Jose Perea
Associate Professor, Interdisciplinary with College of Science
Research interests
- Data science
- Machine learning
- Algorithms and theory
Education
- PhD in Mathematics, Stanford University
- BS in Mathematics, Universidad del Valle — Colombia
Biography
Jose Perea is an associate professor in the Khoury College of Computer Sciences and the College of Science at Northeastern University, based in Boston.
Perea is the inaugural 2022–24 lecturer for the Mathematical Association of America and the National Association of Mathematics, received the 2020 NSF CAREER award, was a 2020 honoree of Lathisms, and was a 2018 honoree of Mathematically Gifted and Black. Prior to joining Northeastern, he was an assistant professor at Michigan State University with joint appointments in the department of computational mathematics, science and engineering, and the department of mathematics.
Perea is originally from Colombia and is passionate about using nonstandard ideas from mathematics to solve problems in data science and machine learning.
Recent publications
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Move schedules: fast persistence computations in coarse dynamic settings
Citation: Matthew Piekenbrock, Jose A. Perea. (2024). Move schedules: fast persistence computations in coarse dynamic settings J. Appl. Comput. Topol., 8, 301-345. https://doi.org/10.1007/s41468-023-00156-3 -
DREiMac: Dimensionality Reduction with Eilenberg-MacLane Coordinates
Citation: Jose A. Perea, Luis Scoccola, Christopher J. Tralie. (2023). DREiMac: Dimensionality Reduction with Eilenberg-MacLane Coordinates J. Open Source Softw., 8, 5791. https://doi.org/10.21105/joss.05791 -
Sliding window persistence of quasiperiodic functions
Citation: Hitesh Gakhar, Jose A. Perea. (2024). Sliding window persistence of quasiperiodic functions J. Appl. Comput. Topol., 8, 55-92. https://doi.org/10.1007/s41468-023-00136-7 -
Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea
Citation: Shashank Manjunath, Jose A. Perea, Aarti Sathyanarayana. (2023). Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea CoRR, abs/2304.14853. https://doi.org/10.48550/arXiv.2304.14853 -
Learning on Persistence Diagrams as Radon Measures
Citation: Alex Elchesen, Iryna Hartsock, Jose A. Perea, Tatum Rask. (2022). Learning on Persistence Diagrams as Radon Measures CoRR, abs/2212.08295. https://doi.org/10.48550/arXiv.2212.08295 -
Toroidal Coordinates: Decorrelating Circular Coordinates With Lattice Reduction
Citation: Luis Scoccola, Hitesh Gakhar, Johnathan Bush, Nikolas Schonsheck, Tatum Rask, Ling Zhou, Jose A. Perea. (2022). Toroidal Coordinates: Decorrelating Circular Coordinates With Lattice Reduction CoRR, abs/2212.07201. https://doi.org/10.48550/arXiv.2212.07201 -
Fiberwise dimensionality reduction of topologically complex data with vector bundles
Citation: Luis Scoccola, Jose A. Perea. (2022). Fiberwise dimensionality reduction of topologically complex data with vector bundles CoRR, abs/2206.06513. https://doi.org/10.48550/arXiv.2206.06513 -
Approximating Continuous Functions on Persistence Diagrams Using Template Functions
Citation: Jose A. Perea, Elizabeth Munch, Firas A. Khasawneh. (2023). Approximating Continuous Functions on Persistence Diagrams Using Template Functions Found. Comput. Math., 23, 1215-1272. https://doi.org/10.1007/s10208-022-09567-7 -
A Comparative Study of Machine Learning Methods for Persistence Diagrams
Citation: Danielle Barnes, Luis Polanco, Jose A. Perea. (2021). A Comparative Study of Machine Learning Methods for Persistence Diagrams Frontiers Artif. Intell., 4, 681174. https://doi.org/10.3389/frai.2021.681174