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Principal Investigator
Computational Biology and Medical Ecology Lab
Zhanshan (Sam) Ma   Ph.D.  
Title Principal Investigtor
Phone +86 871 65183700
Fax +86 871 65183700
Address Kunming Institute of Zoology, the Chinese Academy of Sciences No. 32 Jiaochang Donglu, Kunming, Yunnan, 650223, P.R.China
Zip Code 650223
   Education and Appointments:

Prof. Sam Ma received his double PhDs in Computer Science and Entomology in 2008 and 1997, respectively, both from the University of Idaho (UI), USA. In 2010, he was retained as a Professor and Principal Investigator (PI) by Kunming Institute of Zoology (KIZ), the Chinese Academy of Sciences (CAS). Prior to joining in KIZ, he was a Research Scientist (in Computational Biology & Computer Science) at the University of Idaho. He was a senior software engineer from 1998 to 2006 in the computer industry in Silicon Valley, USA. Dr. Ma has been keeping dual track publishing in both Computer Science and Biology (particularly in biomedicine in recent years) with over 100 peer-refereed papers in premier platforms such as IEEE Transactions on Reliability, Science Translational Medicine, Ecological Monographs, The ISME Journal, Advanced Science, Science Advances, and iScience. He was a member of London-based “Faculty 1000 of Biology and Medicine” (2008-2016). He heads the “Computational Biology and Medical Ecology Lab” in Kunming Institute of Zoology, the Chinese Academy of Science (CAS). He is also faculty members in the CAS Center for Excellence in Animal Evolution and Genetics and the University of Chinese Academy of Sciences.


Software Packages & Monograph


[1] DBG2OLC: An ultra efficient de Novo genome assembler for the 3rd generation sequencing technologies (PacBio & Oxford Nanopore)]. Available at:    


[2] Sparc: A sparsity-based consensus algorithm for long erroneous 3rdGS sequencing reads. Available at:                                                              


[3] SparseAssembler: Sparse k-mer Graph for Memory Efficient de novo Genome Assembly. The core algorithm (Sparse k-mer) was implemented in BGI’s SoapDenovo-II, the updated version of BGI’s flagship software SoapDenovo]. Available at:               


[4] 10x-assisted-3GS Hybrid Assembly:

Ma ZS et al. (2018) Hybrid assembly of ultra-long Nanopore reads augmented with 10×-genomics contigs: Demonstrated with a human genome. Genomics, vol. 110,


[5] HPTree: Reconstructing evolutionary trees in parallel for massive sequences. BMC System Biology.


[6] Ma ZS et al. (2017) “Bioinformatics: Computing and Software.” Science Press, Beijing.




  Professional Services

Faculty 1000 of Biology and Medicine, UK (2008-2016)

PeerJ Computer Science, USA (Current)

Bullard Fellow, Harvard University, USA (2021-)

   Research Interests:

Genome Assembly Algorithms and Software

Medical Ecology of Human Microbiome

Complex Networks and Evolutionary Game Theory

Evolutionary Computing and Computational Intelligence  

Reliability, Survivability and Resilience

  Supported Projects:


(1) The Chinese Academy of Sciences (CAS) startup funding for the “One-Hundred Talented PI Plan” (PI, Year 2010-2014)

(2) The CAS startup funding for the “Exceptional Overseas Technologist Program” (PI, 2011-2014)

(3) National Science Foundation of China (NFSC) grant: “The Ecological Theater and the Evolutionary Computing Play” (PI, 2012-2015)

(4) National Science Foundation of China (NFSC) grant: “Reliability, survivability and resilience networked systems” (PI, 2015-2018)

(5) National Science Foundation of China (NFSC) grant: “Rapid biodiversity measurement with metagenetics.” (2012-2015) (Main Contributor)

(6) Special Funding for the “Exceptional Scientists in Science and Technology” by Yunnan Province: “GPU Computing for Computational Bioinformatics and Personalized Medicine” (PI, 2012-2015)

(7) Funding for the “Top Talents from Overseas” of Yunnan Province (PI, 2012-2015)

(8) “Cloud Ridge Technology Leadership Grant” of Yunnan Province (PI, 2015-2020)

(9) A China-US collaboration grant for the big data analytics in genomics and metagenomics (2017-2020)  

(10) National Science Foundation of China (NFSC) grant: “Medical ecology of the human microbiome-associated diseases” (PI, 2020-2023)

  Public Services:
  Selected Publications:

1. Ma ZS (2020) Predicting the Outbreak Risks and Inflection Points of COVID19 Pandemic with Classic Ecological Theories. Advanced Science,

2. Ma ZS (2020) Critical network structures and medical ecology mechanisms underlying human microbiome-associated diseases. iScience. doi: 10.1016/j.isci.2020.101195.

3.  Ma ZS (2020) Testing the Anna Karenina Principle in human microbiome-associated diseases. iScience, 23(4):101007.

4. Ma ZS (2020) Heterogeneity-disease relationship in the human microbiome associated diseases. FEMS Microbiology Ecology, Volume 96, fiaa093. doi:10.1093/femsec/fiaa093.

5. Ma ZS & Taylor RAJ (2020) Human reproductive system microbiomes exhibited significantly different heterogeneity scaling with gut microbiome, but the intra-system scaling is invariant. Oikos, 129 (6): 903-911.

6. Ma ZS, Li LW, Zhang YP (2020) Defining individual-level genetic diversity and similarity profiles. Scientific Reports, 10(1): 5805.

7. Li WD & Ma ZS (2020) A theoretic approach to the mechanism of gut microbiome translocation in SIV-infected Asian macaques. FEMS Microbiology Ecology, Volume 96, Issue 8, August 2020, fiaa134,

8. Li WD & Ma ZS (2020) Dominance network analysis of the healthy human vaginal microbiome not    dominated by Lactobacillus species. Computational and Structural Biotechnology Journal.

9.  Li WD, Ma ZS (2020) FBA Ecological Guild: Trio of Firmicutes-Bacteroidetes Alliance against  actinobacteria in human oral microbiome. Scientific Reports, 10(1): 287.

10. Li WD & Ma ZS (2020) Population-level diversity-disease relationship (DDR) in the human microbiome associated diseases. Frontiers in Microbiology. (in Revision)

11. Li WD, Sun Y, et al (2020) Ecological and network analyses reveal four microbial species with potential significance for the diagnosis/treatment of ulcerative colitis (UC). (Under Review)

12. Li LW, Li WD, Zou Q, Ma ZS (2020) Network analysis of the hot spring microbiome sketches possible niche differentiations among ecological guilds. Ecological Modelling, vol. 431. doi: 10.1016/j.ecolmodel.2020.109147.

13. Li LW, Ma ZS (2020) Species sorting and neutral theory analyses reveal archaeal and bacterial communities are assembled differently in hot springs. Frontiers in Bioengineering and Biotechnology, 8. doi: 10.3389/fbioe.2020.00464. 

14 Luo J, Chai J, et al. (2020) From asymmetrical to balanced genomic diversification during rediploidization: Subgenomic evolution in allotetraploid fish. Science Advances. DOI: 10.1126/sciadv.aaz7677

15. Zhang Y & Zhang Z et al. (2020) Selective loss of 5hmC promotes neurodegeneration in the mouse model of Alzheimer's disease. The FASEB Journal,

16. Sun Y, Li LW et al (2020) Does ulcerative colitis influence the spatial heterogeneity of the intestinal mucosal microbiome? Evolutionary Bioinformatics

17. Li LW, Ma ZS (2020) Modeling the microbiome assembly of breast tissues with tumors via neutral, near neutral and niche-neutral hybrid models. (Under Review)

18. Chen HJ, Yi B et al. (2020) Diversity scaling of human digestive tract (DT) microbiomes: the intra-DT and inter-individual patterns. BMC Microbiology (Under Review)

19. Ma ZS (2020) Niche-neutral theoretic approach to mechanisms underlying biodiversity and biogeography of human microbiomes. Evolutionary Applications.

20. Ma ZS (2020) Estimating the optimum coverage and quality of amplicon sequencing with Taylor's power law extensions. Frontiers in Bioengineering and Biotechnology, 8:372.

21. Ma ZS (2020) Assessing and interpreting the metagenome heterogeneity with power law. Frontiers in Microbiology, 11: 648.

22. Ma ZS & AM Ellison (2020) Risks and etiology of bacterial vaginosis revealed by species dominance network analysis. (Preprint)

23. Ma ZS & AM Ellison (2020) Towards a unifying diversity-area relationship (DAR) of species- and gene-diversity. (Preprint)

24. Ma ZS (2020) Towards unifying microbiome ecology of metagenomic genes and microbial taxa: critical ecological processes and network structures. Molecular Ecology Resources (Under Review)

25. Ma ZS, Li WD, Shi P (2020) Microbiome-diversity—host-phylogeny relationship in animal gastrointestinal tract microbiome. Open Biology (In Revision)  

26. Ma ZS, Li WD (2019) How man and woman are different in their microbiome: Ecological and network analyses of the microgenderome. Advanced Science, 6(23): 1902054.

27. Ma ZS, Li LW, Gotelli NJ (2019) Diversity-disease relationships and shared species analyses for human microbiome-associated diseases. The ISME Journal, 13: 19111919.

28. Ma ZS, Ellison AM (2019) Dominance network analysis provides a new framework for studying the diversity-stability relationship. Ecological Monographs. 89(2), DOI: 10.1002/ecm.1358.

29. Li LW & Ma ZS (2019) Comparative power law analysis for the spatial heterogeneity scaling of the hot-spring and human microbiomes. Molecular Ecology, 28(11): 2932-2943.

30. Ma ZS (2019) A new DTAR (diversitytimearea relationship) model demonstrated with the indoor microbiome. Journal of Biogeography, 46(1). DOI: 10.1111/jbi.13636

31. Ma ZS, Li LW, Ye CX, Peng MS, Zhang YP (2019) Hybrid assembly of ultra-long Nanopore reads augmented with 10×-genomics contigs: Demonstrated with a human genome. Genomics, 111(6): 1896-1901.

32. Li LW, Ma ZS (2019) Global microbiome diversity scaling in hot springs with DAR (diversity-area relationship) profiles. Frontiers in Microbiology, vol. 10, article 118.

33. Li WD, Ma ZS (2019) Diversity scaling of human vaginal microbial communities. Zoological Research. 40(6): 587-594.

34. Ma ZS & AM Ellison (2018) A unified concept of dominance applicable at both community and species scale. Ecosphere,

35. Ma ZS (2018) Extending species-area relationships (SAR) to diversity-area relationships (DAR), Ecology and Evolution, 8(20): 10023-10038.

36.  Ma ZS (2018) Diversity time-period and diversity-time-area relationships exemplified by the human microbiome. Scientific Reports, 8(1): 7214.

37. Ma ZS (2018) Sketching the human microbiome biogeography with DAR (diversity-area relationship) profiles. Microbial Ecology, vol. 76,   

38.  Ma ZS, Li LW, Li W (2018) Assessing and interpreting the within-Body biogeography of human microbiome diversity. Frontiers in Microbiology, 9:1619.

39. Ma ZS, Li LW (2018) Measuring metagenome diversity and similarity with Hill numbers. Molecular Ecology Resources, 18(6): 1339-1355.

40. Li W, Yuan Y, et al. (2018) A cross-scale neutral theory approach to the influence of obesity on community assembly of human gut microbiome. Frontiers in Microbiology, 9: 2320.

41. Sun Y, Li LW, et al. (2018) The gut microbiota heterogeneity and assembly changes associated with the IBD. Scientific Reports, 9(1): 440. 

42. Ma ZS, Li LW (2018) Semen microbiome biogeography: an analysis based on a Chinese population study. Frontiers in Microbiology, vol. 9: article 3333.

43. Ma ZS, Ye DD (2017) Triospromising in silico biomarkers for differentiating the effect of disease on the human microbiome network. Scientific Reports, 7(1): 13259.

44. Ma ZS (2017) The P/N (Positive-to-Negative Links) ratio in complex networksa promising in silico biomarker for detecting changes occurring in the human microbiome. Microbial Ecology, 75(4): 1063-1073.

45. Ma ZS, Li L (2017) Quantifying the human vaginal community state types (CSTs) with the species specificity index. Peer J, 2017, 5: e3366.

46. Chen HJ, Peng S, et al. (2017) Oral microbial community assembly under the influence of periodontitis. PLoS One, 12(8): e0182259. 

47. Dai L, Kou H, et al. (2017) Does colorectal cancer significantly influence the assembly of gut microbial communities?  Peer J, 5(8): e3383. DOI:10.7717/peerj.3383.

48. Wei L, Xing PW, et al. (2017) CPPred-RF: A Sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. Journal of Proteome Research, 16(5): 20442053.

49. Zou Q, Wan S, Zeng X, Ma ZS (2017) Reconstructing evolutionary trees in parallel for massive sequences. BMC Systems Biology, 11(S6): 100. DOI: 10.1186/s12918-017-0476-3.

50. Ma ZS (2017) Measuring microbiome diversity and similarity with Hill Numbers. Chapter 8, in Metagenomics, Elsevier

51. Ye CX & ZS Ma (2016) Sparc: a sparsity-based consensus algorithm for long erroneous sequencing reads. PeerJ. 4:e2016. DOI 10.7717/peerj.2016    

52. Ye CX, C Hill, J Ruan, ZS Ma (2016) DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci Rep, 6: 31900.

53. Li LW & Ma ZS (2016) Testing the Neutral Theory of Biodiversity with Human Microbiome Datasets. Scientific Reports, 6:31448.

54. Ma ZS, Li LW, et al. (2016) Integrated network-diversity analyses suggest suppressive effect of Hodgkins lymphoma and slightly relieving effect of chemotherapy on human milk microbiome. Scientific Reports, 6:28048.

55. Wang Y, Wang R, et al. (2016) Sex Ratio Elasticity Influences the Selection of Sex Ratio Strategy. Scientific Reports, 6(1):39807.

56. Ma ZS, et al. (2016) A Brief Review on the Ecological Network Analysis with Applications in the Emerging Medical Ecology. pp 7-41, In Hydrocarbon and Lipid Microbiology Protocols, Springer Protocols Handbooks. Editors: McGenity T. et al. Springer.

57. Ma ZS, et al. (2015) Network analysis suggests a potentially evil alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities. Scientific Reports, 5: 8275.

58. Ma ZS (2015) Power law analysis of the human microbiome. Molecular Ecology, 24(21):54285445.

59. Ma ZS (2015) Towards computational models of animal cognition, an introduction for computer scientists.  Cognitive Systems Research, 33:42-69.     

60. Ma ZS (2015) Towards computational models of animal communication, an introduction for computer scientists. Cognitive Systems Research, 33:70-99.    

61. Ye CX, Hill C, Ruan J, Ma ZS (2014) DBG2OLC: efficient assembly of large genomes using the compressed overlap graph.  Paper: Software:

62. Li H, Ye DD, et al. (2014) Soil bacterial communities of different natural forest types in China. Plant and Soil, 383.   

63. Guan Q & Ma ZS (2014) Ecological analysis of the human milk microbiome.  Chinese Science Bulletin, 59(22): 2205-2212.     

64. Ma ZS , Yang L, Neilson RP, Hess A, Millar R (2014) A survivability-centered research agenda for cloud computing supported emergency response and management systems. 17pp. The 35th IEEE-AIAA Aerospace Conference (Aerospace 2014), Big Sky, Montana, USA, March 7-15th, doi: 10.1109/AERO.2014.6836515  

65. Zhang ZG, Geng JW, et al. (2014) Spatial heterogeneity and co-occurrence patterns of human mucosal associated intestinal microbiota. The ISME Journal, 8(4): 881893.

66. Ma ZS  (2013) Stochastic Populations, Power Law, and Fitness Aggregation in Genetic Algorithms. Fundamenta Informaticae, vol. 122, pp173-206.    

67. Ma ZS (2013) First passage time and first passage percolation models for analyzing network resilience and effective strategies in strategic information warfare research. I. J. Information and Computer Security, 5(4): 334-358.

68. Ma ZS (2012) Chaotic populations in Genetic Algorithms. Applied Soft Computing, 12(8): 2409-2424.    

69. Ma ZS  (2012) A unified definition for reliability, survivability and resilience inspired by the handicap principle and ecological stability. I. J. of Critical Infrastructures, 8(2): 242-272.   

70. Ma ZS  (2012) A note on extending Taylors power law for characterizing human microbial communities: inspiration from comparative studies on the distribution patterns of insects and galaxies, and as a case study for medical ecology and personalized medicine.    

71. Ye CX, Ma ZS, et al. (2012)  Exploiting sparseness in de novo genome assembly. BMC Bioinformatics, 13(Suppl 6):S1.  

72. Gajer P, Brotman RM, et al. (2012) Temporal Dynamics of the Human Vaginal Microbiota.  Sci Transl Med., 4(132):132ra52. 

73. Ma ZS, et al. (2012) A Bird's Eye View of Microbial Community Dynamics. In Microbial Ecological Theory: Current Perspectives. Editors: LA Ogilvie and PR Hirsch. Caister Academic Press.

74. Ma ZS  & Krings AW. (2011) Dynamic hybrid fault modeling and extended evolutionary Game theory for reliability, survivability and fault tolerance analyses. IEEE Transactions on Reliability, 60(1):180-196.  

75. Ma ZS  (2011) Ecological theater for evolutionary computing play: some insights from population ecology and evolutionary ecology. I. Journal of Bio-Inspired Computing, 4(1):31-55.  

76. Ma ZS  (2011) Frailty modeling for risk analysis in network security and survivability. I. J. Computer and Information Security, 4:276-294. 

77. Ma ZS, et al. (2011) Insect navigation and communication in flight and migration: a potential model for joining and collision avoidance in MAVs (Micro-Aerial Vehicle) and mobile robots fleet control. Proc. of the 32nd IEEE-AIAA Aerospace Conference. 14pp, Big Sky, Montana, USA.

78. Ye CX, Cannon C, Ma ZS*, Yu DW, Pop M* (2011) SparseAssembler2: Sparse k-mer Graph for Memory Efficient Genome Assembly.  

79. Ma ZS & AW Krings et al. (2011) Has the cyber warfare threat been overstated? A cheap talk game-theoretic perspective. The 7th Cyberspace Sciences and Information Intelligence Research Workshop, 7th CSIIRW11. October 14-16, 2011. Oak Ridge National Lab, Oak Ridge, USA.  

80. Ma ZS, et al. (2011) Caring about trees in the forest: incorporating frailty in risk analysis for personalized medicine. Personalized Medicine, 8(6): 681-688   

81. Ma ZS (2010) Is Strategic Information Warfare Really Asymmetric?a New Perspective from the Handicap Principle. Journal of Information Warfare, 9(3): 51-61.  

82. Ma ZS, et al. (2010) Logics in Animal Cognition: Are They Important to Brain Computer Interfaces (BCI) and Future Space Missions? Proc. 31st IEEE-AIAA Aerospace Conference 2010, 8pp. Big Sky, Montana, USA.   

83. Ma ZS (2010) An integrated approach to network intrusion detection with block clustering analysis, generalised logistic regression and linear discriminant analysis. I. J. Information and Computer Security. Vol. 4(1):76-97

84. Ma ZS, FT Sheldon, AW Krings (2010) The handicap principle, strategic information warfare and the paradox of asymmetry. The 6th Cyberspace Sciences and Information Intelligence Research Workshop, 6th CSIIRW10, Oak Ridge National Lab, Oak Ridge, USA.  

85. Ma ZS (2010) A New Extended Evolutionary Game Theory Approach to Strategic Information Warfare Research. J. of Information Warfare, vol. 8(2).   

86. Ma ZS & AW Krings (2009) Is Chaos Theory Relevant to Reliability and Survivability? IEEE-AC paper #1697, 10pp, The 30th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14th, 2009. 

87. Ma ZS (2009) Dragonfly Preying on Flying Insects, Rendezvous Search Games, and Rendezvous and Docking in Space Explorations. IEEE-AC paper #1698, 8pp, The 30th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14th, 2009. 

88. Ma ZS (2009) Cognitive Ecology and Social Learning Inspired Machine Learning: with Particular Reference to the Evolving of Resilient Airborne Networks (AN). IEEE-AC paper #1706, 14pp, The 30th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14th, 2009. 

89. Ma ZS, AW Krings, RE Hiromoto (2009) Dragonfly as a model for UAV/MAV Flight and Communication Controls. IEEE-AC paper #1718, 8pp, The 30th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14th, 2009.

90. Ma ZS & AW Krings (2009) Insect sensory systems inspired computing and communications. Ad Hoc Networks 7(4): 742-755.

91. Ma ZS, AW Krings et al. (2009) The Handicap Principle for Trust in Computer Security, the Semantic Web and Social Networking. Springer Lecture Notes in Computer Science, vol. 5854, pp458-468

92. Ma ZS (2009) Towards a Population Dynamics Theory for Evolutionary Computing: Learning from Biological Population Dynamics in Nature.  Springer Lecture Notes in Artificial Intelligence, vol. 5855, pp 195-205.

93. Ma ZS (2009) Towards an Extended Evolutionary Game Theory with Survival Analysis and Agreement Algorithms for Modeling Uncertainty, Vulnerability, and Deception. Springer Lecture Notes in Artificial Intelligence, vol. 5855, pp 608-618.

94. Ma ZS, AW. Krings, FT. Sheldon (2009) An outline of the three-layer survivability analysis architecture for strategic information warfare research. The 5th Cyberspace Sciences and Information Intelligence Research Workshop, 5th CSIIRW09, Oak Ridge National Lab, Oak Ridge, USA. CSIIRW 2009: 28

95. Ma ZS, AW Krings, RC Millar (2009) Introduction of first passage time (FPT) analysis for software reliability and network security. The 5th Cyberspace Sciences and Information Intelligence Research Workshop, 5th CSIIRW09, Oak Ridge National Lab, Oak Ridge, USA. CSIIRW 2009: 63

96. Krings AW & Ma ZS (2009) Surviving Attacks and Intrusions: What can we Learn from Fault Models. HICSS (Hawaii International Conference on System Sciences) 2009: 1-8.  

97. Ma ZS  (2011) Did we miss some evidence of chaos in laboratory insect populations? Population Ecology, 53:405412.  

98. Ma ZS  (2010) Survival Analysis Approach to Life Table Analysis and Hypothesis Testing for Russian Wheat Aphid Populations. Bulletin of Entomological Research. vol. 100(3): 315-324

99. Ma ZS (2009) A new modelling approach to insect reproduction with same-shape reproduction distribution and rate summation: with particular reference to Russian wheat aphid. Bulletin of Entomological Research, vol. 99(5): 445-455

100. Ma ZS, Bechinski EJ (2008) Life tables and demographic statistics of Russian wheat aphid under different temperatures and host plant stages. European Journal of Entomology, vol. 106:205210

101. Ma, Z.S. & Bechinski, E.J. (2008) Accelerated failure time modeling of the development and survival of Russian wheat aphid, Diuraphis noxia (Mordvilko). Population Ecology 51(4), 543548

102. Ma ZS & EJ Bechinski (2008) A survival-analysis-based simulation model for Russian wheat aphid population dynamics. Ecological Modelling, vol. 216(3):323-332

103. Ma ZS & EJ Bechinski (2008) Developmental and Phenological Modeling of Russian Wheat Aphid (Hemiptera: Aphididae). Annals of the Entomological Society of America, Vol. 101(2):351-361

104. Ma ZS, AW Krings, RE Hiromoto (2008) Insect Sensory Systems Inspired Communications and Computing (II): An Engineering Perspective. Funchal, Maderia, Prtugal, Jan 28-31, 2008,  IEEE BIOSIGNALS, 292-297

105. Ma ZS & AW Krings (2008) Dynamic hybrid fault models and the applications to wireless sensor networks (WSNs). ACM-IEEE MSWiM 2008, Vancouver, Canada, 100-108.

106. Ma ZS & AW Krings (2008) Spatial Distribution Patterns, Power Law, and the Agent-based Directed Diffusion Sensor Networks. 2008 IEEE PerCom (IEEE Pervasive Computing), Hong Kong, March 17-21, 2008: 596-601.

107. Ma ZS & AW Krings (2008) Dynamic populations in genetic algorithms. The 23rd Annual ACM Symposium on Applied Computing, March 16-20, 2008, Fortaleza, Ceara, Brazil. ACM-SAC 2008: 1807-1811

108. Ma ZS & AW Krings (2008) Bio-Robustness and Fault Tolerance: A New Perspective on Reliable, Survivable and Evolvable Network Systems. Paper #1650, 20pp. The 29th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8th, 2008. 

109. Ma ZS & AW Krings (2008) Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM). Paper #1618, 21pp. The 29th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8th, 2008. 

110. Ma ZS & AW Krings (2008) Competing Risks Analysis of Reliability, Survivability, and Prognostics and Health Management (PHM). Paper #1626, 20pp. The 29th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8th, 2008. 

111. Ma ZS & AW Krings (2008) Multivariate Survival Analysis (I): Shared Frailty Approaches to Reliability and Dependence Modeling. Paper #1634, 21pp. The 29th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8th, 2008.

112. Krings, AW & Ma ZS (2006) Fault-Models in Wireless Communication: Towards Survivable Ad Hoc Networks. 2006 MILCOM, DOI: 10.1109/MILCOM.2006.302349, 2006 IEEE Military Communications Conference. 3-25 October 2006, Washington, DC.  














人体菌群多样性与疾病关系的“1/3猜想”提出(新华网, 基金委, ABC NBC等报道)


预测“暗” 生物多样性有了新数学模型(科技日报,新华网基金委报道) 


研究发现全球温泉有超60000种微生物 (新华社报道、基金委等)










  Research Team:


Li Lianwei, PhD in Bioinformatics, The University of Chinese Academy of Sciences

PhD Students (Prior Major)

Li Wendy (Medicine, Bioinformatics)

Chen Hongju (Mathematics, Differential Equations)

Xiao Wanmeng (Forestry, Bioinformatics)

MD Motiur Rahman (Statistics and Machine Learning)

MS Students (Prior Major)

Qiao Yuting (Bioinformatics)

Yang Xu (Entomology)

Guest Professor

Prof. Aaron Ellison

Senior Research Fellow

Fellow of the Ecological Society of America

Harvard University, USA 

Recipient of CAS PIFI (President’s International Fellowship Initiative)


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