Dr Eugénie Hunsicker

Pronouns: She/her or they/them
  • Visiting Fellow

Education:

  • Ph. D. University of Chicago 1999
  • M. Sc. University of Chicago 1993
  • B. A. Haverford College, Haverford, PA 1992 Magna Cum Laude, high departmental honors
  • Exchange Oxford University, Oxford, UK 1991
  • Phi Beta Kappa 1991

Employment:

  • 2006-2022: Lecturer, Senior Lecturer & Reader in Mathematics, º¬Ðß²ÝÊÓƵ.
  • 2005-2006: Associate Professor of Mathematics, Lawrence University.
  • 1999-2005: Assistant Professor of Mathematics, Lawrence University.

Professional Memberships:

Research leadership

  • Director of Plastrax research group for the development of technologies for tracking and characterising microplastics

Research interests in statistics and data science:

  • Analysis of high dimensional and functional data
  • Metabolomics, including statistics for biomarker identification and process control
  • Statistical methods for image data, particularly in physical and materials sciences
  • Topological and geometric methods in statistics and machine learning
  • Analysis of clinical data for epidemiology and medical policy making
  • Signal processing for nanopore sensors

Research interests in mathematics:

  • Elliptic PDE on noncompact and singular manifolds
  • Pseudodifferential operator calculi
  • Hodge and signature theorems for singular and noncompact manifolds
  • Intersection cohomology and its generalizations
  • Monopole and other moduli spaces arising in physics
  • Numerical techniques for spectral problems with singular potentials
  • Applications of category theory to systems engineering

Research videos and slides

Introductory talks

Research talks

External research activities

Lecturer at graduate summer schools:

External workshops/semesters organised:

  • “Hausdorff Institute for Mathematics workshop in honour of Prof. Werner Mueller’s 60th birthday” HIM, 2010;
  • “Topology of Stratified Spaces” MSRI, 2008;
  • MSRI semester, “Analysis on Singular Spaces” 2008;
  • “L^2-harmonic forms in geometry and string theory” ARCC, 2004

Honorary Officer for Equality, Diversity and Inclusion for the Royal Statistical Society

Chair of the Athena Forum

Videos related to Diversity

Writings related to Diversity

Work and Awards

Current PhD Students

  • Kerry Rosenthal: Statistical Methods for Compact Mass Spectrometry in Metabolomics (joint with M. Lindley, M. Turner, and E. Ratcliffe)
  • Nayani Adhikari: Diagnosis and Retraining of Asthmatic and Dysfunctional Breathing Techniques Using Opto-Electronic Plethsymography (joint with S. Winter and M. Pain)
  • Jennifer Ferris: Statistical Models of Lower Limb Soft Tissue for Integration into Multimodal Imaging Technologies (joint with S. Winter and D. Zhou)

Plastrax Research Group PhD Students

  • Imoleayomide Ajayi: Data Science for Microplastics Characterisation (joint with Z. Zhou and M. Platt)
  • Symiah Barnett: At-site Microplastic Monitoring for Rivers and Marine Environments (Funded by CENTA, joint with M. Platt and E. Baynes)
  • Shadab Soheilian: Characterisation of Micro- and Nano-plastics in Complex Systems (joint with Z. Zhou)
  • Elizabeth Christie: Developing New Analytical Technology for the Rapid Identification and Quantification of Micro/Nanoplastics (joint with M. Platt and E. Baynes)
  • Beth Jordan: Engineered Polymetric Micro- and Nanoplastics (Supervisors F. Hatton, B. Cousins, E. Baynes)

Past PhD Students

  • Lei Ye (º¬Ðß²ÝÊÓƵ, 2021)
  • Steff Farley (º¬Ðß²ÝÊÓƵ, 2021)
  • Yanis Bahroun (º¬Ðß²ÝÊÓƵ, 2020)
  • Navid Bari (º¬Ðß²ÝÊÓƵ, 2017)
  • Vladimir Lukiyanov (º¬Ðß²ÝÊÓƵ, 2016)
  • Kamil Mroz (º¬Ðß²ÝÊÓƵ, 2014)
  • Louis Omenyi (º¬Ðß²ÝÊÓƵ, 2014)
  • Nikolaos Roidos (º¬Ðß²ÝÊÓƵ, 2010)

Students interested in undertaking a self-funded PhD with me are encouraged to get in contact!

Possible project areas include:

  • Inverse problems for nanopore sensors
  • Category Theory for architecture definition in systems engineering and digital twins
  • Bayesian methods for data integration in materials science
  • Shape statistics in biomechanics
  • Mixed methods (AI/statistics/social sciences/humanities) for understanding mechanisms of inequality in science.