I was born & raised in New York City by a social-worker mother and an engineer father. My father actually started out in mathematics—as a grad student of Richard Courant's in the 1940s—but switched to engineering so he could support his growing family. Math remained his passion. It was in the air at our house, along with my mother's passion for social justice and the performing arts. She took me to protest marches and the ballet; he showed me how a rotating wheel mounted on top of a moving toy car would trace out a cycloid if you watched it from above.

Culture/Fieldston school, which was big on humanities and the arts, but not so much on physics and math. MIT was a bit of a shock after that, but I grew to love the deep, organic, curiosity-driven sense of inquiry that I found there and I stayed for 14 years, ending up with a PhD in Electrical Engineering and Computer Science in 1992. Somewhere along the way, I'd gotten interested in nonlinear dynamics after reading an article about chaos in American Scientist, which led to dissertation work on chaos and control. I then moved directly to a tenure-track position in the Department of Computer Science at the University of Colorado-Boulder. Postdocs in CS were pretty much unheard of in the 1990s and that transition, too, was a bit of a shock, since I had to find a place to live, fire up an independent research program, write grants, recruit students, and figure out how to teach, all pretty much immediately. On the flip side, though, it was great to sink roots in right away, without the prospect of moving again in a few years.

I'm still in the CU-Boulder CS Department, happily plugged into a culture that is friendly, intellectually rich, and highly supportive of interdisciplinary work—the latter of which is an essential feature for me, since I'm interested in the analysis of time-series data from complex adaptive nonlinear systems, which roughly translates to "just about everything." The department also has deep ties to the Department of Applied Mathematics, which I exploited to connect with Jim Meiss, who remains my closest collaborator. Besides Jim, I've worked with people in four engineering departments during my 30+ years at CU, as well as astrophysics, speech sciences, evolutionary biology, kinesiology, paleoscience, atmospheric sciences, and dance.

In my group, we employ a variety of approaches to extract knowledge from data, ranging from the classical nonlinear time-series analysis techniques to computational topology and information theory. We've used permutation entropy to flag anomalies in ice-core data, for example, and isolate climate events in those cores. Computational topology has let us analyze the structure of the arctic ice pack, clustering in honeybee groups, and swarming in bacteria colonies. It has also helped us predict solar flares and distinguish different musical instruments. We've also done some theoretical and methodological work in both computational topology and nonlinear time-series analysis, and we've demonstrated that you can do an end run around some of the prescriptions of the Takens theorem in some situations. And we've showed that you can even use chaos in some interesting ways: e.g., to generate choreographic variations.

Figure 1. An original performance piece using six mathematically produced variations and a human dancer, entitled "Con/cantation: chaotic variations," premiered in Boston in April of 2007.

One of the hardest and most fun parts of interdisciplinary work is learning about the problem domain: What language and assumptions are used in this field? What variables matter and why? What information can be extracted from their patterns? And why should we care? Another huge challenge is getting funding for this kind of work, not only because you have a lot of background to cover in a finite number of grant proposal pages, but also because you have to convince a heterogeneous (and perhaps doctrinaire) audience that you know what you're doing.

Being a professor in an academic organization that truly supports interdisciplinary work is like being a kid in a candy shop. I can think about anything I want, with wonderful collaborators! The absolute best part of the job is my PhD students. My model for PhD advising was my own advisor, Gerald Jay Sussman, a polymath, machinist, and registered locksmith (among many other things) who would occasionally disappear on sabbatical to write papers with astrophysicists. Gerry's advising philosophy hewed to the old quote from Vladimir Arnol'd: that assigning a thesis topic is like assigning a spouse. I mirrored that approach when I first started out but backed off a bit when I found that dropping people into the deep end of the pool didn't always work well. I do a bit more hand-holding now—but only as much as each student needs, and no more, and I don't insist that their topic overlap 100% with my own interests (hence the long list of departments at the end of the third paragraph of this piece...) A side effect of this is that my students take longer to finish than if I simply handed them a problem, but they have a richer experience and they graduate with much more interesting CVs. Besides, choosing a good problem is, in my opinion, the single most important thing that you learn during a PhD.

Figure 2. The research group at Dynamics Days 2025 in Denver.

Apropos of taking longer to graduate: my PhD work took me nine years. Some of that was Gerry's encouragement to explore the odd & interesting tangents, learn how to run a lathe, take courses in general relativity, and so on. Another part of it was the other passion that I got from my father: sports. When I was a kid, we'd go to Van Cortlandt Park with baseball mitts and a football every Saturday if the weather was good, and up to Vermont to ski if it snowed. I played on softball, field hockey, and basketball teams in high school and college, then fell in helplessly in love with rugby at MIT, but had to stop playing after several concussions. Casting around for something else to scratch the sports itch, I wandered into the boathouse and started rowing, ending up on the US team for the 1986 and 1987 World Championships, as well as the 1988 Olympics. This also had roots on the other side of the family, as my mother's father, John Carlin, was a coach on the US rowing team. He died when I was seven, unfortunately, and so never got to see me row.

Figure 3. The womens four with coxswain at the 1988 Olympic Games. Liz is second from the right.

Anyway, that is MY excuse for taking a long time to get through graduate school. I hope that grad students who are reading this can find other passions, alongside their passion for mathematics, to add joy to their lives—and academic homes that foster and nurture that diversity and richness of experience.