INTERESTS
	Algebraic statistics: random graph models, mixture models, non-asymptotic goodness-of-fit tests, Markov bases, existence and complexity of MLE, parameter identifiability Computational and applied algebraic geometry: toric geometry, combinatorics of Graver bases, symbolic and numerical computations Commutative algebra: reductions, determinantal ideals, special fiber rings Biomathematics: algebraic phylogenetic models, Markov bases, model geometry, model identifiability.
COLLABORATORS
	Elizabeth Allman (U. Alaska, Fairbanks), Tristram Bogart (San Francisco State U.), Julia Chifman (Mathematical Biosciences Institute), Alberto Corso (U. Kentucky), Mathias Drton (U. of Chicago), Stephen E. Fienberg (Carnegie-Mellon), Luis Garcia-Puente (Sam Houston State), Raymond Hemmecke (Technische Universitat Munchen), Uwe Nagel (U. Kentucky), John Rhodes (U. Alaska, Fairbanks), Alessandro Rinaldo (Carnegie-Mellon), Aleksandra B. Slavkovic (Penn State), Erik Stokes (Rose-Hulman), Seth Sullivant (North Carolina State), Jan Verschelde (U. Illinois, Chicago), Cornelia Yuen (SUNY Potsdam).
Students:	Elizabeth Gross
SUMMARY
	Algebraic statistics has developed in recent years as a branch of applied algebraic geometry. The field focuses on problems arising in statistics and applications, yet it is fundamentally driven by algebraic methods. Recent advances in the field suggest developments on the computational frontier, including numerical algebraic geometry. The wide applicability of the algebraic and geometric methods offers a good connection with researchers interested in models where standard computational tools do not scale well, for example, social networks, mixture models, and phylogenetics - evolutionary models used in computational biology.