Research

Building Scalable Data Intelligence

We develop scalable and adaptive machine learning methods that uncover structure in complex, high-dimensional, and dynamic data. Our research spans tensors, graphs, and streams, with applications in real-world intelligent systems, scientific data, and trustworthy AI.

Scalable Data Mining & Representation Learning

We develop scalable (AI) methodologies to extract meaningful structures from high-dimensional, large-scale data. Our work focuses on principled representation learning to capture the complex relational dependencies and latent patterns embedded within massive datasets.

Research Areas

Tensor and graph representation learning
Efficient tensor decomposition
High-Performance Search & Retrieval

Streaming & Adaptive Computation

We develop computational frameworks for dynamic and evolving data environments. Our research focuses on enabling (AI) models and representations to adapt and update efficiently in real-time. as data streams in.

Research Areas

Streaming and incremental algorithms
Online and continual learning
Temporal modeling & Range-based Analytics

Intelligent Systems for Real-World Domains

We design AI systems grounded in scalable modeling and adaptive computation to solve complex challenges in real-world domains. Our research bridges algorithmic innovation and practical impact across complex real-world domains.

Research Areas

Intelligent Recommender Systems
Anomaly Detection & Monitoring Systems
Forecasting & Predictive Intelligence

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