Daniel Pimentel-Alarcon

Daniel Pimentel-Alarcon is an Assistant Professor at the University of Wisconsin-Madison with a joint appointment at the Wisconsin Institute for Discovery. He holds affiliations in Electrical and Computer Engineering within the College of Engineering. His research focuses on developing theory and algorithms for learning from messy data, addressing challenges like outliers, missing values, and small sample sizes. Techniques from optimization, signal processing, algebraic geometry, and machine learning underpin his work, driven by applications in biomedicine and astronomy.

Education:

• PhD in Electrical and Computer Engineering (UW-Madison, advised by Robert Nowak)
• Master's in Mathematics and Electrical & Computer Engineering (UW-Madison)
• Undergraduate degrees in Telematics and Computer Engineering (ITAM, Mexico City)

Research Interests: Machine learning, signal processing, optimization, statistics, algebraic geometry, and interdisciplinary data science applications. Current efforts explore fundamental limits of learning from noisy/sparsely sampled data and practical algorithmic solutions.

Labs & Affiliations: Wisconsin Institute for Discovery (WID), UW-Madison College of Engineering.