My area of research is Dynamical Systems, a branch of modern mathematics concerned with time evolutions of natural and iterative processes. In earlier parts of my career, my work was focused primarily on the theory of chaotic dynamical systems, on topics such as Lyapunov exponents, entropy, fractal dimension, strange attractors, random perturbations, and rates of correlation decay. I have also worked with concrete models including particle systems (billiards) and kicked oscillators. In the last 10+ years, my main focus has shifted toward high dimensional systems, and to systems with both deterministic and stochastic components. I have also become more interested in the application of dynamical systems ideas to the sciences.

Currently, my three main topics of research are (1) ergodic theory in infinite dimensions with a view towards PDEs, (2) nonequilibrium statistical mechanics, mostly nonequilibrium steady states for deterministic and stochastic models and (3) theoretical neuroscience, working towards a coherent dynamical theory of the visual cortex.

Below are some highlights of my work, in roughly chronological order. References are to the "Selected Publications" that follow.

Entropy, Lyapunov exponents, and fractal dimension

Entropy and Lyapunov exponents are two different ways to capture dynamical complexity: entropy measures randomness in the sense of information theory, while positive Lyapunov exponents measure the rates at which nearby orbits diverge. It was known (shortly before I entered the field) that entropy is bounded above by the sum of positive Lyapunov exponents, and that these two quantities are equal for conservative systems. Ledrappier and I completed and clarified the picture: (A) We gave a necessary and sufficient condition for these two quantities to be equal, namely when the measure is SRB (see below); and (B) we proved that in general, the gap between them can be expressed in terms of the dimensions of the invariant measure. These relations are very general; they hold for all diffeomorphisms and flows on finite dimensional manifolds.

The results above are part of a subject called nonuniform hyperbolic theory. I would like to extend this theory to stochastic and infinite dimensional systems. Averaging effects of random noise should simplify the dynamical picture [2]. Extension to infinite dimensions will expand the scope of this theory to include semiflows defined by, e.g., dissipative parabolic PDEs; see [10] for a first step.

Natural invariant measures

For Hamiltonian systems, Liouville measure is clearly the natural invariant measure. What plays the role of Liouville measure for dissipative systems, such as those with attractors? The short answer is: SRB measures -- except that things are a bit more complicated. In the 1970s Sinai, Ruelle and Bowen discovered these measures for uniformly hyperbolic attractors, a class of chaotic attractors satisfying strong geometric conditions. This body of ideas, minus the assertion of existence, was extended to general attractors by Ledrappier and myself (see [1]). Not every chaotic attractor admits an SRB measure, however, and it is very hard to determine if a given attractor does or not. These questions have remained unsettled; [3],[4] and [7] are among the few results known, and [3] was the first time SRB measures were constructed for genuinely nonuniformly hyperbolic attractors.

Decay of time correlations

By definition, deterministic dynamical systems have memory. The more chaotic a system is, the more rapidly it mixes up its phase space geometry, equivalently, the faster the decay of its time correlations (with respect to smooth test functions). The following two sets of results are my main contributions in this topic [4],[5]: (A) Via a so-called "tower" construction, I connected sufficiently hyperbolic (or chaotic) systems to countable state Markov chains. Leveraging these Markov-like structures, I showed that many statistical properties of the system are determined by tail properties of return times to certain reference sets. For example, exponentially decaying tails lead to exponential correlation decay, central limit theorems, large deviation principles, etc. (B) I proposed to connect the tail properties above directly to the geometry of the map, promoting the idea that to gain insight into the mode of correlation decay for a system that is predominantly hyperbolic, one should focus on its most nonhyperbolic parts. (A) and (B) together offered a unified way to get a handle on statistical properties of large classes of dynamical systems. I demonstrated that on a few examples, including the 2D periodic Lorentz gas [4]; others have used this method many more times.

Strange attractors

Even though there is no formal definition of a "strange attractor", everyone agrees that its dynamics cannot be simple. Wang and I undertook a systematic study of what can be thought of as "strange attractors of the simplest kind". We called them rank-one attractors, referring to the fact that while these attractors can live in phase spaces of any dimension, they have only one direction of instability, with strong contraction in all other directions. One might expect to see such attractors following a regime's loss of stability, and that is what my co-authors and I (and others) have shown: rank-one attractors occur naturally in periodically kicked oscillators, in periodically forced systems undergoing Hopf bifurcations, with homoclinic loops, and in certain slow-fast systems. They can occur in systems defined by ODEs as well as PDEs; see e.g. [9].

This project consisted, in fact, of two separate parts, the second of which is reported above. The first part was a 130 page paper in which Wang and I identified a set of geometric conditions and proved that they imply the existence of rank-one attractors [7]. This part of our work benefited from the techniques of Benedicks and Carleson, in their analysis of the Hénon maps. We extracted certain ideas from this one example, and developed them into a general class of dynamical systems that includes all of the examples above. We also gave a full description of the geometric and statistical properties of the attractors in this class.

Nonequilibrium dynamics

Systems in the real world do not operate in isolation; they are driven by external forces, and interact with the outside world. Much of current dynamical systems theory ignores such interactions, understandably so for reasons of simplicity. Below are two of my attempts to "push the envelope":

(A) An important source of inspiration is nonequilirbium statistical mechanics. In [6], Eckmann and I investigated the steady states of a class of mechanical chains connected to two unequal heat baths. Our aim was to elucidate how (a) dynamical properties and (b) local thermal equilibria factor in the determination of macroscopic observations such as mean energy and particle density profiles.

(B) Demers and I studied systems with holes: once the orbit of a point enters a "hole", it is lost forever. Starting from an initial distribution, relevant questions include the rate at which mass escapes, surviving distributions, etc. For a prototypical result, see [8], which treats a billiard table with holes.

Modeling the visual cortex (Theoretical Neuroscience)

Stripping away countless layers of complexity, one can model certain parts of the brain as a complicated network of spiking neurons with many unknown parameters. It is a dynamical system, but instead of being handed a known map or equation and asked to deduce its properties, here one has at one's disposal bits of biological facts and experimental data, i.e. outputs of the system, from which to back out the rules of the dynamics and parameters.

I am involved in a computational modeling project with Rangan the goal of which is to study the primary visual cortex (V1) of higher mammals (primates). We have successfully constrained a parsimonious network with ~ 10 free parameters to match a comparable number of experimental results, giving some confidence to the possibility that our dynamical regime may be indicative of the operating point of real cortex. An emergent phenomenon is that when there is strong competition between excitation and inhibition, as is believed to be the case in V1, spiking patterns tend to be irregular yet highly structured, fueled by positive feedback of recurrent excitation. Some results and model predictions are reported in [11].


  1. (with F. Ledrappier) The metric entropy of diffeomorphisms, Part I: Characterization of measures satisfying Pesin's entropy formula, Part II: Relations between entropy, exponents and dimension, Annals of Math., 122, (1985), 509-574.
    PDF Part I | PDF Part II ]

  2. (with F. Ledrappier) Entropy formula for random transformations, Prob. Th. Rel. Fields, 80, (1988), 217-240.
    [ PDF ]

  3. (with M. Benedicks) Sinai-Bowen-Ruelle measures for certain Hénon maps, Invent. Math, 112, (1993), 541-576.
    [ PDF | Postscript ]

  4. Statistical properties of dynamical systems with some hyperbolicity, Annals of Math., (1998), 585-650.
    [ PDF | Postscript ]

  5. Recurrence times and rates of mixing, Israel J Math. 110, (1999), 153-188
    [ PDF | Postscript ]

  6. (with J.-P. Eckmann) Nonequilibrium Energy Profiles for a Class of 1-D Models, Commun. Math. Phys., 262, (2006), 237-267.
    [ PDF | Postscript ]

  7. (with Q.D. Wang) Toward a theory of rank one attractors, Annals of Math., 167, No.2, (2008), 349-480.
    [ PDF | Postscript ]

  8. (with M. Demers and P. Wright) Escape rates and physically relevant measures for billiards with small holes, Commun. Math. Phys., Vol 294, 2, (2010), 353-338.
    [ PDF ]

  9. (with K. Lu and Q. D. Wang) Strange attractors for periodically forced parabolic equations, AMS Memoirs, (2013).
    [ PDF ]

  10. (with Z. Lian) Lyapunov exponents, periodic orbits and horseshoes for semiflows on Hilbert spaces, J. Amer. Math. Soc., 25 (2012), 637-665.
    [ PDF ]

  11. (with A. Rangan) Emergent dynamics in a model of visual cortex, J Comp. Neurosci., Vol 35, Issue 2, 155-167 (2013).
    [ PDF ]