Lessons and Opportunities in Large Scale Networks and Smart Health Application
By Dr. Chen-Nee Chuah, Electrical and Computer Engineering, U.C. Davis
For more details: https://www.meetup.com/SF-Bay-ACM/eve...
Abstract:
Data science and statistical/machine learning techniques can be leveraged to tackle various predictive and decision control problems in a wide range of application domains. This talk will focus on two domains: (1) large-scale networked systems such as IP backbone or wireless cellular networks, and (2) smart health applications. The first part of the talk will discuss lessons learned and opportunities in Internet measurement area. We will demonstrate how flexibility of software-defined networking (SDN) can be leveraged to adapt measurement rules based on optimal online strategies to augment traditional network inference techniques to obtain better estimates of network characteristics, such as traffic matrix or per-hop delay/loss rates. We will discuss the importance of data pre-processing, featurization, and choice of models by case studies from our prior work on detecting malicious activities in wireless networks and modeling user activity graphs on massive online social platforms. The second part of the talk focuses on opportunities and challenges that arise in applying IoTs, big data, AI, and machine learning (ML) techniques to smart health domain such as AI-assisted critical patient care or medical imaging. Specifically, we will draw examples from our on-going collaborative projects with UC Davis Medical Center, the Alzheimer Disease Center, and the MIND Institute in Sacramento.
Biography:
Chen-Nee Chuah is currently the Child Family Professor in Engineering at the Department of Electrical and Computer Engineering at the University of California, Davis. She received her B.S. in Electrical Engineering from Rutgers University, and her M. S. and Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley. Her research interests include Internet measurements, network management, and cybersecurity. Her more recent projects focus on applying data science and intelligent learning techniques to tackle predictive and decision control problems in societal-scale networked systems, such as massive online social platforms, intelligent transportation systems, and smart health applications. Chuah has experience leading numerous funded projects as lead-PI or Co-PI from NIH, NSF, DoD, and industry, including multi-institution grants. She was a recipient of the NSF CAREER Award and was named a Chancellor’s Fellow of UC Davis in 2008. She has served as an Associate Editor for IEEE/ACM Transactions on Networking and IEEE Transactions on Mobile Computing. Chuah is a Fellow of the IEEE and an ACM Distinguished Scientist.