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Yang-Yu Liu, Ph.D.

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Overview
I am a statistical physicist by training, with expertise in analytical calculation, modeling and data analysis. My Ph.D research encompassed a broad range of topics from statistical to condensed matter and biological physics. Now I am working on complex networks and systems biology. The primary goal of my current research is to combine tools from control theory, network science and statistical physics to address the challenging questions pertaining to controlling and observing complex biological systems, which could have a major impact in network medicine, a rapidly developing field that applies systems biology and network science methods to human disease.

I received my Ph.D in Physics from the University of Illinois at Urbana-Champaign in May 2009. My Ph.D thesis focused on the study of an unexpected universal behavior in disordered magnetic systems. This work has been featured in Europhysics News and selected for the Europe Physics Letter — Best of 2009 Collection. In addition to the theoretical study of statistical physics, I have collaborated with condensed matter experimentalists on the modeling of perpendicular recording media, which is the state-of-the-art magnetic storage application. I have also been collaborated with experimental biophysicists on developing an efficient algorithm for time-binned data analysis in single-molecule experiments of living cells.

I served as a Postdoctoral Research Associate in the Center for Complex Network Research (CCNR) at Northeastern University (NEU) from June 2009 to September 2012. From October 2012 to July 2013, I was employed as a Research Assistant Professor in CCNR, before joining the Channing Division of Network Medicine. My research has focused on the quantitative study of the dynamic properties of complex systems. In particular, I found that by exploring the underlying network structure of complex systems, one can determine the driver (or sensor) nodes that with time-dependent inputs (or measurements) will enable us to fully control (or observe) the whole system. These findings significantly further our understanding of the intricate interplay between the structural and dynamical properties of complex systems. My work has been featured as a cover story in Nature (May 12, 2011) and a cover story in the Proceedings of the National Academy of Sciences of the U.S.A (Feb. 12, 2013), and received broad media coverage including Nature News & Views, Science News & Analysis, ScienceNews, ScienceDaily, Wired, PHYSORG, and Faculty of 1000.

In summary, my primary academic focus has been interdisciplinary research on controlling and observing complex biological systems. In addition to research, I am interested in teaching and professional service. I have been on the Program Committees of five international meetings, on the Editorial Board of one international journal, and an ad hoc reviewer for 32 international journals (including Science, Nature Physics, Nature Communications, Physics Review Letters, IEEE Transactions on Automatic Control, etc).

Bibliographic
Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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  1. Vinayagam A, Gibson TE, Lee HJ, Yilmazel B, Roesel C, Hu Y, Kwon Y, Sharma A, Liu YY, Perrimon N, Barabási AL. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proc Natl Acad Sci U S A. 2016 May 03; 113(18):4976-81. PMID: 27091990; PMCID: PMC4983807 [Available on 11/03/16].
  2. Liu YY, Slotine JJ, Barabási AL. Observability of complex systems. Proc Natl Acad Sci U S A. 2013 Feb 12; 110(7):2460-5. PMID: 23359701; PMCID: PMC3574950.
  3. Pósfai M, Liu YY, Slotine JJ, Barabási AL. Effect of correlations on network controllability. Sci Rep. 2013; 3:1067. PMID: 23323210; PMCID: PMC3545232.
  4. Zhao JH, Zhou HJ, Liu YY. Inducing effect on the percolation transition in complex networks. Nat Commun. 2013; 4:2412. PMID: 24013476.
    View in: PubMed
  5. Jia T, Liu YY, Csóka E, Pósfai M, Slotine JJ, Barabási AL. Emergence of bimodality in controlling complex networks. Nat Commun. 2013; 4:2002. PMID: 23774965.
    View in: PubMed
  6. Liu YY, Csóka E, Zhou H, Pósfai M. Core percolation on complex networks. Phys Rev Lett. 2012 Nov 16; 109(20):205703. PMID: 23215509.
    View in: PubMed
  7. Liu YY, Slotine JJ, Barabási AL. Control centrality and hierarchical structure in complex networks. PLoS One. 2012; 7(9):e44459. PMID: 23028542.
    View in: PubMed
  8. Liu YY, Slotine JJ, Barabási AL. Controllability of complex networks. Nature. 2011 May 12; 473(7346):167-73. PMID: 21562557.
    View in: PubMed
  9. Liu Y, Park J, Dahmen KA, Chemla YR, Ha T. A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis. J Phys Chem B. 2010 Apr 29; 114(16):5386-403. PMID: 20361785.
    View in: PubMed
  10. Liu Y, Dahmen KA. Unexpected universality in static and dynamic avalanches. Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Jun; 79(6 Pt 1):061124. PMID: 19658490.
    View in: PubMed
  11. Liu Y, Dahmen KA. No-passing rule in the ground state evolution of the random-field Ising model. Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Sep; 76(3 Pt 1):031106. PMID: 17930198.
    View in: PubMed
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Funded by the NIH/NCATS Clinical and Translational Science Award (CTSA) program, grant number UL1TR001102, and through institutional support from Harvard University, Harvard Medical School, Harvard T.H. Chan School of Public Health, Beth Israel Deaconess Medical Center, Boston Children's Hospital, Brigham and Women's Hospital, Massachusetts General Hospital and the Dana Farber Cancer Institute.