兰岳恒
近期热点
资料介绍
个人简历
科研成果一篇发表于PNAS,两篇发表于PRL;另有多篇文章发表于PRA,PRE,J.Chem.Phys.;PlosOne,ScientificReports;Biophys.J.,Bioinformatics;J.Stat.Phys.,J.Stat.Mech.等。总文章数超过50篇,WebofScience总引用次数约450次(googlescholar上总引用约800次,H-index14)学术经历2004.12-2007.2美国北卡大学教堂山分校博士后2007.3-2009.2加州大学圣塔芭芭拉分校博士后2009.3-2016.1清华大学物理系助理教授、副教授2016.3-至今北京邮电大学物理系教授学生培养博士毕业6名,硕士毕业1名。2017年拟招收博士研究生1-2名,硕士研究生2-3名。欢迎具有数学、物理、力学或计算机专业背景的考生报考。研究领域
1.细胞信号传导,生化反应网络的图论和进化论分析.细胞运动,系统生物学.生物信息学.2.斑图形成,流体和复杂体系中的相干结构,反应扩散系统的稳定性.3.非平衡统计物理,随机过程,纳米尺度热力学,场论,非线性和复杂动力学,半经典计算.4.精确解,重正化群在复杂体系和非线性动力学中的应用,5.计算物理、计算生物学和深度学习.近期论文
1.“Stochastic Thermodynamics of a Particle in a Box”, Z. Gong&, Y. Lan* and H. T. Quan*, Phys. Rev. Lett. 117, 180603(2016).2. “A Stochastic Model of the Germinal Center Integrating Local Antigen Competition, Individualistic TVB Interactions, and B Cell Receptor Signaling”, P. Wang&, C. Shi&, H. Qi* and Y. Lan*, J. Immunol. 197, 1169-1182(2016).3. “Channel based generating function approach to the stochastic Hodgkin- Huxley neuronal system”, A. Lin&, Y. Huang, J. Shuai and Y. Lan*, Sci. Rep. 6, 22662(2016).4. “Hierarchical feedbacks and reaction hubs in cell signaling networks”, J. Xu& and Y. Lan*, PLOS One 10(5), e0125886 (2015).5.“Energy dissipation in an adaptive molecular circuit”, S. Wang&*, Y. Lan and L. Tang, J. Stat. Mech., P07025 (2015).6.“The stochastic thermodynamics of a rotating Brownian particle in a gradient flow”, Y. Lan&* and E. Aurell, Scientific Reports 5, 12266 (2015).7.“A variational approach to connecting orbits in nonlinear dynamical systems”, C. Dong& and Y. Lan*, Phys. Lett. A 378, 705(2014).8.“SEK:sparsity exploiting k-mer-based estimation of bacterial community composition”, S. Chatterjee&*, D. Koslicki, S. Dong, N. Innocenti, L. Chen, Y. Lan, M. Vehkaper¨a, M. Skoglund, L. K. Rasmussen, E. Aurell and J. Corander, Bioinformatics 30, 2423(2014).9. “Organization of spatially periodic solutions of the steady Kuramoto-Sivashinsky equation”, C. Dong& and Y. Lan*, Commun. Nonl. Sci. Numer. Simul. 19, 2140(2014).10. “Improved contact prediction in proteins: using pseudolikelihoods to infer potts models”, M. Ekeberg&*, C. L¨ovkvist, Y. Lan, M. Weigt and E. Aurell, Phys. Rev. E 87, 012707(2013). 相关热点