Research Areas
My broad research interest is in Statistical Bioinformatics with a focus on Statistical Cancer Epigenomics and Cancer Systems Biology. The goal is to use novel advanced computational approaches to help understand oncogenesis and develop novel improved tools for risk prediction and early detection of common cancers.

Brief Biography
Work Experience
09/2013–Present: Professor in Computational Systems Epigenomics. Principal Investigator of Shanghai Institute of Nutrition and Health, CAS/Key Lab of Computational Biology, Shanghai, CAS
09/2008–09/2019: Heller Research Fellow as Principal Research Associate (2008–2010) and then as Group Leader (2010–2013), Royal Society Newton Advanced Fellow (2015–2019), Honorary Research Fellow, UCL Cancer Institute, University College London
09/2013–12/2019: Professor in Computational Systems Epigenomics. Principal Investigator of Max-Planck Partner Institute for Computational Biology, Shanghai, CAS
09/2003–08/2008: Senior Postdoctoral Fellow in Computational Biology, based within the Breast Cancer Functional Genomics Laboratory headed by Professor Carlos Caldas, University of Cambridge, Department of Oncology
08/2001–08/2003: Research Assistant in Mathematical Ecology, based within the Mathematical Biology Group headed by Professor David A. Rand, University of Warwick, Mathematics Institute
06/2000–07/2001: Member of the Complexity Research Group headed by Dr Sverrir Olafsson, British Telecom Labs
Education:
PhD Theoretical Particle Physics, University of Cambridge, May 2000
Certificate of Advanced Study in Mathematics, University of Cambridge, Awarded Distinction, July 1996
BSc (Hon) Mathematical Physics, University of Edinburgh, Awarded 1st Class (Tait Medal), July 1995

Selected Publications (*Corresponding Author)

1. Chang J*, Lu J, Liu Q, Xiang T, Zhang S, Yi Y, Li D, Liu T, Liu Z, Chen X, Dong Z, Li C, Yi H, Yu S, Huang L, Qu F, Wang M, Wang D, Dong H, Cheng G, Zhu L, Li J, Li C, Wu P, Xie X, Teschendorff AE*, Lin D*, Wang X*, Wu C*. Single-cell multi-stage spatial evolutional map of esophageal carcinogenesis. Cancer Cell 2025 Mar;43(3):380–397.e7.
2. Teschendorff AE*, Horvath S*. Epigenetic ageing clocks: statistical methods and emerging computational challenges. Nat Rev Genet 2025 May;26(5):350–368.
3. Tong H, Dwaraka VB, Chen Q, Luo Q, Lasky-Su JA, Smith R, Teschendorff AE*. Quantifying the stochastic component of epigenetic aging. Nat Aging 2024 Jun;4(6):886–901.
4. Zhu T, Liu J, Beck S, Pan S, Capper D, Lechner M, Thirlwell C, Breeze CE*, Teschendorff AE*. A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution. Nat Methods 2022 Mar;19(3):296–306.
5. Teschendorff AE*, Feinberg AP*. Statistical mechanics meets single-cell biology. Nat Rev Genet 2021 Jul;22(7):459–476.
6. Teschendorff AE*. Avoiding common pitfalls in machine learning omic data science. Nat Mater 2019 May;18(5):422–427.
7. Zheng SC, Breeze CE, Beck S, Teschendorff AE*. Identification of differentially methylated cell types in epigenome-wide association studies. Nat Methods 2018 Dec;15(12):1059–1066.
8. Teschendorff AE*, Relton CL*. Statistical and integrative system-level analysis of DNA methylation data. Nat Rev Genet 2018 Mar;19(3):129–147.
9. Teschendorff AE*, Enver T. Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome. Nat Commun 2017 Jun;8:15599.
10. Zheng SC, Beck S, Jaffe AE, Koestler DC, Hansen KD, Houseman AE, Irizarry RA, Teschendorff AE*. Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses. Nat Methods 2017 Feb;14(3):216–217.