Alexander Statnikov, Ph.D.

  • Adjunct Associate Professor, Department of Medicine, Division of Translational Medicine, NYU School of Medicine


227. E. 30th Street 7th Floor
New York, NY 10016

Administrative Contact:
Jeanne Webster

Curriculum Vitae

Alexander Statnikov is an Adjunct Associate Professor in the Department of Medicine (Division of Translational Medicine) and Center for Health Informatics and Bioinformatics at New York University Langone Medical Center. He has founded and headed the Computational Causal Discovery Laboratory that developed causal discovery methods and systems for critical science and service operations. Furthermore, in the capacity as Benchmarking Director of the Best Practices Integrative Informatics Consultation Service, Dr. Statnikov was operationally responsible for the best practice-related benchmarking of informatics methods. Dr. Statnikov is also an Associated Faculty at NYU Center for Data Science.

Dr. Statnikov holds a Ph.D. in Biomedical Informatics from Vanderbilt University (2008), a Master’s in Biomedical Informatics from Vanderbilt University (2005), a Master’s in Applied Mathematics from Case Western University (2002), and a Bachelor’s in Mathematics from Case Western University (2001). His prior professional work experience includes 7 years of employment as a Senior Scientific Programmer (Data Scientist) at Vanderbilt University.

Dr. Statnikov is an internationally recognized expert in data science, machine learning, bioinformatics, and medical informatics. He is an inventor and developer of computational methods and software systems for analysis of biomedical and other high-dimensional data, including methods and software for causal network analysis, variable/feature selection, and predictive modeling/supervised classification. Dr. Statnikov is author of more than 50 peer-reviewed articles in the premier journals and forums, 4 books and monographs, and inventor of several U.S. patents.

Research Interests: (1) Computational causal discovery in high-dimensional biomedical data; (2) Translational bioinformatics and development, optimization, and validation of molecular signatures; (3) Machine learning applications in biomedicine.

For more information, visit the center homepage and lab homepage.