What is she best known for?
The fields of study she is best known for:
- Computer network
- Artificial intelligence
- The Internet
Dina Katabi focuses on Computer network, Throughput, Wireless, Network packet and Wireless network.
The Computer network study combines topics in areas such as Real-time computing and Key.
Dina Katabi interconnects Testbed, Decoding methods, Routing protocol and Linear network coding in the investigation of issues within Throughput.
Her studies in Wireless integrate themes in fields like Transmitter, MIMO, Communication channel and Software-defined radio.
Her studies deal with areas such as Bandwidth allocation and Distributed computing as well as Network packet.
She has included themes like ExOR, Wireless Routing Protocol, Path vector protocol, Routing domain and Distance Vector Multicast Routing Protocol in her Wireless network study.
Her most cited work include:
- XORs in the air: practical wireless network coding (2108 citations)
- Embracing wireless interference: analog network coding (1331 citations)
- Trading structure for randomness in wireless opportunistic routing (1059 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Computer network, Wireless, Wireless network, Real-time computing and Artificial intelligence.
Her work carried out in the field of Computer network brings together such families of science as Distributed computing, MIMO and Throughput.
Her research on Wireless also deals with topics like
- Communication channel that connect with fields like Physical layer,
- Transmitter, which have a strong connection to Electronic engineering.
Her study brings together the fields of Multicast and Wireless network.
Her biological study spans a wide range of topics, including Machine learning, Computer vision and Radio frequency.
Her research integrates issues of Unicast and Wireless mesh network in her study of Linear network coding.
She most often published in these fields:
- Computer network (33.88%)
- Wireless (24.90%)
- Wireless network (15.51%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (13.88%)
- Computer vision (6.94%)
- Dementia (2.04%)
In recent papers she was focusing on the following fields of study:
Dina Katabi mainly investigates Artificial intelligence, Computer vision, Dementia, Physical medicine and rehabilitation and Artificial neural network.
Her Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Function, Radio frequency and Code.
Her work in the fields of Computer vision, such as RGB color model and Re identification, overlaps with other areas such as Term and Extended time.
Dina Katabi combines subjects such as Representation, Pulse oximetry and Bayesian network with her study of Artificial neural network.
Her Signal study combines topics in areas such as Computer network, Bottleneck, Pose and Antenna.
Her Human–computer interaction research incorporates themes from Wearable technology, Wireless and Signal processing.
Between 2018 and 2021, her most popular works were:
- ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation (34 citations)
- ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation (32 citations)
- Making the Invisible Visible: Action Recognition Through Walls and Occlusions (25 citations)
In her most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- The Internet
Her scientific interests lie mostly in Artificial intelligence, Computer vision, Robustness, Machine learning and MNIST database.
Her Visualization, Representation and Artificial neural network study in the realm of Artificial intelligence interacts with subjects such as Process and Action recognition.
Her Computer vision research includes elements of Radio frequency and Discriminator.
Her study in Robustness is interdisciplinary in nature, drawing from both Adversarial system, Pixel and Deep neural networks.
Her Feature extraction research integrates issues from RGB color model, Human body and Re identification.
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