Alexander J. Hartemink

Hartemink

Professor in the Department of Computer Science

Computational biology, machine learning, Bayesian statistics, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems biology, computational neurobiology, classification, feature selection

Appointments and Affiliations

  • Professor in the Department of Computer Science
  • Professor of Biology
  • Bass Fellow

Contact Information

Education

  • Ph.D. Massachusetts Institute of Technology, 2001
  • M.Phil. University of Oxford (United Kingdom), 1996
  • B.S. Duke University, 1994

Awards, Honors, and Distinctions

  • Sloan Research Fellowship-Molecular Biology. Alfred P. Sloan Foundation. 2005
  • Faculty Early Career Development (CAREER) Program. National Science Foundation. 2004

Courses Taught

  • COMPSCI 260: Introduction to Computational Genomics
  • COMPSCI 391: Independent Study
  • COMPSCI 393: Research Independent Study
  • COMPSCI 394: Research Independent Study
  • ETHICS 89S: Special Topic: First-Year Seminar in Ethics

In the News

Representative Publications

  • Mitra, S; Zhong, J; Tran, TQ; MacAlpine, DM; Hartemink, AJ, RoboCOP: jointly computing chromatin occupancy profiles for numerous factors from chromatin accessibility data., Nucleic Acids Research, vol 49 no. 14 (2021), pp. 7925-7938 [10.1093/nar/gkab553] [abs].
  • Tran, TQ; MacAlpine, HK; Tripuraneni, V; Mitra, S; MacAlpine, DM; Hartemink, AJ, Linking the dynamics of chromatin occupancy and transcription with predictive models., Genome Res, vol 31 no. 6 (2021), pp. 1035-1046 [10.1101/gr.267237.120] [abs].
  • Tripuraneni, V; Memisoglu, G; MacAlpine, HK; Tran, TQ; Zhu, W; Hartemink, AJ; Haber, JE; MacAlpine, DM, Local nucleosome dynamics and eviction following a double-strand break are reversible by NHEJ-mediated repair in the absence of DNA replication., Genome Res, vol 31 no. 5 (2021), pp. 775-788 [10.1101/gr.271155.120] [abs].
  • Mitra, S; Zhong, J; MacAlpine, DM; Hartemink, AJ, RoboCOP: Multivariate State Space Model Integrating Epigenomic Accessibility Data to Elucidate Genome-Wide Chromatin Occupancy., Res Comput Mol Biol, vol 12074 (2020), pp. 136-151 [10.1007/978-3-030-45257-5_9] [abs].
  • Mitra, S; MacAlpine, D; Zhong, J; Hartemink, A, Data from: RoboCOP: Multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy (2020) [10.7924/r4hx1b43s] [abs].