example, I remember one time he described learning how the inner ear works and realizing that it’s more or less the same thing as wavelet transform, which led him to propose the use of wavelets in speech recognition.” The Daubechies wavelet pushed the field into the digital age. Wavelets proved to be revolutionary in part because they were so mathematically profound. But mostly because, as Calderbank points out, Daubechies, a tireless community builder, made it his mission to build a network of bridges to other areas.

In due course, the awards began to pile up: MacArthur in 1992 was followed by the American Mathematical Society Steele Exhibition Award in 1994 for his book “Ten Lessons on Wavelets.” In 2000, Daubechies became the first woman to receive the National Academy of Sciences award in mathematics. By then she was a mother of two young children. (Her 30-year-old daughter Carolyn is a data scientist, her son Michael, 33, a high school math teacher on Chicago’s South Side.) And she seemed to handle everything with ease.