What is he best known for?
The fields of study he is best known for:
- Programming language
- Artificial intelligence
- Operating system
Anupam Datta mainly investigates Theoretical computer science, Cryptographic protocol, Privacy policy, Otway–Rees protocol and Universal composability.
His Theoretical computer science research is multidisciplinary, incorporating elements of Vulnerability, Probabilistic logic, Key exchange and Graph.
The study incorporates disciplines such as Health Insurance Portability and Accountability Act and Personally identifiable information in addition to Privacy policy.
His Personally identifiable information study integrates concerns from other disciplines, such as Information privacy, Internet privacy, Data mining and Audit.
His studies examine the connections between Information privacy and genetics, as well as such issues in Legislation, with regards to Access control and Data integrity.
His studies in Computer security integrate themes in fields like Technical report and Enforcement.
His most cited work include:
- TrustVisor: Efficient TCB Reduction and Attestation (472 citations)
- Privacy and contextual integrity: framework and applications (363 citations)
- Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems (307 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computer security, Theoretical computer science, Artificial intelligence, Audit and Personally identifiable information.
His Theoretical computer science research includes themes of Cryptographic protocol, Protocol, Cryptography, Universal composability and Mathematical proof.
His research in Artificial intelligence intersects with topics in Machine learning and Natural language processing.
His work deals with themes such as Stackelberg competition, Adversary and Confidentiality, which intersect with Audit.
His Personally identifiable information research is multidisciplinary, relying on both Enforcement and Privacy policy.
His Privacy policy research integrates issues from Correctness, Privacy by Design and Semantics.
He most often published in these fields:
- Computer security (24.23%)
- Theoretical computer science (21.13%)
- Artificial intelligence (15.98%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (15.98%)
- Set (5.15%)
- Machine learning (7.73%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Set, Machine learning, Attribution and Convolutional neural network.
Anupam Datta has researched Artificial intelligence in several fields, including Class, Pattern recognition and Natural language processing.
His Language model study in the realm of Natural language processing interacts with subjects such as Coreference, Subject and Agreement.
His Machine learning study combines topics from a wide range of disciplines, such as Line, Default, Interest rate and Key.
Within one scientific family, Anupam Datta focuses on topics pertaining to Interpretation under Convolutional neural network, and may sometimes address concerns connected to Proportionality.
As part of the same scientific family, Anupam Datta usually focuses on Action, concentrating on Artificial neural network and intersecting with Theoretical computer science.
Between 2017 and 2021, his most popular works were:
- Gender Bias in Neural Natural Language Processing (62 citations)
- Influence-Directed Explanations for Deep Convolutional Networks (24 citations)
- Gender Bias in Neural Natural Language Processing (22 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Artificial intelligence
- Operating system
Anupam Datta spends much of his time researching Artificial intelligence, Measure, Convolutional neural network, Set and Multidisciplinary approach.
His work on Gradient descent as part of his general Artificial intelligence study is frequently connected to Coreference, thereby bridging the divide between different branches of science.
Anupam Datta has included themes like Ground truth, Training set, Linear model and Feature in his Gradient descent study.
In his study, which falls under the umbrella issue of Measure, Interpretation, Machine learning, Peering, Deep neural networks and Interpretation is strongly linked to Class.
His Multidisciplinary approach research is multidisciplinary, incorporating perspectives in Online advertising, Advertising, Mulligan and Personalization.
Anupam Datta combines subjects such as Impossibility, Mathematical optimization, Component and Decomposition with his study of Counterfactual thinking.
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