2023 - Research.com Computer Science in Australia Leader Award
2004 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to the formulation and extraction of semantics in multimedia data.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Computer vision, Hidden Markov model and Data mining. Artificial intelligence and Pattern recognition are frequently intertwined in his study. His Machine learning research is multidisciplinary, incorporating elements of Inference and Set.
His study on Motion detection, Cognitive neuroscience of visual object recognition and Film grammar is often connected to Obstacle as part of broader study in Computer vision. His Hidden Markov model research also works with subjects such as
Svetha Venkatesh focuses on Artificial intelligence, Machine learning, Computer vision, Data mining and Pattern recognition. His study ties his expertise on Natural language processing together with the subject of Artificial intelligence. The concepts of his Machine learning study are interwoven with issues in Structure and Probabilistic logic.
His study connects Cluster analysis and Data mining. His study on Pattern recognition is mostly dedicated to connecting different topics, such as Facial recognition system. His Hidden Markov model study frequently draws connections to adjacent fields such as Activity recognition.
His primary areas of investigation include Artificial intelligence, Machine learning, Bayesian optimization, Mathematical optimization and Bayesian probability. He combines subjects such as Graph and Pattern recognition with his study of Artificial intelligence. His studies deal with areas such as Set and Reinforcement learning as well as Graph.
His Machine learning research focuses on subjects like Sample, which are linked to Data point. Svetha Venkatesh has included themes like Linear subspace, Global optimization, Benchmark, Rate of convergence and Optimization problem in his Bayesian optimization study. His work in Mathematical optimization tackles topics such as Regret which are related to areas like Space and Sampling.
His scientific interests lie mostly in Artificial intelligence, Bayesian optimization, Machine learning, Mathematical optimization and Theoretical computer science. His work carried out in the field of Artificial intelligence brings together such families of science as Natural language processing and Pattern recognition. The various areas that Svetha Venkatesh examines in his Bayesian optimization study include Hyperparameter, Global optimization and Benchmark.
His Machine learning study combines topics in areas such as Abstraction and Drug repositioning. His Mathematical optimization study integrates concerns from other disciplines, such as Probability distribution, Regret and Bayesian probability. His Theoretical computer science study also includes
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.
Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016
Theo Vos;Amanuel Alemu Abajobir;Kalkidan Hassen Abate;Cristiana Abbafati.
Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016
Simon I Hay;Amanuel Alemu Abajobir;Kalkidan Hassen Abate;Cristiana Abbafati.
Video abstraction: A systematic review and classification
Ba Tu Truong;Svetha Venkatesh.
ACM Transactions on Multimedia Computing, Communications, and Applications (2007)
Activity recognition and abnormality detection with the switching hidden semi-Markov model
T.V. Duong;H.H. Bui;D.Q. Phung;S. Venkatesh.
computer vision and pattern recognition (2005)
Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study
Lalit Dandona;Rakhi Dandona;G Anil Kumar;D K Shukla.
The Lancet (2017)
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
N.T. Nguyen;D.Q. Phung;S. Venkatesh;H. Bui.
computer vision and pattern recognition (2005)
The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: the Global Burden of Disease Study 2017
Kalpana Balakrishnan;Sagnik Dey;Tarun Gupta;R S Dhaliwal.
The Lancet Planetary Health (2019)
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.
Wei Luo;Dinh Phung;Truyen Tran;Sunil Gupta.
Journal of Medical Internet Research (2016)
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection
Dong Gong;Lingqiao Liu;Vuong Le;Budhaditya Saha.
international conference on computer vision (2019)
Policy recognition in the abstract hidden Markov model
Hung H. Bui;Svetha Venkatesh;Geoff West.
Journal of Artificial Intelligence Research (2002)
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