Summary:
Throughout history, interactions between scientific inquiries and technological advances have powered discoveries and a deeper understanding of the natural world. Conversely, scientific challenges spurred the development of technology, which in turn opened new avenues for scientific inquiry. This phenomenon of innovation is now occurring as planetary- scale collaborations using ever-more expansive technical platforms. There are numerous modern examples of this science and technology innovation cycle, such as space exploration,the human genome project, the large hadron collider, hypertext, and the world wide web, toname a few. These advances enabled by high-performance computing laid the foundation fortoday’s artificial intelligence sweeping the world. Machine learning (ML) and artificial intelligence will profoundly influence all aspects of our lives. We see persistent advances at an accelerating rate involving AI, and its practical applications appear in every imaginable sector of the economy. At the same time, we see opportunities to raise some more profound questions in the scientific domain:
-How will ML change the way that we think about and practice science?
-Are there limits to human cognition that ML can overcome, which will create entirely new fields of science?
-Will AI fundamentally revise what constitutes scientific understanding?
In this era of intersection between science and technology of machine learning, the College of Science at Virginia Tech should organize a program using ML to seek new discovery pathways for a deeper understanding of science. Such a long-term effort should grow as a multidisciplinary collaboration involving natural scientists, computer scientists, policymakers, philosophers, ethicists, and other pertinent stakeholders. College of Science already has several excellent AI-related programs, and these programs can be the starting point for an initiative. This new initiative can be organized through the following team effort.
-Establish an interest group with representatives from each department.
-Take an inventory of existing and planned research efforts that intersect with AI.
-Invite internal and external speakers to help paint the big picture.
-Define an aspirational set of challenges or hypotheses.
-Break down these aspirations into a set of projects for execution and priority.
-Identify a management and governance structure.
-Identify the timeline and resources needed and means to gain resources.
-The Arlington Innovation Center: Health Research volunteers to lead the organizing effort of ML
for Science.
Current Members:
Arpit Dua, PhD - Virginia Tech - https://www.phys.vt.edu/About/people/Faculty/arpit-dua.html
Pang Du, PhD - Virginia Tech - https://www.stat.vt.edu/people/stat-faculty/du-pang.html
Feng Guo, PhD - Virginia Tech - https://www.stat.vt.edu/people/stat-faculty/guo-feng.html
Ali Habibniam PhD - Virginia Tech - http://www.alihabibnia.com/
Jennifer Mullekom, PhD - Virginia Tech - https://www.stat.vt.edu/people/stat-faculty/vanmullekom-jennifer.html
Tom Woteki, PhD - Virginia Tech - https://www.stat.vt.edu/people/stat-faculty/woteki-tom.html
News and Information (TBD):
