What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Operating system
His primary scientific interests are in Approximation algorithm, Distributed computing, Combinatorics, Computer network and Wireless network.
His research integrates issues of Time complexity, Wireless ad hoc network, Steiner tree problem and Spanning tree in his study of Approximation algorithm.
Madhav V. Marathe combines subjects such as Parallel algorithm, Scalability, Computational epidemiology and Heuristic with his study of Distributed computing.
His studies deal with areas such as Discrete mathematics and Computational complexity theory as well as Combinatorics.
His biological study spans a wide range of topics, including Mobile broadband and Mobile data offloading.
The Wireless network study combines topics in areas such as Distributed algorithm and Network packet.
His most cited work include:
- Modelling disease outbreaks in realistic urban social networks. (1612 citations)
- Simple heuristics for unit disk graphs (455 citations)
- Mobile Data Offloading through Opportunistic Communications and Social Participation (443 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Approximation algorithm, Combinatorics, Discrete mathematics, Mathematical optimization and Distributed computing.
His Approximation algorithm study is concerned with the larger field of Algorithm.
His work on Time complexity, Vertex, Degree and Tree as part of general Combinatorics research is frequently linked to Node, bridging the gap between disciplines.
His work in Discrete mathematics addresses issues such as Computational complexity theory, which are connected to fields such as Theoretical computer science.
His studies in Theoretical computer science integrate themes in fields like Graph and Artificial intelligence.
His study looks at the relationship between Distributed computing and topics such as Computer network, which overlap with Wireless network.
He most often published in these fields:
- Approximation algorithm (23.57%)
- Combinatorics (16.50%)
- Discrete mathematics (16.16%)
What were the highlights of his more recent work (between 2017-2021)?
- Theoretical computer science (11.11%)
- Data science (7.41%)
- Machine learning (5.39%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Theoretical computer science, Data science, Machine learning, Artificial intelligence and Work.
His Theoretical computer science study incorporates themes from Sequence, Graph dynamical system and Social system.
Madhav V. Marathe has researched Data science in several fields, including Bayesian calibration, Emerging infectious disease, Management science and Computational model.
The concepts of his Big data study are interwoven with issues in Ubiquitous computing, Voltage regulation and Computational epidemiology.
His Resilience study combines topics from a wide range of disciplines, such as Function and Distributed computing.
His study on Distributed computing is mostly dedicated to connecting different topics, such as Optimization problem.
Between 2017 and 2021, his most popular works were:
- Using data-driven agent-based models for forecasting emerging infectious diseases. (66 citations)
- Using data-driven agent-based models for forecasting emerging infectious diseases. (66 citations)
- DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Operating system
Machine learning, Artificial intelligence, Data-driven, Benchmark and Series are his primary areas of study.
His work on Deep learning and Artificial neural network is typically connected to Consistency and County level as part of general Machine learning study, connecting several disciplines of science.
In his work, Madhav V. Marathe performs multidisciplinary research in Artificial intelligence and Preparedness.
Madhav V. Marathe interconnects Bayesian calibration, Emerging infectious disease, Computational model and Data science in the investigation of issues within Data-driven.
His Benchmark study integrates concerns from other disciplines, such as Data modeling and High performance computation.
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.
