Kathleen Storey

+1-610-***-**** - ad48ud@r.postjobfree.com - LinkedIn - GitHub

SUMMARY OF QUALIFICATIONS

• Mathematician with 10 years experience in computational modeling with analytical and data-driven techniques.

• Expertise in mathematical oncology, systems biology, statistical analysis, and data-driven model calibration.

• Strong written and oral communication skills, recognized by invited research presentations, publications, and an outstanding teaching award.

• Leadership and teamwork skills developed by collaboration with mathematicians, clinicians, and biologists.

• Quick to learn and implement new technical skills, demonstrated by the creation of new courses and mathematical modeling projects.

TECHNICAL SKILLS

Programming Languages: MATLAB, R, Python, SQL

Libraries and Tools: Pandas, NumPy, SciPy, Sci-Kit Learn, Tableau, Power BI EDUCATION

University of Minnesota Minneapolis, MN, USA

Ph.D. and M.S., Mathematics September 2012 - June 2018 Carleton College Northfield, MN, USA

B.A., magna cum laude, Mathematics September 2008 - June 2012 WORK EXPERIENCE

Assistant Professor

Lafayette College, Easton, PA July 2021 - present

• Communicated course material, and developed assignments, exams, and class activities for a variety of semester-long courses, including Probability, Mathematical Modeling, and Calculus.

• Conducted research and published papers in the field of mathematical oncology with collaborators in mathematics, medicine, and biology.

• Supervised and guided students working on semester-long research projects using MATLAB and HPC. Postdoctoral Assistant Professor

University of Michigan, Ann Arbor, MI August 2018 - May 2021

• Communicated course material on Differential Equations to large classes of engineering undergraduate students, and on Mathematical Modeling to smaller classes of undergraduate and graduate students.

• Supervised graduate students who held weekly lab sessions for my Differential Equations courses.

• Conducted research and published papers in the field of mathematical oncology with collaborators in mathematics, medicine, and biology.

SELECTED PROJECTS

• Classifying biological aggregation patterns with machine learning, Performed a classification comparison on simulation data using unsupervised k-means clustering and supervised linear SVM to two different measurements of collective motion: time series data for traditional biological aggregation order parameters and a topological approach that requires no prior knowledge of the expected patterns. GitHub Publication

• Genomic data analysis using statistical and topological methods, Developed a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects using TDA, differential gene expression, and spectral shape analysis. This method revealed two subgroups of tumor cells with distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods. GitHub Publication

• Experimental design procedure for calibration of prostate cancer models, Developed a Bayesian information-theoretic procedure, using an adaptive score function to determine the optimal data collection times and measurement types for tumor data, to make clinical predictions using differential equation models. GitHub Publication

• Computational agent-based modeling of immunotherapy treatment response, Developed a spatial model of glioblastoma response to a combination of oncolytic viral therapy and immunotherapy, revealing the importance of spatial location in viral dosing. Publication

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