D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 48 Citations 9,424 433 World Ranking 3103 National Ranking 185

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Combined MPEG-4 FGS and modulation algorithm for wireless video transmission

Joseph Meehan;Der Schaar Mihaela Van.
(2001)

344 Citations

Reputation-based incentive protocols in crowdsourcing applications

Yu Zhang;Mihaela van der Schaar.
international conference on computer communications (2012)

290 Citations

System and method for fine granular scalable video with selective quality enhancement

Chen Yingwei;Radha Hayder;Van Der Schaar Mihaela.
(1999)

246 Citations

In-band motion compensated temporal filtering

Yiannis Andreopoulos;Adrian Munteanu;Joeri Barbarien;Mihaela Van der Schaar.
Signal Processing-image Communication (2004)

242 Citations

Multimedia Over IP and Wireless Networks: Compression, Networking, and Systems

Mihaela van der Schaar;Philip A. Chou.
(2012)

200 Citations

Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study.

Pedro Baqui;Ioana Bica;Ioana Bica;Valerio Marra;Ari Ercole.
The Lancet Global Health (2020)

188 Citations

Context-Aware Proactive Content Caching With Service Differentiation in Wireless Networks

Sabrina Muller;Onur Atan;Mihaela van der Schaar;Anja Klein.
IEEE Transactions on Wireless Communications (2017)

176 Citations

Interframe wavelet coding—motion picture representation for universal scalability

Jens-Rainer Ohm;Mihaela van der Schaar;John W. Woods.
Signal Processing-image Communication (2004)

157 Citations

Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning

Byung-Gook Kim;Yu Zhang;Mihaela van der Schaar;Jang-Won Lee.
IEEE Transactions on Smart Grid (2016)

137 Citations

INTERNATIONAL ORGANISATION FOR STANDARDISATION ORGANISATION INTERNATIONALE DE NORMALISATION ISO/IEC JTC1/SC29/WG11 CODING OF MOVING PICTURES AND AUDIO

Yiannis Andreopoulos;Adrian Munteanu;Mihaela van der Schaar;Jan Cornelis.
(2004)

124 Citations

Best Scientists Citing Mihaela van der Schaar

Mihaela van der Schaar

Mihaela van der Schaar

University of California, Los Angeles

Publications: 83

Yiannis Andreopoulos

Yiannis Andreopoulos

University College London

Publications: 29

Dusit Niyato

Dusit Niyato

Nanyang Technological University

Publications: 25

Zhu Han

Zhu Han

University of Houston

Publications: 25

Marta Karczewicz

Marta Karczewicz

Qualcomm (United Kingdom)

Publications: 24

Feng Wu

Feng Wu

University of Science and Technology of China

Publications: 23

Deniz Gunduz

Deniz Gunduz

Imperial College London

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Ralph F. Osterhout

Ralph F. Osterhout

Microsoft (United States)

Publications: 21

Shipeng Li

Shipeng Li

Chinese University of Hong Kong, Shenzhen

Publications: 17

Yanmin Zhu

Yanmin Zhu

Shanghai Jiao Tong University

Publications: 16

Xu Jizheng

Xu Jizheng

ByteDance

Publications: 15

David Taubman

David Taubman

UNSW Sydney

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Beatrice Pesquet-Popescu

Beatrice Pesquet-Popescu

University of Paris-Saclay

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Ebroul Izquierdo

Ebroul Izquierdo

Queen Mary University of London

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John D. Haddick

John D. Haddick

Microsoft (United States)

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Miska Hannuksela

Miska Hannuksela

Nokia (Finland)

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Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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