Research Areas
Data.ai
Foundational and applied work in data management and analytics: scalable databases and pipeline automation, data integration and cleaning, big-data processing, and tools for exploratory and statistical analysis.
Decisions.ai
Advances in algorithmic decision-making under uncertainty, combining normative models (e.g., utility theory, stochastic optimization) with data-driven approaches (reinforcement learning, bandits) to build intelligent decision-support systems in single- and multi-agent settings.
Foundations.ai
Core theoretical underpinnings of AI, spanning probabilistic inference and learning theory, algorithmic complexity and guarantees, causal reasoning, and formal methods—all aimed at rigorously understanding when and why AI techniques work.
Language.ai
Natural language processing and understanding—from semantic representation and dialogue systems to large-scale language modeling, multilingual text generation, and information extraction—grounded in both linguistic theory and deep learning.
Responsible.ai
Designing AI that’s fair, transparent, accountable, and aligned with human values: work on bias mitigation, interpretable models, privacy-preserving methods, and policy frameworks to ensure ethical deployment of AI in society.
Search.ai
Innovations in information retrieval and search: semantic and personalized ranking, query understanding, real-time indexing, and neural IR architectures to help users find the right information at web-scale.
Speech.ai
End-to-end speech and audio processing: automatic speech recognition, text-to-speech synthesis, speaker and language identification, and low-resource multilingual speech technologies.
Structures.ai
Modeling and learning over structured data—graph and relational learning, knowledge-graph construction and completion, hierarchical and compositional representations—bridging symbolic and statistical AI.
Vigil.ai
Computer vision research on image and video analysis, 3D reconstruction, visual reasoning, and sensor fusion—applicable to domains from autonomous systems to medical and scientific imaging.
Systems.ai
Building high-performance, scalable AI infrastructure: distributed and federated learning, hardware–software co-design, runtime optimization, and system reliability to support next-generation AI workloads.
