Rate My Professor Guadalupe Canahuate

Guadalupe Canahuate is a Professor in the Department of Electrical and Computer Engineering at the University of Iowa, having joined the College of Engineering in 2011. She holds a PhD in Computer Science from The Ohio State University, awarded in 2009, and a Master of Science degree in Computer Science from the same institution. Her research specializations encompass big data, machine learning, databases, high-dimensional data analysis, and large-scale indexing. Canahuate applies these expertise areas to biomedical challenges, particularly in head and neck cancer research, where she develops methods for radiomics feature analysis, spatial clustering for risk stratification, prediction of late treatment symptoms using LSTM models and patient-reported outcomes, and visual analytics tools for therapy outcomes.

Among her key publications are 'THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy' (2021, cited over 40 times), 'Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes' (2021), 'Precision risk analysis of cancer therapy with interactive nomograms and survival plots' (2018, cited over 50 times), 'Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas' (2023), and 'Indexing Incomplete Databases' (2006, cited nearly 30 times). With 96 publications, her work has accumulated over 1,500 citations on platforms like Google Scholar and ResearchGate. Canahuate was selected as an innovative early-career engineer for the National Academy of Engineering's 2020 US Frontiers of Engineering Symposium. She has led or co-led grants including NSF Research Experiences for Undergraduates programs, Jumpstarting Tomorrow Seed Grants for addressing health disparities and biases in AI and machine learning tools, and collaborations with MD Anderson Cancer Center. At the University of Iowa, she instructs courses in electrical and computer engineering and computer science, mentors graduate students, and contributes to initiatives in data science, computing, and artificial intelligence.