Presently, I am an Assistant Professor of Medicine at Harvard Medical School, Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center. I am also Co-Director of the Margret and H. A. Rey Institute for Nonlinear Dynamics in Medicine.
I concentrated in Physics at the University of Lisbon. In 2000, while a PhD student there, I was awarded a Fulbright grant and enrolled in the Graduate School of Arts and Sciences, Harvard University, as a special student. I completed my PhD dissertation under the supervision of Professor Eduardo Ducla Soares from the University of Lisbon, in conjunction with Dr. C.-K. Peng and Dr. Ary L. Goldberger from Harvard Medical School. My general area of research is in complex systems (nonlinear dynamics and statistical physics).
My specific focus is in biomedical applications, including improving diagnostics and monitoring of instabilities in physiologic control. The work includes the study of life-threatening cardiac diseases and also neurological pathologies related to gait disorders and seizures. This work is at the interdisciplinary crossroads of contemporary physics, bioengineering, physiology, biology and clinical medicine.
A hallmark of such complex systems is their multiscale organization, both temporally and spatially. However, the ability to model, monitor and control complex dynamics is still in its infancy and poses some of the most daunting challenges in contemporary science. My work is directed at developing (i) methods to quantify multiscale properties of complex signals, (ii) models of physiologic control that account for these properties under healthy conditions and their changes with pathology and aging, and (iii) novel indexes for risk stratification and monitoring of pharmacologic and non-pharmacologic interventions.
Over the past few years, my collaborators and I have developed quantitative algorithms to probe some of the generic features of complex systems and applied these tools to the understanding of the underlying system dynamics. We have introduced multiscale entropy and time irreversibility analysis techniques and applied them to the study of the cardiac dynamics of healthy subjects and patients with different type of pathologies. The former technique quantifies the information content of a signal across multiple time scales and the latter quantifies the degree of temporal irreversibility over multiple time scales. Time irreversibility is a fundamental property of open dissipative systems that operate far from equilibrium.
We have shown that healthy dynamics, which are regulated by control mechanisms that lack a single (characteristic) time scale, are the most complex and the most time irreversible of the physiologic time series we have examined. These properties reflect the ability of living systems to evolve to more hierarchically ordered structural configurations and to adjust to continuous changes of internal and external variables. We have shown further that both complexity and time irreversibility degrade with aging and disease.
These results challenge traditional mechanisms of physiologic control based on classical homeostasis and are of interest from a number of perspectives including basic modeling of regulatory systems and practical bedside applications.
The multiscale entropy method has been featured in Nature and is being used by an increased number of investigators around the world. As part of my commitment to open science I have made the code available at the NIH supported PhysioNet website (PhysioNet) along with a tutorial describing its applications.
We have also applied the multiscale entropy method to the study human coding and non-coding DNA sequences and found that the latter are more complex than the former. These results are consistent with the emerging view that the so-called “junk DNA” sequences, although not coding for proteins, may carry important biologic information for cellular activity.
My current work focuses on uncovering fundamental mechanisms of biologic control that account for the multiscale properties of complex systems ranging from the cellular to the system level.