Research Areas

  Artificial Intelligence


Our artificial intelligence research addresses the central challenges of machine cognition, both from a theoretical perspective and from an empirical, implementation-oriented perspective.

  • Automatic Programming
    • Generic Programs, Partial Evaluation, Design Representation, Inference, Programming Interfaces.
  • Automated Reasoning and Interactive Theorem Proving
    • Simplification; Induction; Concept-formation; Lemma discovery; User interfaces; Hardware and software modeling and verification.
  • Autonomous Agents
    • Learning agents; Bidding agents; Robots; Game AI.
  • Computer Vision
    • Object and activity recognition; Content-based retrieval; Learning and vision; Robot vision; Biologically inspired vision.
  • Data Mining
    • Database search and mining; Large-scale data analysis; Social network analysis; Network estimation.
  • Knowledge Representation and Reasoning
    • Knowledge representation languages: Description logic, frames, graphical representations; Knowledge content areas: Temporal, Spatial, Causal knowledge; Ontology; Semantic matching; Constraint satisfaction; Expert systems; Semantic web; Cognitive modeling: memory models; Belief revision and truth maintenance.
  • Learning Theory
    • Computational and statistical analysis of learning algorithms; Online learning; Active learning; Probabilistic inference.
  • Logic-based AI
    • Commonsense knowledge; Reasoning about actions; Nonmonotonic reasoning; Answer set programming.
  • Machine Learning
    • Supervised learning; Reinforcement learning; Transfer learning; Active learning; Online learning; Statistical relational learning; Optimization; Graphical models; Nonparametric models; Probabilistic inference.
  • Multiagent Systems
    • Multiagent learning; Multirobot systems; Game theory.
  • Natural Computation
    • Neural networks; Evolutionary computation; Computational neuroscience; Cognitive science.
  • Natual Language Processing
    • Syntactic parsing; Semantic analysis; Information extraction; Machine translation; Machine reading; Connectionist models of lexical, sentence, and story processing.
  • Robotics
    • Robot learning; Developmental robotics; Multirobot systems; Multilegged walking; Embodied Cognition.
  • AI Applications
    • Autonomous driving; Robot soccer; Question answering; Math and Physics Problem Solving; Nonlinear control; Game playing; Fraud detection.

Bioinformatics and Computational Biology

Computer Architecture

Data Mining, Machine Learning, and Natural Computation

Formal Methods


Our research involves the use of mathematics to assist with the specification, design, implementation, and verification of computing systems.

  • Declarative Programming:
    • Declarative approaches to search. Nonmonotonic logic. Stable models. Semantics of answer set programming languages. Answer set solvers.
  • Concurrency:
    • Theory of concurrency and causality, Language for distributed and concurrent programming, Process algebra.
  • Hardware and Software Verification:
    • Microprocessor ISA specification. Microcode verification. Floating-point verification. Binary code analysis. Number theory and metamathematics.
  • Model Checking:
    • Property checking. Predicate abstraction. Automatic verification. Reachability. State-space exploration.
  • Security:
    • Model checking. Constraint solving. Computational soundness.
  • Theorem Proving:
    • Rewriting. Rule-driven simplification. Decision and semi-decision procedures. Heuristic proof techniques. Induction. Integrated proof techniques. Domain-specific proof techniques. Metafunctions. Search. Interactive guidance. Proof development environments.