What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Emma Brunskill mainly focuses on Artificial intelligence, Reinforcement learning, Machine learning, Mathematical optimization and Markov decision process.
She has included themes like Domain and Computer vision in her Artificial intelligence study.
Her Reinforcement learning research is multidisciplinary, relying on both Sequence and Importance sampling.
In the field of Machine learning, her study on Leverage overlaps with subjects such as Ranging.
Her Mathematical optimization research is multidisciplinary, incorporating perspectives in Partially observable Markov decision process, Regret and Macro.
Her research in Markov decision process intersects with topics in Algorithm, Data-driven, Finite set and Learning analytics.
Her most cited work include:
- Global and regional hearing impairment prevalence: an analysis of 42 studies in 29 countries (330 citations)
- Building peer-to-peer systems with chord, a distributed lookup service (268 citations)
- Designing mobile interfaces for novice and low-literacy users (188 citations)
What are the main themes of her work throughout her whole career to date?
Emma Brunskill mostly deals with Reinforcement learning, Artificial intelligence, Machine learning, Markov decision process and Mathematical optimization.
Emma Brunskill focuses mostly in the field of Reinforcement learning, narrowing it down to matters related to Regret and, in some cases, Function and Structure.
Her Artificial intelligence research incorporates themes from Sequence and Set.
Her Machine learning study combines topics from a wide range of disciplines, such as Teaching method and Markov process.
Her Markov decision process study combines topics in areas such as Discrete mathematics, Upper and lower bounds and Econometrics.
Her work on Bellman equation as part of her general Mathematical optimization study is frequently connected to Probably approximately correct learning, thereby bridging the divide between different branches of science.
She most often published in these fields:
- Reinforcement learning (39.06%)
- Artificial intelligence (38.02%)
- Machine learning (26.04%)
What were the highlights of her more recent work (between 2019-2021)?
- Reinforcement learning (39.06%)
- Markov decision process (22.40%)
- Artificial intelligence (38.02%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Reinforcement learning, Markov decision process, Artificial intelligence, Machine learning and Mathematical optimization.
Emma Brunskill has researched Reinforcement learning in several fields, including Regret, Interface, Function approximation and Importance sampling.
Her Markov decision process research includes elements of Function, Upper and lower bounds, Discrete mathematics and Correctness.
She interconnects Observational study and Linear least squares in the investigation of issues within Artificial intelligence.
Her Machine learning study integrates concerns from other disciplines, such as Energy and Reduction.
Her research in Mathematical optimization focuses on subjects like Value, which are connected to Bellman equation and Sublinear function.
Between 2019 and 2021, her most popular works were:
- Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (32 citations)
- Learning Near Optimal Policies with Low Inherent Bellman Error. (28 citations)
- Scaling up behavioral science interventions in online education. (18 citations)
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