معرفی
Jose Perea is an Associate Professor at Northeastern University with joint appointments in the Department of Mathematics and the Khoury College of Computer Sciences. He holds a PhD from Stanford University (2011) and a BSc from Universidad del Valle, Colombia (2006). His research focuses on topological data analysis (TDA), developing methods for dimensionality reduction, periodicity detection, and manifold learning with applications in computational biology, signal processing, and healthcare.
Previous positions include Assistant Professor at Michigan State University (2015–2021) and Visiting Assistant Professor at Duke University (2011–2015). His work has been supported by grants from NSF, DARPA, and other institutions. Notable contributions include the DREiMac and FibeRed algorithms for topological data visualization, and SW1PerS for periodicity analysis in time series.
- Education:
- PhD in Mathematics, Stanford University (2011)
- BSc in Mathematics, Universidad del Valle (Summa cum laude, Valedictorian, 2006)
Research interests include topological signal processing, persistent homology, and machine learning with topological features. He has developed tools for analyzing EEG signals in pediatric sleep apnea and detecting periodic patterns in videos and sensor data. His lab focuses on bridging algebraic topology with practical data science challenges.
Recipient of a 2020 NSF CAREER Award and honored by Lathisms and Mathematically Gifted and Black. Active in interdisciplinary collaborations, including TIER 1 Awards for Northeastern teams. Teaching includes advanced courses on TDA and computational methods.
Labs/Teams: Leads Northeastern's TDA research group, collaborating on projects in computational mathematics and computer science.
