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High Performance & Cloud Computing

Over the past 10 years, companies have become more open to sending and hosting enterprise data on third-party networks and infrastructures. This transition started with such enterprise applications as CRM systems, considered a low security risk, to now running all business operations in the cloud with no local/in-house IT assets, including BI.

As businesses look to adopt predictive and prescriptive analytics strategies in and around the Cloud and Big Data, the associated math-based simulations and scenario optimization studies are increasing the workload demand on computing infrastructure. These computations need to take place where the data resides, requiring a high-performance cloud computing strategy as part of an organizations overall business analytics maturity model.


Embedded System,Wireless Sensor Networks & Internet of Things

Wireless technology has enormous potential to change the way people and things communicate. Future wireless networks will allow people on the move to communicate with anyone, anywhere, and at any time using a range of high-performance multimedia services. Wireless video will support applications such as enhanced social networking, distance learning and remote medicine. Wireless sensor networks can also enable a new class of intelligent home electronics, smart and energy-efficient buildings and highways, and in-body networks for analysis and treatment of medical conditions.

Fortunately for us researchers, there are many technical challenges that must be met in order to make this vision a reality. These challenges transcend all levels of the overall system design, including hardware, communication link design, wireless networking, distributed sensing, communication, and control, and cross-layer design. In addition, synergies between the hardware, link, and network designs must be exploited in order to meet the demanding performance requirements of these future systems.

Data Analytics & machine Learning

The rapid growth of the Internet, the widespread deployment of sensors and scientific advances such as the mapping of the human genome have resulted in a tsunami of data. In many fields the challenge has shifted from collecting a sufficient amount of data to understanding and gaining insight from the massive amount of data that are available.Research in data analytics, machine learning, and visualization is concerned with developing computational methods to extract knowledge from large, complex, interrelated data sets. Current research spans machine learning, visualization, and analysis of massive data sets

Searching is a language game. Find just the right combination of words and you have the key to the black box of answers that we call a database. Guess wrong, and the box remains mum, or worse, it spews back nonsense. So, we craft our queries with care. Logical and proximity operators are chosen judiciously, words truncated only so far and no further. Search fields are selected for precision without omission. But suppose that we could build some of that knowledge into the system. Suppose that we could give it some of our understanding and also loosen it up so that it was more forgiving. Suppose that we could get it to give us clues about what to do next. That's the hope and the promise of Natural Language Processing (NLP) in information retrieval.