We develop generic approaches and specialized techniques that cover a wide range of descriptive, predictive and prescriptive analytics and work effectively with text, image, transactional, graph and time-series data in a responsible manner. E.g. we use Deep Learning methods to develop models for high dimensional heterogeneous, unstructured and evolving data and apply this models to areas such as medical imaging, genomics, anomaly detection and sentiment analysis. We further work on methods for analyzing and explaining the model’s decisions and performance and facilitate effective DM with domain expert in the loop.
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Recent Publications
Our most recent peer reviewed publications
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Small time asymptotics of the entropy of the heat kernel on a Riemannian manifold
Applied and Computational Harmonic Analysis (2024) -
Efficient detection of multivariate correlations with different correlation measures
VLDB Journal (2024) -
RT-GCN
Information Fusion (2024) -
Neural Langevin Dynamics: Towards Interpretable Neural Stochastic Differential Equations.
(2024) -
Can Fairness be Automated?
Journal of Artificial Intelligence Research (2024)