martes, 3 de mayo de 2022 12:00 pm -1:00 pm VIRTUAL Please contact Centro de Investigaciones Biológicas Upr firstname.lastname@example.org for link.
Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human–nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. The Information Age provides new opportunities to better understand human– nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human– nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scale
Dr. Titus Brown from the University of California, Davis will be our guest for Topicos next week and will be sponsored by IDI-BD2K.
He will be presenting his seminar on Tuesday (see flyer). He will be covering topics of biological data analysis, data integration, and data sharing. His lab’s primary interest is in genomic, transcriptomic, and metagenomic sequence analysis. He will be available on Tuesday and Thursday to meet with faculty, postdocs or students. If you would like to meet with him, or know someone that might be interested, please contact me (email@example.com) or Gustavo Lopez (firstname.lastname@example.org) to save date/hour for your meeting.
The IDI-BD2K project is pleased to announce a series of seminars by
Professor Elias Moreno from Universidad de Granada.
The main objective of the visit is to disseminate to various audiences Prof. Moreno’s research on the application of rigorous Decision Theory Analyses in the choice of medical treatments. Also, to reinforce his collaborations with local researchers in statistics.
The main activities will be:
Prof Moreno will deliver two conferences in the the Rio Piedras Campus University of Puerto Rico.
The first will given on Monday, February 4, 2019 at 11:30am, Auditorio Jesús Amaral, Escuela de Arquitectura, and will be a general Motivation and Introduction to the problem.
The second will be given on Thursday, February 7, 2019 at 4:00pm in CNL A-211, Natural Sciences, and is a much more technical talk using state of the art methods in Bayesian Model Selection to make specific decisions for different groups eg male-female etc
A third conference will be given on Tuesday, February 5, 2019 at 10:00am in the Centro Comprensivo de Cáncer, motivating the students and faculty to use Statistical Decision Analysis in the choice of treatments as it is done in Europe. (Registration)
Throughout the rest of the week, until Friday, February 8, 2019 Professor Moreno will reactivate his joint research projects in Bayesian Statistics with Profs. Luis Pericchi and Maria Eglee Perez, incorporating students of Mathematics and Computer Science. In particular there is interest in evaluating Machine Learning algorithms using rigorous Statistical Science.
Les escribo para comunicarles sobre actividad organizada por elNational Research Mentoring Network (NRMN). La misma, se llevará a cabo el lunes, 28 de enero de 2019 de 1:00 a 4:00pm en el anfiteatro JGD-123.
The IDI-BD2K program is pleased to present a seminar entitled
Causal network discovery from biomedical data
Gregory Cooper, M.D., Ph.D.
University of Pittsburgh
Date: November 14, 2018
Time: 5:00 PM
Room: CNL A-211
Ciencias Naturales Fase II
UPR Río Piedras
Causal modeling is an up and coming field at the intersection of statistics and machine learning which seeks to discover which features are most responsible for the decisions of ML algorithms. This talk will provide an introduction to concepts and methods for learning causal relationships in the form of causal networks from biomedical and clinical data, including solely observational data. Examples will be given of applying these methods to biomedical data. The talk will also provide pointers to software for learning causal networks from data, including data containing thousands of variables.