BIN LI, an assistant professor of electrical, computer and biomedical engineering at the University of Rhode Island’s College of Engineering, is a recipient of a Google Faculty Research Award. The award will fund research of a crowd-learning app in the field of computer science and engineering. Google LLC’s program supports research in computer science, engineering and related fields at academic institutions around the world. Prior to joining the URI faculty in August 2016, Li was a postdoctoral researcher in the Coordinated Science Lab at the University of Illinois at Urbana-Champaign.
What does it mean to you to be awarded a Google Faculty Research Award? It is a great honor for me to receive this award. This award recognizes our research efforts on emerging crowd-learning applications. In addition to financial support, the award provides a great opportunity for my research group to work closely with Google. It is also very exciting for my students. Google Faculty Research Awards are structured as seed funding to support one graduate student for one year and are awarded as an unrestricted gift. I share the award with professor Jia Liu from Iowa State University.
What is the breadth of focus of your research, and how will Google assist you with that endeavor? My research focuses on networking and machine learning and their applications in mobile crowd-learning, virtual/augmented reality, and mobile edge computing. The awarded project is to ensure that emerging mobile crowd-learning platforms, such as GasBuddy, Basket, Google Waze and Pavemint, provide timely and accurate information for mobile users. The award facilitates collaboration with Google researchers in their Geo group and enables us to learn the practical challenges of our incentive mechanism design, which is essential for a large-scale adoption of mobile crowd-learning applications. The goal is to develop a theoretical foundation for optimizing information timeliness and accuracy in mobile crowd-learning applications, which can guide practical incentive mechanism design.
Do you have an innovative project that you are working on, and what is its intended purpose or objective? Besides working on mobile crowd-learning applications, I am conducting in-depth research on mobile edge computing sponsored by the National Science Foundation. The goal is to enable compute-limited mobile devices to perform compute-intensive machine-learning tasks in real time, such as image recognition, speech recognition and language translation.
What made you pursue a career as a professor of electrical, computer and biomedical engineering? I have been extremely interested in the intersection of networking and machine learning. My position as a professor at the University of Rhode Island allows me to conduct research that freely explores this area and its various applications. It also allows me to act as a mentor to students; providing guidance and seeing them succeed in their careers is very gratifying.
