What is she best known for?
The fields of study she is best known for:
- Programming language
- Algorithm
- Operating system
Her primary areas of study are Theoretical computer science, Scheme, Secure two-party computation, Homomorphic encryption and Computation.
The Theoretical computer science study combines topics in areas such as Cryptography, Class, Verifiable computing, Encryption and Ciphertext indistinguishability.
She has researched Scheme in several fields, including Algorithm, mmap and Cryptanalysis.
Her biological study spans a wide range of topics, including Tree, Range query, Binary search algorithm and Security parameter.
Mariana Raykova mostly deals with Oblivious ram in her studies of Computation.
Her research in Computer security focuses on subjects like Private set intersection, which are connected to Cryptographic protocol.
Her most cited work include:
- Candidate Indistinguishability Obfuscation and Functional Encryption for all Circuits (841 citations)
- Advances and Open Problems in Federated Learning (571 citations)
- Pinocchio: Nearly Practical Verifiable Computation (448 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Theoretical computer science, Encryption, Computer security, Cryptography and Computation.
Mariana Raykova combines subjects such as Scheme, Homomorphic encryption, Secure multi-party computation, Obfuscation and Functional encryption with her study of Theoretical computer science.
She has included themes like Discrete mathematics, Multiple encryption, Compiler and Ciphertext indistinguishability in her Functional encryption study.
Her Encryption research incorporates themes from Overhead, Distributed computing and Bloom filter.
Her Cryptography research is multidisciplinary, incorporating perspectives in Verifiable computing and Key.
The Computation study which covers Delegate that intersects with Attribute-based encryption and Verifiable secret sharing.
She most often published in these fields:
- Theoretical computer science (34.26%)
- Encryption (28.70%)
- Computer security (19.44%)
What were the highlights of her more recent work (between 2017-2021)?
- Protocol (12.96%)
- Computer network (11.11%)
- Computer security (19.44%)
In recent papers she was focusing on the following fields of study:
Mariana Raykova mainly investigates Protocol, Computer network, Computer security, Cryptography and Secure multi-party computation.
Her studies in Protocol integrate themes in fields like Shuffling and Correctness.
As a part of the same scientific family, Mariana Raykova mostly works in the field of Computer network, focusing on Blockchain and, on occasion, Database transaction and Payment.
Her Computer security study frequently links to adjacent areas such as Metadata.
Many of her studies involve connections with topics such as Homomorphic encryption and Cryptography.
The various areas that she examines in her Secure multi-party computation study include Oblivious transfer, Secret sharing, Artificial intelligence, Bloom filter and Machine learning.
Between 2017 and 2021, her most popular works were:
- Advances and Open Problems in Federated Learning (571 citations)
- RapidChain: Scaling Blockchain via Full Sharding (226 citations)
- Advances and Open Problems in Federated Learning (28 citations)
In her most recent research, the most cited papers focused on:
- Programming language
- Algorithm
- Operating system
Her scientific interests lie mostly in Computer network, Protocol, Blockchain, Computation and Mobile device.
Her study in Computer network is interdisciplinary in nature, drawing from both Secure computing, Graph and Correctness.
Her Protocol study incorporates themes from Degree, Differential privacy and Cryptography.
Her work deals with themes such as Scalability, Payment and Database transaction, which intersect with Blockchain.
In the field of Computation, her study on Secure multi-party computation overlaps with subjects such as Trade offs.
Her Mobile device study combines topics in areas such as Orchestration, Training set and Federated learning.
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