Noémie Elhadad; Associate Professor of in Biomedical Informatics
Summarizing and Learning from Patient Records
Noémie Elhadad is an Associate Professor of Biomedical Informatics at Columbia University. Her research is in biomedical informatics, natural language processing, and data science. She develops techniques that aim to support clinicians, patients, and health researchers in their information workflow by automatically extracting and making accessible information from unstructured, large clinical datasets (e.g., the electronic patient record) and patient platforms (e.g., online health communities). Her research relies on two types of methods: (1) She designs novel computational approaches that infer models of health phenomena and account for the specific biases of large health data; (2) She translates the learned models into actionable knowledge and robust systems within the healthcare ecosystem.
Francisco J. R. Ruiz; Postdoctoral Research Scientist at the Department of Computer Science in Columbia University and at the Engineering Department in the University of Cambridge
SHOPPER: A Probabilistic Discrete Choice Model of Consumer Behavior
Francisco J. R. Ruiz is a Postdoctoral Research Scientist at the Department of Computer Science in Columbia University and at the Engineering Department in the University of Cambridge. Francisco holds a Marie-Skłodowska Curie Individual Fellowship in the context of the E.U. Horizon 2020 program. He completed his Ph.D. in 2015 and M.Sc. in 2012, both from the University Carlos III in Madrid. His research is focused on statistical machine learning; in particular, his interests include: approximate Bayesian inference, probabilistic modeling for discrete data, applications of Bayesian non-parametrics, and time series models.