Human-Machine and Human-Robot Interaction For Long-Term User Engagement and Behavior Change
University of Southern California
Tuesday, October 22, 9:00-10:00 AM
Abstract: The nexus of in-home intelligent assistants, activity tracking, and machine learning creates opportunities for personalized virtual and physical agents / robots that can positively impacts user health and quality of life. Well beyond providing information, such agents can serve as physical and mental health and education coaches and companions that support positive behavior change. However, sustaining user engagement and motivation over long-term interactions presents complex challenges. Our work over the past 15 years has addressed those challenges by developing human-machine (human-robot) interaction methods for socially assistive robotics that utilize multi-modal interaction data and expressive agent behavior to monitor, coach, and motivate users to engage in heath- and wellness-promoting activities. This talk will present methods and results of modeling, learning, and personalizing user motivation, engagement, and coaching of healthy children and adults, as well as stroke patients, Alzheimer's patients, and children with autism spectrum disorders, in short and long-term (month+) deployments in schools, therapy centers, and homes, and discuss research and commercial implications for technologies aimed at human daily use.
Bio: Maja Matarić is Chan Soon-Shiong Distinguished Professor of Computer Science, Neuroscience, and Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center, and Vice Dean for Research in the Viterbi School of Engineering. Her MS and PhD are in Computer Science and Artificial Intelligence, and her BS in in Computer Science from the University of Kansas. She is Fellow of AAAS, IEEE, and AAAI, and the recipient of the US Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring, Anita Borg Institute Women of Vision Award in Innovation, the Okawa Foundation Award, NSF Career Award, MIT TR35 Award, and IEEE RAS Early Career Award. A pioneer of distributed robotics and, more recently, socially assistive robotics, Prof. Mataric’s research enables robots to help people improve through empowering interaction in rehabilitation, training, and education. Her group is developing robot-assisted therapies for autism, stroke, Alzheimer's and other domains,. She has published extensively, authored “The Robotic Primer” (MIT Press), has served as associate editor on three journals, on the NSF CISE Advisory Committee, and other advisory boards. Prof. Mataric’ is actively involved in leading K-12 STEM outreach efforts that engage student interest in science, technology, engineering, and math (STEM) topics and careers.
4 Systems Perspectives into Human-Centered Machine Learning
University of Washington and Apple
Wednesday, October 23, 9:00-10:00 AM
Abstract: Machine learning (ML) has had a tremendous impact in across the world over the last decade. As we think about ML solving complex tasks, sometimes at super-human levels, it is easy to forget that there is no machine learning without humans in the loop. Humans define tasks and metrics, develop and program algorithms, collect and label data, debug and optimize systems, and are (usually) ultimately the users of the ML-based applications we are developing.
In this talk, we will cover 4 human-centered perspectives in the ML development process, along with methods and systems, to empower humans to maximize the ultimate impact of their ML-based applications. In particular, we will cover:
- Developer tools for ML that allow a wider range of people to create intelligent applications, even on mobile devices.
- Learning to optimize the performance and power of ML models on a wide range of hardware backends and mobile devices.
- Closing the gap between the loss function we optimize in ML and the product metrics we really want to optimize.
- Helping humans understand why ML models make each prediction, when these models will break, and how to improve them.
Bio: Carlos Guestrin is the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington. He is also the Senior Director of Machine Learning and AI at Apple, after the acquisition of Turi, Inc. (formerly GraphLab and Dato), a company Carlos co-founded, which developed a platform for developers and data scientist to build and deploy intelligent applications. His team also released a number of popular open-source projects, including XGBoost, MXNet, TVM, Turi Create, LIME, GraphLab/PowerGraph, SFrame, and GraphChi. His previous positions include the Finmeccanica Associate Professor at Carnegie Mellon University and senior researcher at the Intel Research Lab in Berkeley. Carlos received his MSc and PhD in Computer Science from Stanford University, and a Mechatronics Engineer degree from the University of Sao Paulo, Brazil. Carlos’ work received awards at a number of conferences and journals: KDD, IPSN, VLDB, NIPS, UAI, ICML, AISTATS, JAIR, ACL, and JWRPM. He is also a recipient of the ONR Young Investigator Award, NSF Career Award , Alfred P. Sloan Fellowship, and IBM Faculty Fellowship. Carlos was named one of the 2008 ‘Brilliant 10’ by Popular Science Magazine, received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE).
SIGMOBILE Test-of-Time Paper Award
Patrick Murphy, Ashutosh Sabharwal, and Behnaam Aazhang
European Signal Processing Conference, 2006.
WARP was a groundbreaking open-source specialized hardware platform for high-performance wireless research. As one of the very few university hardware projects that moved outside the university, WARP has served as an experimental enabler for hundreds of ideas, which otherwise would have hardly been demonstrated, due to code base limitations and the large cost of wireless platforms capable of supporting high-end research. In the process, WARP was instrumental in changing the way the SIGMOBILE wireless community did research – strong experimental evidence versus oversimplified simulations.
Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application
Emiliano Miluzzo, Nicholas D. Lane, Kristof Fodor, Ronald Peterson, Hong Lu, Mirco Musolesi, Shane B. Eisenman, Xiao Zheng, and Andrew T. Campbell
ACM SenSys, 2008.
CenceMe was the first paper to demonstrate how smartphones can be used to derive rich behavioral insights continuously from onboard sensors. Since its publication, the work has inspired a huge body of research and commercial endeavors that has continued to increase the breadth and depth of personal sensing. Some of the activity inference methods that are now common in smartphone operating systems can be traced back to the original CenceMe system.