Ana Garcia

Cristina Bicharra has a degree in Civil Engineering from the Universidade Federal do Rio de Janeiro (1983), a Master’s Degree in Computer Aided Civil Engineering – Stanford University (1989) and a Doctorate degree in Computer Aided Civil Engineering – Stanford University (1992). She sat her postdoctoral in 2002, also at Stanford University and, in 2013, at the MIT Business School (2013). She is currently Permanent Professor at the Department of Applied Computing at the Universidade Federal do Estado do Rio de Janeiro (UNIRIO) and a researcher level 2 at CNPq since 1994. Until 2017, she was a Permanent Professor at the Department of Computer Science at Universidade Federal Fluminense, which she joined in 1994. She is also the founder and coordinator, from 1996 to 2017, of the Active Documentation and Intelligent Design laboratory (ADDLabs), a UFF laboratory in partnership with Petrobras that developed more than 20 R&D projects. She coordinates cooperation projects with international institutions: since 2013, one with Universidad Carlos III de Madrid in the area of Environmental Intelligence and, since 2014, another with MIT in the area of Collective Intelligence. She has completed 8 doctoral and 32 master supervisions. Current areas of interest include Artificial Intelligence, Collective Intelligence, Environmental Intelligence and Human-Computer Interaction.

Education

• Ph.D. Stanford University (1987 — 1992)
• Engenheiro Universidade Federal do Rio de Janeiro (1979 — 1983)

Companies

• Full Professor Federal University of the State of Rio de Janeiro (2017)
• Professora Titular (Full Professor) Federal University of the State of Rio de Janeiro (2017)
• Full Professor Universidade Federal Fluminense (2010 — 2017)
• Visiting Scholar MIT Sloan School of Management (2012 — 2014)
• Associate Professor Universidade Federal Fluminense (2006 — 2010)

Skills

The most important

• Higher Education
• E-Learning
• Lecturing

Other

Mathematical Modeling, University Teaching, Teaching, Science, Artificial Intelligence, Machine Learning, Research