Personal Information
Organización/Lugar de trabajo
San Francisco Bay Area United States
Ocupación
CEO and Co-Founder at R7 Speech Sciences
Sector
Technology / Software / Internet
Sitio web
http://www.cs.jhu.edu/~delip
Acerca de
RECRUITERS: I AM NOT LOOKING FOR A JOB.
Delip Rao is the founder of R7 Speech Sciences, a San Francisco based company focusing on turning human conversations into structured datasets.
In the past, Delip also founded Joostware, an AI research consulting company and The Fake News Challenge, an initiative to bring AI researchers across the world to work on fact-checking related problems.
Delip is an accomplished researcher who specializes in scalable approaches for large-scale datasets dealing with real-world situations. He has worked on NLP and ML research problems involving semi-supervised learning, graph-based ranking, sequence learning, distributed machine learning, and more, and has ...
Etiquetas
natural language processing
text analytics
nlp
discrimination
big data
fairness
machine learning
disparate impact
Ver más
Personal Information
Organización/Lugar de trabajo
San Francisco Bay Area United States
Ocupación
CEO and Co-Founder at R7 Speech Sciences
Sector
Technology / Software / Internet
Sitio web
http://www.cs.jhu.edu/~delip
Acerca de
RECRUITERS: I AM NOT LOOKING FOR A JOB.
Delip Rao is the founder of R7 Speech Sciences, a San Francisco based company focusing on turning human conversations into structured datasets.
In the past, Delip also founded Joostware, an AI research consulting company and The Fake News Challenge, an initiative to bring AI researchers across the world to work on fact-checking related problems.
Delip is an accomplished researcher who specializes in scalable approaches for large-scale datasets dealing with real-world situations. He has worked on NLP and ML research problems involving semi-supervised learning, graph-based ranking, sequence learning, distributed machine learning, and more, and has ...
Etiquetas
natural language processing
text analytics
nlp
discrimination
big data
fairness
machine learning
disparate impact
Ver más