Doctor of Philosophy (Ph.D.)
Applied Mathematics
University of Maryland College Park
1999
See all Profiles
Faculty
Faculty
Katharine Gurski, Ph.D.
Professor
Department/Office
- Mathematics
School/College
- College of Arts & Sciences
Additional Positions
-
Data Science Program
Biography
Katharine F. Gurski, Ph.D. is an associate professor in the Department of Mathematics at Howard University in Washington, D.C. She joined Howard University in 2008 as an assistant professor and became an associate professor in 2013. She also served as co-director of graduate studies in the Graduate School and has held guest and research positions at national research institutions, including the National Institute of Standards and Technology and the National Research Council. Gurski earned a doctorate in applied mathematics from the University of Maryland, College Park, a master’s in applied mathematics and a master’s in physics from the University of Illinois at Urbana-Champaign, and a bachelor’s in physics, summa cum laude, from Emory University.
Gurski’s research focuses on mathematical modeling and analysis with applications to physical and biological problems, including fluid dynamics, infectious disease modeling and computational methods. Her work has appeared in peer-reviewed journals in mathematical biology, numerical analysis and computational science, and she has organized and contributed to national and international workshops, conferences and seminars. She has also received research support from the National Science Foundation, the Simons Foundation and Howard University’s Office of the Provost.
At Howard University, Gurski teaches courses in applied mathematics, differential equations and mathematical modeling, mentoring undergraduate and graduate students in research and independent study. She has been active in professional service, including editorial work and organizing conference sessions, and has contributed to curricular and faculty committees within the Department of Mathematics. Her career reflects a commitment to interdisciplinary collaboration, mathematical inquiry and the development of students in STEM disciplines.
Education & Expertise
Education
Master of Science (M.S.)
Applied Mathematics
University of Illinios Urbana Champaign
1991
Master of Science (M.S.)
Physics
University of Illinois Urbana- Champaign
1989
Bachelor of Science (B.S.)
Physics
Emory University
1987
Research
Research
Funding
Collaborative Research: Linking Pharmacokinetics to Epidemiological Models of Vector-Borne Diseases and Drug Resistance Prevention, National Science Foundation ($199,999). September 2018-August 2021.
Explicit Methods for Extended Time Stepping in Stiff Nonsymmetric Problems, Simons Foundation, Collaboration Grant for Mathematics, ($35,000). September 2012-August 2018.
Focused Research Group: Collaborative Research: Developing Mathematical Algorithms for Adaptive, Geodesic Mesh MHD for use in Astrophysics and Space Physics, National Science Foundation. July 2014-June 2018. ($273,146).
Linking Pharmacokinetics to Epidemiological Models of Vector-Borne Diseases and Drug Resistance Prevention, AIM SQuaRES, American Institute for Mathematics, 2019-2021 (Travel support for group of 6 researchers for 1 week each year for 3 years)
Accomplishments
Accomplishments
Publications and Presentations
Publications and Presentations
A Sexually Transmitted Infection Model
We develop an autonomous population model that can account for the possibilities of an infection from either a casual sexual partner or a longtime partner who was either infected at the start of the partnership or was newly infected. The impact of the long-term partnerships on the rate of infection is captured by calculating the expected values of the rate of infection from these extended contacts. We present a new method to evaluate partner acquisition rates for casual or long-term partnerships which produces in a more realistic number of lifetime sexual partners.
Efficient, Divergence-Free, High Order MHD
Efficient, Divergence-Free, High Order MHD on 3D Spherical Meshes with Optimal Geodesic Meshing
There is a great need in several areas of astrophysics and space physics to carry out high order of accuracy, divergence-free MHD simulations on spherical meshes. This requires us to pay careful attention to the interplay between mesh quality and numerical algorithms.This requires us to pay careful attention to the interplay between mesh quality and numerical algorithms. Methods have been designed that fundamentally integrate high-order isoparametric mappings with the other high accuracy algorithms that are needed for divergence-free MHD simulations on geodesic meshes. The goal of this paper is to document such algorithms that are implemented in the geodesic mesh version of the RIEMANN code.
Intermittent Preventive Treatment (IPT)
We develop an age-structured ODE model to investigate the role of intermittent preventive treatment (IPT) in averting malaria-induced mortality in children, and its related cost in promoting the spread of antimalarial drug resistance. IPT, a malaria control strategy in which a full curative dose of an antimalarial medication is administered to vulnerable asymptomatic individuals at specified intervals, has been shown to reduce malaria transmission and deaths in children and pregnant women. However, it can also promote drug resistance spread. Our mathematical model is used to explore IPT effects on drug resistance and deaths averted in holoendemic malaria regions.
Advancing an interdisciplinary framework to study seed dispersal ecology
Advancing an interdisciplinary framework to study seed dispersal ecology
In this perspective, we provide guidance on integrating empirical and theoretical approaches that account for the context dependency of seed dispersal to improve our ability to generalize and predict the consequences of dispersal, and its anthropogenic alteration, across systems. We synthesize suitable theoretical frameworks for this work and discuss concepts, approaches and available data from diverse subdisciplines to help operationalize concepts, highlight recent breakthroughs across research areas and discuss ongoing challenges and open questions. We address knowledge gaps in the movement ecology of seeds and the integration of dispersal and demography that could benefit from such a synthesis. With an interdisciplinary perspective, we will be able to better understand how global change will impact seed dispersal processes, and potential cascading effects on plant population persistence, spread and biodiversity.
Employing plant functional groups to advance seed dispersal ecology and conservation
Employing plant functional groups to advance seed dispersal ecology and conservation
Building on current frameworks, we here posit that seed dispersal ecology should adopt plant functional groups as analytical units to reduce this complexity to manageable levels. Functional groups can be used to distinguish, for their constituent species, whether it matters (i) if seeds are dispersed, (ii) into what context they are dispersed and (iii) what vectors disperse them. To avoid overgeneralization, we propose that the utility of these functional groups may be assessed by generating predictions based on the groups and then testing those predictions against species-specific data.