Intelligibility of Machine Learning Algorithms
Anind's current research involves feedback/intelligibility and control in ubiquitous computing, context-aware computing, toolkits and end-user programming environments, and machine learning. Prior to CMU, Anind was Ubicomp Software Architect at Intel Research Berkeley and Adjunct Assistant Professor in EECS at UC Berkeley. He is also well known for his doctoral work on the Context Toolkit in the Future Computing Environments group at Georgia Tech.
Using the CrowdSignals Data
Anind will use CrowdSignals data to create more intelligent algorithms that explain everyday users how machine learning algorithms work internally and are used to infer useful information that helps them be more efficient in their everyday life.
"CrowdSignals is an amazing opportunity to advance research on countless topics, particularly for understanding people through ubiquitous and mobile computing! At a cost that's dramatically lower than in-house data collection campaigns, this data will be accessible to everyone including professionals, researchers and students."
- Anind Dey, M. Geschke Director
Human-Computer Interaction Institute
Carnegie Mellon University
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