PhD in Computer Science Potential Research Areas
The Seidenberg School’s PhD in Computer Science covers a wealth of research areas. We pride ourselves on engaging with every opportunity the computer science field presents. Check out some of our specialties below for examples of just some of the topics we cover at Seidenberg. If you have a particular field of study you are interested in that is not listed below, just get in touch with us and we can discuss opportunities and prospects.
Some of the research areas you can explore at Seidenberg include:
Telehealth uses current computing technologies to deliver medical advice and treatment to patients at a distance. The purpose of telehealth is to offer better availability of medical professionals to patients at a reduced cost. But there’s more – telehealth also means that patients in California can access doctors in New York, or further afield, and that records can be shared securely through cloud-based technologies to ensure the best service.
The faculty at Seidenberg has nationally unique strength in XML technologies for electronic healthcare record management and processing as well as cloud-based technologies for global system integration and service delivery. We excel at delivering business solutions based on current technologies with the maximal value/cost ratio.
The Seidenberg School has an excellent track record when it comes to cybersecurity research. We lead the nation in web security, developing secure web applications, and research into cloud security and trust. Since 2004, Seidenberg has been designated a Center of Academic Excellence in Information Assurance Education three times by the National Security Agency and the Department of Homeland Security. We also secured more than 2 million dollars in federal and private funding for cybersecurity research during the past few years.
Educational Approaches using Emerging Computing Technologies
The internet is fast becoming a place of education, and Pace University is keeping up with the trend by offering online bachelor’s degrees through NACTEL and iPace. The traditional classroom setting doesn’t suit everyone, which is why many teachers and students are choosing the Web to teach, study, and learn.
The Seidenberg School’s research into new educational approaches include innovative spiral education models, Portable Seidenberg labs based on cloud computing and computing virtualization with which students can work in personal enterprise IT environment anytime anywhere, and creating new semantic tools for personalized cyber-learning.
Pattern Recognition and Machine Learning
Just as humans take actions based on their sensory input, pattern recognition and machine learning systems operate on raw data and take actions based on the categories of the patterns. These systems can be developed from labeled training data (supervised learning) or from unlabeled training data (unsupervised learning). Pattern recognition and machine learning technology is used in diverse application areas such as optical character recognition, speech recognition, and biometrics. The Seidenberg faculty has recognized strengths in many areas of pattern recognition and machine learning, particularly handwriting recognition and pen computing, speech and medical applications, and applications that combine human and machine capabilities.
A popular application of pattern recognition and machine learning in recent years has been in the area of biometrics. Biometrics is the science and technology of measuring and statistically analyzing human physiological and behavioral characteristics. The physiological characteristics include face recognition, DNA, fingerprint, and iris recognition, while the behavioral characteristics include typing dynamics, gait, and voice. The Seidenberg faculty has nationally recognized strength in biometrics, particularly behavioral biometrics dealing with humans interacting with computers and smartphones.
Big Data Analytics
The term “big data” is used for data so large and complex that it becomes difficult to process using traditional structured data processing technology. Big data analytics is the relatively new science that enables organizations to analyze a mixture of structured, semi-structured and unstructured data in search of valuable information and insights. The data come from many areas, including meteorology, genomics, environmental research, and the Internet. This science uses many machine learning algorithms and the challenges include data capture, search, storage, analysis, and visualization. The Seidenberg faculty is currently building expertise in this area of research.
Business Process Modeling
Business Process Modeling is the emerging technology for automating the execution and integration of business processes. The BPMN based business process modeling enables precise modeling and optimization of business processes, and BPEL based automatic business execution enables effective computing service and business integration and effective auditing. Seidenberg was among the first in the nation to introduce BPM into curricula and research.