2016 - Fellow of the American Association for the Advancement of Science (AAAS)
2014 - SIAM Fellow For contributions to numerical linear algebra, data analysis, and machine learning.
2014 - ACM Fellow For contributions to large-scale data analysis, machine learning and computational mathematics.
His primary areas of study are Cluster analysis, Artificial intelligence, Mathematical optimization, Algorithm and Data mining. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition. His work carried out in the field of Mathematical optimization brings together such families of science as Rate of convergence, Estimator, Sparse approximation and Mahalanobis distance.
The study incorporates disciplines such as Matrix, Matrix completion, Mutual information, Conditional mutual information and Eigenvalues and eigenvectors in addition to Algorithm. Inderjit S. Dhillon works mostly in the field of Data mining, limiting it down to concerns involving Biclustering and, occasionally, Residue. His biological study spans a wide range of topics, including Theoretical computer science and Fuzzy clustering.
His primary areas of investigation include Algorithm, Artificial intelligence, Cluster analysis, Mathematical optimization and Machine learning. His Algorithm research is multidisciplinary, incorporating elements of Dimension, Matrix and Speedup. Inderjit S. Dhillon interconnects Eigenvalues and eigenvectors, Combinatorics and Rank in the investigation of issues within Matrix.
Inderjit S. Dhillon combines topics linked to Pattern recognition with his work on Artificial intelligence. The Cluster analysis study combines topics in areas such as Theoretical computer science and Data mining. His Mathematical optimization study integrates concerns from other disciplines, such as Estimator, Least squares, Applied mathematics and Convex optimization.
Algorithm, Artificial intelligence, Machine learning, Matrix and Mathematical optimization are his primary areas of study. His Algorithm research includes elements of Dimension, Inference, Support vector machine, Cluster analysis and Speedup. His Cluster analysis study combines topics from a wide range of disciplines, such as Disjoint sets, Similarity, Feature learning and Outlier.
His Artificial intelligence research incorporates themes from Submodular set function, Set and Pattern recognition. His research in Matrix intersects with topics in Singular value decomposition, Logarithm and Rank. His work on Coordinate descent as part of his general Mathematical optimization study is frequently connected to Multiplier, thereby bridging the divide between different branches of science.
Inderjit S. Dhillon spends much of his time researching Artificial intelligence, Machine learning, Algorithm, Artificial neural network and Matrix. His Artificial intelligence study incorporates themes from Time series and Pattern recognition. His work deals with themes such as Noise, Mathematical optimization and Speedup, which intersect with Algorithm.
His studies in Matrix integrate themes in fields like Computational complexity theory, Logarithm and Feature vector. He performs multidisciplinary study in the fields of Quality and Cluster analysis via his papers. His Cluster analysis research integrates issues from Key and Nonlinear system.
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.
Information-theoretic metric learning
Jason V. Davis;Brian Kulis;Prateek Jain;Suvrit Sra.
international conference on machine learning (2007)
ScaLAPACK Users' Guide
L. S. Blackford;J. Choi;A. Cleary;E. D'Azevedo.
ScaLAPACK user's guide
L. S. Blackford;J. Choi;A. Cleary;E. D'Azeuedo.
Co-clustering documents and words using bipartite spectral graph partitioning
Inderjit S. Dhillon.
knowledge discovery and data mining (2001)
Clustering with Bregman Divergences
Arindam Banerjee;Srujana Merugu;Inderjit S. Dhillon;Joydeep Ghosh.
siam international conference on data mining (2005)
Concept Decompositions for Large Sparse Text Data Using Clustering
Inderjit S. Dhillon;Dharmendra S. Modha.
Machine Learning (2001)
Inderjit S. Dhillon;Subramanyam Mallela;Dharmendra S. Modha.
knowledge discovery and data mining (2003)
Kernel k-means: spectral clustering and normalized cuts
Inderjit S. Dhillon;Yuqiang Guan;Brian Kulis.
knowledge discovery and data mining (2004)
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
I.S. Dhillon;Yuqiang Guan;B. Kulis.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
Arindam Banerjee;Inderjit Dhillon;Joydeep Ghosh;Srujana Merugu.
Journal of Machine Learning Research (2007)
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