Safiye Celik

  Address: Paul G. Allen Center for Computer Science & Engineering Box 352350 185 Stevens Way Seattle, WA 98195-2350
  Email: safiye [at]

  Curriculum Vitae: [pdf]

I am a PhD student in Paul G. Allen School of Computer Science and Engineering at the University of Washington, supervised by Su-In Lee. I was granted PhD candidacy in 2016 and MS degree in 2014. Before PhD, I also got a standalone MS degree in Computer Engineering from Bogazici University, supervised by Ali Taylan Cemgil. I got my BS in Computer Engineering from Middle East Technical University.

I am a final-year PhD student in Computer Science and Engineering at the University of Washington, advised by Su-In Lee. I am interested in developing machine learning algorithms to address the problem of high-dimensionality in systems biology applications to introduce promising ways for personalized medicine. Most recently, I have been working on identifying robust biomarkers that represent important molecular events underlying complex diseases like Alzheimer’s, or response of cancer patients to different chemotherapy drugs or drug combinations.

Before starting PhD, I worked for two years as a researcher at the Scientific and Technological Research Council of Turkey (TUBITAK), where I developed pattern recognition algorithms for information extraction from huge spatio-temporal databases, and three years as a software engineer at MilSOFT Software Technologies Inc. where I developed tools for automatic analysis and enhancement of images captured by unmanned air vehicles and a compiler/parser for NATO Message Handling System for search and rescue ship communication tasks.


  • Safiye Celik, Josh C Russell, Cezar R Pestana, Ting-I Lee, Shubhabrata Mukherjee, Paul K Crane, Dirk Keene, Jennifer F Bobb, Matt Kaeberlein, Su-In Lee. DECODER: A probabilistic approach to integrate big data reveals Complex I as a potential therapeutic target for Alzheimer's disease. Under review in Genome Medicine. Accepted for oral presentation at ICML Workshop in Computational Biology (WCB) 2018. [bioRxiv link] [ICML WCB short paper pdf]

  • Ayse B. Dincer, Safiye Celik, Naozumi Hiranuma, Su-In Lee. DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia. ICML WCB 2018. [bioRxiv link]

  • Joseph D. Janizek, Safiye Celik, Su-In Lee. Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine. ICML WCB 2018. [bioRxiv link]

  • Nicasia Beebe-Wang, Safiye Celik, Su-In Lee. MD-AD: Multi-task deep learning for Alzheimer's disease neuropathology. ICML WCB 2018. [bioRxiv link]

  • F. Linzee Mabrey, Sylvia Chien, Timothy Martins, James Annis, Robert A Beckman, Lawrence A Loeb, Andrew Carson, Brad Patay, C. Anthony Blau, Vivian G Oehler, Safiye Celik, Su-In Lee, Raymond J Monnat Jr, Janis L Abkowitz, Frederick R Appelbaum, Elihu H Estey, Pamela S Becker. High Throughput Drug Screening of Leukemia Stem Cells Reveals Resistance to Standard Therapies and Sensitivity to Other Agents in Acute Myeloid Leukemia. Under review in Leukemia.

  • Su-In Lee*, Safiye Celik*, Benjamin A Logsdon, Scott M Lundberg, Timothy J Martins, Vivian G Oehler, Elihu H Estey, Chris P Miller, Sylvia Chien, Akanksha Saxena, Anthony Blau and Pamela S Becker. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nature Communications 2018 Jan 3;9(42) (* = equal contribution). [paper link] [project website]
  • Safiye Celik, Benjamin A Logsdon, Stephanie Battle, Charles W Drescher, Mara Rendi, David Hawkins and Su-In Lee. Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer. Genome Medicine 2016 Jun 10;8(1):66. [paper link] [project website]
  • Safiye Celik, Benjamin A Logsdon and Su-In Lee. Efficient Dimensionality Reduction for High-Dimensional Network Estimation. 2014 International Conference on Machine Learning (ICML). [pdf] [supplement] [project website]

  • Safiye Celik, Benjamin A Logsdon and Su-In Lee. Sparse Estimation of Module Gaussian Graphical Models with Applications to Cancer Systems Biology. 2013 NIPS Workshop on Machine Learning in Computational Biology (MLCB). [pdf]