In 2018, Syracuse University established a Big Data and Data Analytics cluster, investing in bioinformatics, data mining and cybersecurity, physics, marketing and business analytics, and sport analytics. The mission of the cluster is to develop and apply computationally efficient data analysis technologies for problems involving large amounts of data in many different disciplines, with multidisciplinary collaborations between domain experts and computational researchers.
Big Data researchers will address problems such as secure data mining, statistical analysis, pattern recognition and anomaly detection, working with University research groups that have major computing and data-intensive research projects, and with the University's Information Technology Services. Innovative and transformative research efforts will build on the data-intensive research and infrastructure at Syracuse University, uniting data-driven domain application research with broad foundational research in data science in a “hub and spoke” model.
Current Areas of Focus
- Genomics: reproductive trait evolution and modeling gene regulatory network dynamics. Collaborating institutions include SUNY Upstate Medical University and SUNY College of Environmental Science and Forestry, Cornell University, University of Antioquia (Columbia), University of Arkansas and other global consortia.
- Sport management: NFL player health research and cause of death cohort study, as well as a virtual reality application that will teach social skills to students with autism across multiple platforms. Collaborating institutions include University of Kansas Center for Research on Learning and Ohio Center on Autism and Low Incidence Disabilities.
- Physics: NSF-funded construction of a high performance GPU cluster integrated with the open science grid, reproducibility in multi-messenger astrophysics to enable open science across disciplines, and exploration for a potential data innovation institute.
- Marketing: purchasing analytics analyzing consumer search and purchase behavior in digital and offline domains including video analytics, online brand communities and artificial intelligence in new product development.
- Engineering: fast, reliable and efficient data storage for big data, deep constrained learning for power systems, privacy-preserving artificial intelligence.
Recent Notable Awards
CUSE Collaborative Grant: Inference of gene regulatory networks using dense time-course mRNA sequencing and evolutionary algorithms (Yasir Ahmed-Braimah, PI)
CUSE Grant: Exploration of a Data Innovation Institute (Duncan Brown, PI)
NASA and BAERI: Large Scale Machine Learning Optimization with Evolutionary Algorithms (Chilukuri Mohan)
NSF EAGER Award: Reproducibility in Multi-Messenger Astrophysics (Duncan Brown, PI)
NSF: High Performance GPU Cluster (Sam Scozzafava, Eric Sedore and Duncan Brown)
NSF CORE: Deep Constrained Learning for Power Systems (Ferdinando Fioretto, PI)