Collaboration with Cisco explores frontier of data technologies
CSE faculty are at the forefront of new research projects
July 14, 2021
A new collaboration between University of Minnesota researchers and Cisco Systems seeks to advance cutting-edge technologies that transform the way people access, manage, and protect data.
Cisco—which develops, manufactures, and sells networking hardware, software, telecommunications equipment, and other high-technology services and products (like WebEx)—has funded six projects at the University as of May 2021 and plans to fund more in the near future. College of Science and Engineering researchers are involved in four of the six projects, which address topics like technology for health care, ethics in artificial intelligence, and edge computing.
The awards come through Cisco Research, the company arm that connects its own engineers and scientists with academic research labs.
“Our recent sponsored research awards program specifically looks at sponsoring research in universities with intellectual property sharing agreements to assist in building new ventures within Cisco, especially within areas of emerging tech where Cisco has not been traditionally well represented,” said Ramana Kompella, the company’s head in systems and networking. “Our hope is that our collaborations with the best and brightest in academia allow us to augment our internal abilities to provide innovative and transformative solutions for our customers.”
Across their four projects, CSE researchers are collaborating with faculty from the University’s Carlson School of Management, College of Liberal Arts, and Medical School.
“We've very excited to be working with Cisco in this way,” said Joseph Konstan, CSE’s Associate Dean for Research. “CSE researchers are leaders in computing, communications, medical device technology, AI, security, and ethics, and this program provided a very quick and low-effort process to explore mutual research ideas and make exciting and important projects happen. Cisco's funding not only advances research—it also supports and trains the graduate students who are carrying out most of this research.”
Funded Projects
Below is a list of the Cisco-funded projects with University research leads’ names and departments.
Edge Computing
Innovating Edge Computing Support over Commercial 5G Networks
Kia Bazargan (Electrical and Computer Engineering)
Aims to apply an unconventional computing method, which transforms complex computations into simpler ones without losing accuracy, to upcoming edge computing applications.
Innovating Edge Computing Support over Commercial 5G Networks
Feng Qian (Computer Science and Engineering) and Zhi-Li Zhang (Computer Science and Engineering)
Aims to innovate edge computing support over 5G to improve the overall quality of the service, decisions around proactive and adaptive content delivery, and shifting between LTE and 5G to balance the trade-off between energy usage and performance.
Ethics in AI
Ethics in AI: Privacy-Preserving Machine Learning and Decision Making
Jie Ding (Statistics), Xuan Bi (Information and Decision Sciences), Mingyi Hong (Electrical and Computer Engineering)
Aims to develop artificial intelligence methods that better preserve data privacy when multiple smart devices, clinics, and organizations are working together.
Tech for Health Care
Automated Imaging-Based Fracture Detection in Trauma Care
Ju Sun (Computer Science and Engineering), Chris Tignanelli (Surgery), Genevieve Melton-Meaux (Surgery)
Aims to develop an accurate and reliable automated fracture detection method based on CT scans that can be used in trauma and critical care environments to reduce the delays and errors that come along with the current practice of manually identifying fractures.
Sensing and Responding to Personalized Support Needs and Advancing Equity in Mental Health Care Delivery via Smartphone Mobile Applications
Kingshuk Sinha (Supply Chain and Operations)
Aims to explore how and when smartphone mobile applications can advance equity in mental health care delivery by providing real-time and personalized support and care referral that is both affordable and accessible to the target populations.
Understanding and Improving Federated Learning in Health Care
Mochen Yang (Information and Decision Sciences), Xuan Bi (Information and Decision Sciences)
Aims to understand the economic incentives behind partnerships among health care providers through the use of federated learning, which combines their data resources in building machine learning systems without having to share sensitive patient data.
Story repurposed with permission from the Office of the Vice President for Research. Read the original story by Kevin Coss.
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