New Faculty: Elisa Perrone

We are happy to welcome Dr. Elisa Perrone to the faculty of Mathematical Sciences. Her research spans from mathematical statistics to applied statistics. She is mainly interested in multivariate statistics and dependence modeling, with particular emphasis on copulas and their geometric properties. This work includes the development of copula-based approaches used in optimal experimental design and environmental sciences such as hydrology and weather forecasting.

Dr. Elisa Perrone

Prior to joining UMass Lowell, Dr. Perrone was a postdoc at the Massachusetts Institute of Technology and the principal investigator of the project ‘Geometry of discrete copulas for weather forecasting’, funded by the Austrian Science Fund (FWF). Elisa has a doctoral degree from the Johannes Kepler University Linz (Austria).

Dr. Perrone’s faculty website.

New Faculty: Amanda Redlich

We are happy to welcome Dr. Amanda Redlich to the faculty of Mathematical Sciences.   Her research is in probabilistic combinatorics and randomized algorithms.  In her combinatorial work she looks for patterns in big random structures, like social networks.  In her algorithmic work she looks for efficient ways to solve big problems, like analyzing genetic code.  Her most recent work has been on randomized allocation algorithms and biological random graphs.

Dr. Amanda Redlich

Before coming to UML, Amanda worked at Bowdoin College and Rutgers University.  She has a bachelor’s degree from University of Chicago and a PhD from MIT.

Dr. Redlich’s faculty web page

New Faculty: Daniel Glasscock

Last fall, we were happy to welcome Dr. Daniel Glasscock to the UML Department of Mathematical Sciences.

Image of  Dr. Daniel Glasscock.
Dr. Daniel Glasscock

Daniel’s research lies at the intersection of combinatorics and analysis.  He’s interested in applications of tools and ideas in dynamical systems (a branch of analysis with its origins in celestial mechanics) to combinatorics and combinatorial number theory.  Daniel has degrees in math from Rice University (Bachelors), Central European University (Masters), and The Ohio State University (PhD).  He is interested in teaching and research at all levels, and he organizes a yearly math summer study abroad program in Budapest, Hungary.

His faculty web page

New Faculty: Sedi Bartz

This fall we are welcoming Dr. Sedi Bartz to the Department of Mathematical Sciences. Dr. Bartz’ research focuses on topics of nonlinear analysis and variational analysis. He develops refinements of abstract convex analysis, and in turn, transforms his refinements into a unifying language for phenomena in variational analysis which used to be considered quite apart. Dr. Bartz is also a specialist in classical convex analysis and monotone operator theory, theories which are among the most popular tools of modern optimization.

Dr. Bartz holds a Ph.D. from The Technion-Israel Institute of Technology. He arrives to UML after a 3 year term as a Post-Doctoral Fellow at the University of British Columbia, Kelowna, Canada.

New Faculty: Nilabja Guha

This fall, we are happy to welcome Dr. Nilabja Guha to the Department of Mathematical Sciences. Dr. Guha is a statistician who was at Texas A&M University in a postdoctoral position prior to joining UML. He received his doctoral degree in statistics from the University of Maryland Baltimore County where his adviser was Dr Anindya Roy.

Dr Guha’s research interest include Bayesian Modeling, Inverse problems, Uncertainty Quantification, High-dimensional Problems and Graphical Modeling.

Some of his recent publications are

  • Guha, Nilabja, Anindya Roy, Yaakov Malinovsky, and Gauri Datta., 2016. An optimal shrinkage factor in prediction of ordered random effects. Statistica Sinica 26: 1709-1728.
  • Guha, N. and Tan, X., 2017. Multilevel approximate Bayesian approaches for flows in highly heterogeneous porous media and their applications. Journal of Computational and Applied Mathematics, 317, pp.700-717.
  • Yang, K., Guha, N., Efendiev, Y. and Mallick, B.K., 2017. Bayesian and variational Bayesian approaches for flows in heterogeneous random media. Journal of Computational Physics, 345, pp.275-293.