Learning for Intelligent Systems

  • Robust Real-world Object Recognition 

    Humans are able to recognize and learn new objects (e.g. faces, cars, flowers) almost effortlessly. How can a computer be able to achieve this functionality? It has been a very challenging goal for artificial intelligence research since its inception. The problem is extremely complex in its generality and has an enormously large search space. A fundamental understanding to develop automated recognition requires topics in engineering and computer sciences, mathematical and statistical sciences, neural and cognitive science. Automated recognition of objects in images will lead to an enormous number of practical applications for the benefit of society.

  • Multimedia, Interactive, Distributed Dynamic Databases

    An enormous amount of data is collected every day from a variety of sensors. How can we catalog and search these dynamic and distributed databases automatically so as to extract “meaningful information?” The applications are in research, education, entertainment, advertisement, surveillance, monitoring, etc. It requires the tools and fundamental techniques for representation of data/events, data compression, algorithms for data indexing, human-computer interfaces, adaptation to the competence and needs of the user, integration of information, evidence accumulation, inexact queries, spatio-temporal reasoning, reasoning in the time-history of the data, distributed processing and control, statistical validation of algorithms, etc.

  • Perception-Based Intelligent Robots

    Sensing, perception and autonomous decision making are key ingredients for intelligent automation where the machine is faced with unexpected challenges and makes intelligent decisions. Examples include perception-based real-world navigation, space exploration, robot assistants for frail and handicapped people, service robots to automate hazardous tasks, etc. The critical research areas are the core competencies of the center faculty: engineering, computer science, mathematics, statistics, psychology, economics and business.