I am broadly interested in the areas of machine learning, statistical inference, and information theory. My work focuses on developing tools for identifying structure in large datasets using techniques from these fields.
Email: dnreshef <at> mit <dot> edu
Selected PublicationsEquitability Analysis of the Maximal Information Coefficient, with Comparisons
D. Reshef*, Y. Reshef*, M. Mitzenmacher**, P. Sabeti**
(*,** these authors contributed equally)
Preliminary version at:
Factors Related to Increasing Prevalence of Resistance to Ciprofloxacin and Other Antimicrobial Drugs in Neisseria gonorrhoeae, United States
E. Goldstein , R. Kirkcaldy, D. Reshef, S. Berman, H. Weinstock, P. Sabeti, C. Del Rio, G. Hall, E. Hook, M.Lipsitch
Emerging Infectious Diseases, 2012.
Detecting novel associations in large datasets
D. Reshef*, Y. Reshef*, H. Finucane, S. Grossman, G. McVean, P. Turnbaugh, E. Lander,
M. Mitzenmacher**, P. Sabeti** (*, ** these authors contributed equally)
[Manuscript] [Accompanying Science Perspective] [Science Podcast] [Project Website (reprints)]
On measures of dependence
Graduate thesis (University of Oxford), Department of Statistics, advised by Gilean McVean, 2011.
Oseltamivir for treatment and prevention of pandemic influenza A/H1N1 virus infection in households, Milwaukee, 2009
E. Goldstein, B. Cowling, J. O'Hagan, L. Danon, V. Fang, A. Hagy, J. Miller, D. Reshef, J. Robins, P. Biedrzycki, M. Lipsitch
BMC Infectious Diseases, 2010.
Development of high-throughput drug screening assay for membrane repair
D. Reshef, E. Gallardo, S. Gibb, L. Glover, J. Landers, I. Illa, R. Brown Jr.
Presented at International Dysferlin Conference, 2007.