What is she best known for?
The fields of study she is best known for:
- Computer network
- The Internet
- Statistics
The scientist’s investigation covers issues in Data mining, Anomaly detection, Artificial intelligence, Backbone network and Inference.
Nina Taft combines subjects such as Kalman filter, Network planning and design and Internet traffic with her study of Data mining.
Her Anomaly detection research incorporates themes from Flow, Detector, Flow network, Residual and Principal component analysis.
As a part of the same scientific study, Nina Taft usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Differential privacy.
Her Backbone network research is multidisciplinary, relying on both Internet backbone, Autoregressive integrated moving average and Econometrics.
The various areas that Nina Taft examines in her Inference study include Recommender system and World Wide Web.
Her most cited work include:
- Traffic matrix estimation: existing techniques and new directions (485 citations)
- Structural analysis of network traffic flows (432 citations)
- Combining filtering and statistical methods for anomaly detection (266 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Computer network, Data mining, Anomaly detection, Distributed computing and The Internet.
Host and Service provider is closely connected to Computer security in her research, which is encompassed under the umbrella topic of Computer network.
Her Data mining study integrates concerns from other disciplines, such as Anomaly, Detector, Internet traffic and Artificial intelligence.
Her Anomaly detection research is multidisciplinary, incorporating elements of Real-time computing, Principal component analysis and Residual.
Her work is dedicated to discovering how Distributed computing, Routing are connected with Covariance, Internet protocol suite and Telecommunications network and other disciplines.
As part of one scientific family, Nina Taft deals mainly with the area of Traffic engineering, narrowing it down to issues related to the Simulation, and often Algorithm.
She most often published in these fields:
- Computer network (24.77%)
- Data mining (19.27%)
- Anomaly detection (14.68%)
What were the highlights of her more recent work (between 2012-2016)?
- Data mining (19.27%)
- Recommender system (7.34%)
- Inference (7.34%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Data mining, Recommender system, Inference, Matrix decomposition and Encryption.
Her Data mining research is multidisciplinary, incorporating perspectives in Machine learning, Survey data collection, Artificial intelligence and Joint probability distribution.
Nina Taft undertakes multidisciplinary studies into Inference and Context in her work.
Her Encryption research includes themes of Algorithm, Computation and Privacy preserving.
Her work in Homomorphic encryption addresses issues such as Ridge, which are connected to fields such as Information privacy, Cryptography, Regression and Big data.
Her Oblivious transfer study is associated with Computer network.
Between 2012 and 2016, her most popular works were:
- Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (263 citations)
- Privacy-preserving matrix factorization (188 citations)
- Recommending with an agenda: active learning of private attributes using matrix factorization (32 citations)
In her most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Statistics
Her scientific interests lie mostly in Recommender system, Matrix decomposition, Inference, Leverage and Machine learning.
Her Recommender system research includes elements of Privacy preserving, Database and Profiling.
Her biological study spans a wide range of topics, including Theoretical computer science, Bayesian probability and Internet privacy.
Her studies in Machine learning integrate themes in fields like Construct, The Internet and Artificial intelligence.
Her research brings together the fields of Data mining and Construct.
Nina Taft carries out multidisciplinary research, doing studies in Artificial intelligence and Context.
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