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 and plans to fund more in the near future. The awards come through the company’s research arm, Cisco Research, which connects Cisco’s own engineers and researchers with academic research labs to explore technologies with potential to maximize the company’s business, technological, and societal impact.
“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, PhD, Cisco research 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.”
Faculty members leading the research represent four University colleges: the Carlson School of Management, College of Liberal Arts, College of Science and Engineering, and the Medical School. The projects span the subjects of technology for health care, ethics in artificial intelligence, and edge computing (bringing data processing and storage closer to the physical devices that need them, rather than sending it all to a distant data center, to cut down on the response times and bandwidth).
Brett Schreiner, corporate relations officer with the University of Minnesota Foundation, said the range of expertise Cisco was seeking made the University a good fit for the collaboration.
“Cisco is looking to be on the cutting edge of new technology and the University of Minnesota has numerous faculty and researchers that cross technology with health care, AI, security and privacy, systems and networking, and the list goes on,” Schreiner said. “All of these areas interest Cisco as they look to position themselves and their industry positively for the future.”
A Research Agreement—in Record Time
UMN Technology Commercialization and the Corporate and Foundation Relations team at the University of Minnesota Foundation routinely work together to identify new opportunities for industry collaboration, deepen existing relationships, and support faculty as they work with industry to establish sponsored research projects. Last year, they launched the Industry Engagement portal to help companies see what the University can offer and how to connect to learn more.
In February, Cisco Research reached out through this portal with an interest in sponsoring research. The team had money to fund new projects but was working on a deadline. It had to spend those funds by the end of April, meaning everyone involved had to work fast to line up projects and get a research agreement signed.
Leza Besemann, senior manager of marketing and corporate engagement with UMN Technology Commercialization, quickly called for research proposals from the University community. Over two dozen ideas came in.
“UMN is a large university with faculty and staff researchers across all the areas of expertise Cisco is interested in,” she said. “Because we are so comprehensive and there are many cross-disciplinary research teams, our researchers were able to respond with creative project ideas.”
After hearing a series of short presentations and then participating in longer conversations, Cisco decided on the six projects it would fund. All but one of the principal investigators leading these project are new to industry sponsored research, Besemann said. The proposals and selection process gave them experience in discussing research with industry collaborators and honing future proposals.
At the same time as the proposals came in, UMN and Cisco were working to negotiate a master research agreement. This type of agreement lays out the terms for an industry sponsor to fund multiple research projects over a period of time, making it easier for collaborative sponsored research to move forward. It also significantly cuts down on the amount of legal review and negotiation that would otherwise have to take place—saving precious time needed to meet Cisco’s funding deadline.
The research agreement was modeled around the University’s Minnesota Innovation Partnerships (MN-IP) program, which provides a range of options that reduce the risk and cost a company might face when sponsoring research and licensing any intellectual property that results from it. The terms of MN-IP streamline the process of Cisco using the research discoveries that may come out of the funded projects to develop new products or services that benefit society.
In total, the entire research agreement and funding process took just three months. For Kompella and his team at Cisco Research, it was a pleasant surprise to get the master research agreement set up and signed so quickly. The team is already looking for other areas of research to collaborate with the University on.
“University-industry partnerships are not always easy to set up, as they are often subject to various laws and regulations that make for intellectual property transfer from university to industry an extremely tedious and arduous process,” he said. “We found University of Minnesota a fantastic place to collaborate and partner, with their extremely industry-friendly and forward-looking MN-IP initiative, which lays out an awesome foundational framework for us to foster a win-win collaborative agreement.”
Funded Projects
Below are the Cisco-funded projects as of May 2021, 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.