Robust interdisciplinary collaboration to harness data for the greater good
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“Data science – the grand interdisciplinary challenge to extract new knowledge from big data through advanced analytics – presents a transformational opportunity for Purdue.”
Jay Akridge, Provost and Executive Vice President for Academic Affairs and Diversity
“The need is staggering. Imparting data literacy to the workforce is one of the key goals of this initiative.”
Suresh Garimella, Executive Vice President for Research and Partnerships
The ubiquity of sensing devices and the low cost of data storage have caused a data explosion throughout society. Twenty billion devices are now connected to the Internet; by 2030, that number will rise to 1 trillion.
Data scientists are the critical link that transforms data into information. The field is growing so rapidly that there will be one million more computer science jobs than graduates by 2020 – equaling a $500 billion gap throughout the workforce.
Data science affects every citizen and is needed in every sector. It enables manufacturers to conduct preventative maintenance, reducing expensive production stoppages for repairs. It makes connected care possible, enabling community hospitals to better serve rural diabetic patients.
All 16 of our nation’s security agencies use data science to integrate massive, disparate information.
Even more broadly, the U.N. predicts global food demand is set to double by 2050, and data science will be an integral component of identifying solutions.
The Integrative Data Science Initiative will push the frontiers of scientific discovery well beyond current confines. We will be a national leader in applying Data Science to solve large and pressing problems ranging from food insecurity to disease. The “Embedding Data Science into Domain Curricula” program will also serve as a new model for other universities seeking to implement a comprehensive educational Data Science initiative. Data Science thrives when collaboration is fully engaged and, with this proposed Initiative, we are fully engaging the power of Purdue.
The initiative focuses on advancing the frontiers of research and the application of data science to pressing, socially-relevant issues, as well as a new campus-wide, transformational data science education initiative.
The Integrative Data Science Initiative (IDSI) is designed to build on and advance Purdue’s existing strengths to position the university as a leader at the forefront of advancing data science-enabled research and education by tightly coupling theory, discovery and applications while providing students with an integrated, data science-fluent campus ecosystem. Research on student success ties into the educational mission of the University.
Research opportunities in data science focus both on building underlying fundamental understanding and on immediate use for society. These opportunities underscore the notion that advances in data science are transforming the landscape of research in fundamental ways.
“Much of the work on data science focusses on exciting new technology and other STEM advances, but many of the technological advances will go nowhere, or will fail to reach their full potential, because of the role of policy, law, social norms, consumer behavior and other human factors. Algorithmic ethics are a prime example of this question, with consumers and activists raising the alarm about biases inadvertently reproduced or even strengthened through machine learning. Realizing the full potential of data science requires bringing in these ethical and social issues from the start. In addition, adapting machine learning and other big data analytic techniques for social science will open up exciting new areas of study and will give us fresh insight into human behavior. It will enable us to test new questions and to revisit old, enduring questions with new data. More generally, ethical, legal and social issues (sometimes called ELSI) are currently at the very center of conversations about how to move technological development forward, from drones to automated vehicles, so when Purdue leads on these issues, it will strengthen Purdue’s focus on excellence in the STEM fields.”
S. Laurel Weldon
Director of the Purdue Policy Research Institute, Distinguished Professor of Political Science
“Data Science research on fundamental aspects of models, methods, and analyses at the Center for Science of Information (CSoI) focuses on core concepts of information, knowledge, learning, fairness, trust, risk, collusion, privacy, and information-efficient computation. Core research topics include development of suitable models for data, generation and testing of hypothesis; characterization of bounds (limits) on important parameters such as learning, transferability and generalizability, control, computation complexity, and statistical significance; novel methods for construction of learning models,reasoning and explainability, privacy, and fairness; and new methodologies for validation and verification, taking into account tradeoffs between computation, accuracy, and time; and novel applications in adversarial/ game theoretic settings. The Center also addresses issues at the interface of data science, systems, security, and privacy, investigating problems in real time learning and control for IoTs and cyberphysical systems, loss of privacy from analytics, techniques for secure analytics, data and computation outsourcing, and valuation.”
Director, Purdue Center for the Science of Information, Saul Rosen Distinguished Professor of Computer Sciences, Professor of Electrical and Computer Engineering
“Healthcare organizations like RCHE develop and apply data science methodologies to make use of the wealth of different sources of health information to create evidence-based approaches to personalized medicine, improve care pathways in delivery, and empower individuals to effectively participate in their health and wellness.”
Director of the Regenstrief Center for Healthcare Engineering (RCHE), Professor of Industrial Engineering
“Advances in Data Science hold promise to dramatically improve resource efficiency and effectiveness in accomplishing defense and security missions. To unlock the promise, several challenges must be addressed. For example, how to optimize data gathering and structuring to determine what is the most valuable data to collect and/or ignore? Or, how to maximize human-machine symbiosis via combinations of novel data representations, algorithms, visualizations, and computing architecture that enable never-before see functionality on small, edge devices and platforms? And, in all cases, how to proceed in a way compliant with moral and ethical standards and policies that ensure this compliance.”
Director of the Institute for Global Security and Defense Innovation (i-GSDI), Professor, Aeronautics and Astronautics, Director, Center for Integrated Systems in Aerospace