What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Computer network
Her primary areas of investigation include Artificial intelligence, Machine learning, Scalability, Computer network and Wireless.
The various areas that she examines in her Artificial intelligence study include Data mining, Missing data, Imputation and Computer vision.
Her Machine learning research incorporates elements of Counterfactual thinking, Generative grammar, Risk assessment and Past history.
As a part of the same scientific family, Mihaela van der Schaar mostly works in the field of Scalability, focusing on Coding and, on occasion, Reference frame, Quality of service and Multimedia.
Adaptation and Real-time communication is closely connected to The Internet in her research, which is encompassed under the umbrella topic of Computer network.
Her study in the field of Wireless network and Physical layer also crosses realms of Mobile telephony.
Her most cited work include:
- Reputation-based incentive protocols in crowdsourcing applications (224 citations)
- Combined MPEG-4 FGS and modulation algorithm for wireless video transmission (186 citations)
- System and method for fine granular scalable video with selective quality enhancement (181 citations)
What are the main themes of her work throughout her whole career to date?
Mihaela van der Schaar mainly focuses on Artificial intelligence, Machine learning, Computer network, Distributed computing and Mathematical optimization.
She has researched Artificial intelligence in several fields, including Scalability, Data mining and Computer vision.
Mihaela van der Schaar studies Regret, a branch of Machine learning.
Her work in Computer network addresses subjects such as Wireless, which are connected to disciplines such as Communication channel.
Her work in Distributed computing is not limited to one particular discipline; it also encompasses Markov decision process.
A large part of her Mathematical optimization studies is devoted to Nash equilibrium.
She most often published in these fields:
- Artificial intelligence (38.13%)
- Machine learning (29.44%)
- Computer network (14.38%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (38.13%)
- Machine learning (29.44%)
- Counterfactual thinking (3.53%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Artificial intelligence, Machine learning, Counterfactual thinking, Inference and Observational study.
Much of her study explores Artificial intelligence relationship to Function.
Her studies deal with areas such as Variety and Bayesian probability as well as Machine learning.
Her Observational study research is multidisciplinary, incorporating perspectives in Domain, Causal inference and Confounding.
The study incorporates disciplines such as Gold standard, Randomized controlled trial and Experimental data in addition to Causal inference.
Her Feature course of study focuses on Transplantation and Matching.
Between 2019 and 2021, her most popular works were:
- Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study. (145 citations)
- Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data (23 citations)
- Deep Generative Models for 3D Linker Design. (19 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Computer network
Mihaela van der Schaar mostly deals with Machine learning, Artificial intelligence, Intensive care, Observational study and Ethnic group.
Her study in Machine learning is interdisciplinary in nature, drawing from both Variety, Clinical decision support system and Counterfactual thinking.
Mihaela van der Schaar combines subjects such as Structure, Sample, Key and Benchmark with her study of Counterfactual thinking.
Her research investigates the link between Artificial intelligence and topics such as Counterfactual conditional that cross with problems in Inference.
Her Intensive care research integrates issues from Capacity planning, Interface, Decision support system and Scale.
Her Observational study research incorporates themes from Causal inference and Confounding.
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