The new age of "Big Data" demands the best of both worlds, said Marina Vannucci, chair of Rice's Department of Statistics and principal investigator for the new programme.
"Data science is rapidly evolving as an essential interdisciplinary field where advances often result from combinations of ideas from various disciplines", she stated. "Statistics and computer science are major players in this field, and several examples of successful integration, like machine learning, already exist."
The three-year programme will serve as a point of contact for six graduate students, two postdoctoral researchers and several undergraduates as they pursue statistics and computer science projects in the Rice research groups to which they're assigned. Each undergraduate and graduate student will have mentors from both departments.
"The programme will offer an integrated research experience", Marina Vannucci stated. "There will be seminar courses, where students will learn and present material on key topics, and active participation in research projects, where topics will be put into practice. The idea is to link this program to the bigger umbrella of Rice's Data Science Initiative." The university recently announced a $150 million commitment for strategic research initiatives for Rice's second century, including the establishment of a world-class programme in data science.
The programme builds upon the strengths and interactions of a dynamic group of faculty mentors with expertise in probabilistic models and in methods for computational inference, Marina Vannucci said.
The grant will also fund the development of a course in data science. "It will be open to all students at Rice", she stated. "We will invite mentors to teach short modules or give presentations on their research for the first semester. In the second semester, the students will team up and work on projects."
"Statistics students are trained at envisioning and formulating rigorous statistical models", stated Luay Nakhleh, a professor of computer science and biosciences and co-principal investigator of the research training group. "These models often require efficient algorithms for inference and careful implementations, particularly with complex, large-scale data. Similarly, computer science is becoming increasingly statistical. That's where the two disciplines converge and benefit from each other."
Marina Vannucci and Luay Nakhleh said they hope computer science and statistics students will learn not only to formulate efficient algorithms and accurate models but also gain complementary expertise from each other's disciplines.
They expect students will address challenges in the medical, biological, genomic, energy, finance and systems biology realms. "All should learn and gain skills they currently don't have, but will need going forward", Marina Vannucci stated.