Cigdem Ak, Ph.D.

  • Postdoctoral Scholar, CEDAR, OHSU Knight Cancer Institute, School of Medicine

Biography

Using her mathematics background and multidisciplinary research experiences, she develops and applies novel machine learning algorithms that uncover the underlying dynamics of complex systems in particular those in computational biology and epidemiology.

Her main research interest is designing theoretical and computational approaches to learn good feature representations for the joint analysis of heterogenous sources of data to explore mechanisms of cancer biology. Dr. Ak is currently integrating novel, scalable, and interpretable machine learning solutions into understanding single cell analysis, at CEDAR.

During her PhD studies, she developed interpretable spatiotemporal prediction algorithms. Besides cancer, she also works on modeling spatiotemporal dynamics of COVID-19 with location-specific demographic and socioeconomic characteristics.

Education and training

  • Degrees

    • Ph.D., 2019, Koc University
    • MSc, 2014, École Polytechnique
    • MSc, 2013, École Normale Supérieure
    • B.S., 2012, Université Galatasaray

  • Internship

    • French National Institute for Research in Digital Science and Technology (Inria Lyon, Grenoble)

Memberships and associations:

  • International Society for Computational Biology (ISCB)

Areas of interest

  • Interpretable Machine Learning
  • Multi-modal Data Integration
  • Spatiotemporal modelling
  • Computational Biology
  • Spatial Statistics
  • Precision Medicine
  • Systems Biology

Additional information

Honors and awards

  • Master Excellence Scholarship from Fondation Mathématique Jacques Hadamard, 2013
  • The Ampère Scholarships of Excellence of the ENS de Lyon, 2012

Publications

Elsevier pure profile

Selected publications

  • Ak Ç., Sayar Z., Thibault G., Burlingame E.A., Kuykendall M.J., Eng J., Chitsazan A., Chin K., Adey A.C., Boniface C., Spellman P.T., Thomas G.V., Kopp R.P., Demir E., Chang Y.H., Stavrinides V., Eksi S.E. Multiplex imaging of localized prostate tumors reveals altered spatial organization of AR-positive cells in the microenvironment iScience, 27 (9), art. no. 110668. (2024)
  • Ors A., Chitsazan A.D., Doe A.R., Mulqueen R.M., Ak Ç., Wen Y., Haverlack S., Handu M., Naldiga S., Saldivar J.C., Mohammed H. Estrogen regulates divergent transcriptional and epigenetic cell states in breast cancer Nucleic Acids Research, 50 (20), pp. 11492 - 11508. (2022)
  • Bektaş A.B., Ak Ç., Gönen M. Fast and interpretable genomic data analysis using multiple approximate kernel learning Bioinformatics, 38, pp. I77 - I83. (2022)
  • Ak Ç.*, Chitsazan A.D., Gönen M., Etzioni R., Grossberg A.J. Spatial Prediction of COVID-19 Pandemic Dynamics in the United States ISPRS International Journal of Geo-Information, 11 (9), art. no. 470. (2022)
  • Ak Ç., Ergönül Ö., Gönen M. A prospective prediction tool for understanding Crimean–Congo haemorrhagic fever dynamics in Turkey Clinical Microbiology and Infection, 26 (1), pp. 123.e1 - 123.e7. (2020)
  • Ak Ç., Ergönül Ö., Gönen M. Structured Gaussian Processes with Twin Multiple Kernel Learning Proceedings of Machine Learning Research, 95, pp. 65 - 80. (2018)
  • Ak Ç., Ergönül Ö., Şencan İ., Torunoğlu M.A., Gönen M. Spatiotemporal prediction of infectious diseases using structured Gaussian processes with application to Crimean–Congo hemorrhagic fever PLoS Neglected Tropical Diseases, 12 (8), art. no. e0006737.  (2018)