Alex Cooke

Project analyzing tornado occurrence, intensity, and path characteristics in Caribou, Maine, using principal component analysis and regularized regression to overcome challenges posed by a sparse dataset and numerous correlated environmental parameters. The research identifies key meteorological predictors, particularly lower atmospheric conditions like lifting condensation level and critical angle, to enhance operational forecasting accuracy in a northern, low-frequency tornado environment.

M.S. thesis that uses tornado debris signature height, storm motion vectors, and population data to issue real-time guidance on issues PDS tornado warnings and tornado emergencies in order to reduce forecaster load in high-stress situations and increase warning accuracy.

Project analyzing meteorological predictors of quasi-linear convective system (QLCS) tornadoes in the Memphis County Warning Area, employing principal component analysis and regularized regression to handle high-dimensional data from a limited number of events. The research identifies mid-level lapse rates as a key predictor of QLCS tornado frequency, improving model accuracy and providing critical insights for operational forecasting despite inherent analytical challenges.

Received student grant from American Meteorological Society to attend 2025 Annual Meeting and assist presenters with any technical requirements.

Statistical analysis of growing season length evolution in approximately 40 areas in Spokane, Washington area.

Examination of relationship between large-scale climate oscillations and fire season severity in Spokane, Washington area in order to establish statistical predictors for operational purposes. Project included 30-minute presentation for NWS Spokane Office.

12-minute presentation on radar analysis of 2007 Greensburg tornado. Topics included unusual reflectivity characteristics, three-dimensional analysis, velocity analysis, X-Pol data analysis, and use of wide range of radar products to describe meteorological phenomena.

Co-author of "Crystals of sound: applying the physics of phase transitions to musical intonation" published in Empirical Musicology Review.

Presentation given at the 2023 IUPUI Assessment Institute on the process of creating a successful structure of assessment-based practices at the Cleveland Institute of Music.

$11,000 grant for investigating connections between physics of phase transition and musical tuning systems. Developed algorithm for stochastic generation of music that culminated in concert and film scoring software. Wrote piece co-composed by algorithm, representing future possibilities of AI-assisted composition.

M.S. thesis developing an algorithmic stochastic music system using probability-driven harmonic transitions to generate tonally coherent music that can dynamically recognize and shift tonal centers. Employs entropy analysis and stochastic matrices to model harmonic progressions and melodic modulations, enabling applications ranging from automated music composition to analytical tools for music theory and film scoring.