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His research interests include multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of difficult data (e.g. maps, video, text), computational learning theory, and game theory. Gordon received a B.A. in computer science from Cornell University in 1991, and a PhD at Carnegie Mellon in 1999. [9]
Himabindu "Hima" Lakkaraju is an Indian-American computer scientist who works on machine learning, artificial intelligence, algorithmic bias, and AI accountability.She is currently an Assistant Professor at the Harvard Business School and is also affiliated with the Department of Computer Science at Harvard University.
She undertakes research on post-graduate research within higher education, and is an influential voice on social media on this topic. [8] In 2010, Mewburn started The Thesis Whisperer blog. Her work on this blog, grounded in her academic research, has earned her global recognition as an expert on topics in doctoral education and academic cultures.
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Margaret Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning.She is most well known for her work on automatically removing undesired biases concerning demographic groups from machine learning models, [2] as well as more transparent reporting of their intended use.
Kaiming He (Chinese: 何恺明; pinyin: Hé Kǎimíng) is a Chinese computer scientist who primarily researches computer vision and deep learning. [2] He is an associate professor at Massachusetts Institute of Technology and is known as one of the creators of residual neural network (ResNet).
During his PhD, Tatonetti developed a classifier to detect side effects of drugs based on data available by FAERS. [9] His dissertation, titled Data-driven detection, prediction, and validation of drug-drug interactions , focused on the development of novel statistical and computational methods for observational data mining. [ 10 ]