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Predicting Defect Inflow
Predicting Defect Inflow Professors in Global
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Professor
Stefan Hergarten
University of Freiburg
Germany
hazard prediction
risk prediction
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Professor
Catherine Gorle
Stanford University
United States of America
predictive flow simulations
computational predictions
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Professor
Brad Calder
University of California, San Diego
United States of America
value prediction
critical path prediction
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Professor
Professorship Biometry Molecular Medicine
Ludwig Maximilian University of Munich
Germany
prediction modeling
prediction error estimation
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Professor
Sebastian Fossati
University of Alberta
Canada
prediction
time series forecasting
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Professor
Tao Ma
University of Southern California
United States of America
predictive analytics
time series forecasting
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Professor
Miroslaw Staron
Chalmers University of Technology
Sweden
predicting defect inflow
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Professor
Barry Croke
Australian National University
Australia
streamflow prediction
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Professor
Saeid Habibi
McMaster University
Canada
fault diagnosis prediction
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Professor
Michael Ernst
University of Washington
United States of America
bug prediction
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Professor
Jovan Nedic
McGill University
Canada
flow modelling prediction
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Professor
Ujjal Mukherjee
University of Illinois
United States of America
predicting failures
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Professor
Abdallah Elsayed
University of Guelph
Canada
defect inclusion analysis
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Professor
Leora Dresselhaus-marais
Stanford University
United States of America
defect dynamics
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Professor
Nicholas Harris
University of Southampton
United Kingdom
predictive inspection
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Professor
John Whitcomb
Texas A&M University
United States of America
damage prediction
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Professor
Moritz Fieback
Delft University of Technology
Netherlands
device defect modeling
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Professor
Thorsten Trupke
University of New South Wales
Australia
defect characterisation
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Professor
Wenbo Sui
Technical University of Denmark
Denmark
automated defect detection
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Professor
Mike Holenderski
Eindhoven University of Technology
Belgium
performance prediction
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Professor
Joseph Cusumano
Pennsylvania State University
United States of America
machinery failure prediction
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