Uploaded February 2024 | Updated September 2026, 2 weeks ago
2019 Research Showcase Keynote
Title: Data Science for Human Well-Being
Speaker: Tim Althoff (Paul G. Allen School of Computer Science & Engineering)
Date: November 20, 2019
Abstract: The popularity of wearable and mobile devices, including smartphones and smartwatches, has generated an explosion of detailed behavioral data. These massive digital traces provide us with an unparalleled opportunity to realize new types of scientific approaches that provide novel insights about our lives, health, and happiness. However, gaining valuable insights from these data requires new computational approaches that turn observational, scientifically "weak" data into strong scientific results and can computationally test domain theories at scale.
In this talk, I will describe novel computational methods that leverage digital activity traces at the scale of billions of actions taken by millions of people. These methods combine insights from data mining, social network analysis, and natural language processing to generate actionable insights about our physical and mental well-being. Specifically, I will describe how massive digital activity traces reveal unknown health inequality around the world. I will demonstrate that modeling how fast we are using search engines enables new types of insights into sleep and cognitive performance. Further, I will describe how natural language processing methods can help improve mental health services for millions of people in crisis.
Bio: Tim Althoff is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His research advances computational methods to improve human well-being, combining techniques from Data Mining, Social Network Analysis, and Natural Language Processing. Tim holds Ph.D. and M.S. degrees from the Computer Science Department at Stanford University, where he worked with Jure Leskovec. Prior to his Ph.D., Tim obtained M.S. and B.S. degrees from the University of Kaiserslautern, Germany. He has received several fellowships and awards including the SAP Stanford Graduate Fellowship, Fulbright scholarship, German Academic Exchange Service scholarship, the German National Merit Foundation scholarship, a Best Paper Award by the International Medical Informatics Association, and the SIGKDD Dissertation Award 2019. Tim's research has been covered internationally by news outlets including BBC, CNN, The Economist, The Wall Street Journal, and The New York Times.
This video is closed captioned.
2019 Research Showcase Keynote
Title: Data Science for Human Well-Being
Speaker: Tim Althoff (Paul G. Allen School of Computer Science & Engineering)
Date: November 20, 2019
Abstract: The popularity of wearable and mobile devices, including smartphones and smartwatches, has generated an explosion of detailed behavioral data. These massive digital traces provide us with an unparalleled opportunity to realize new types of scientific approaches that provide novel insights about our lives, health, and happiness. However, gaining valuable insights from these data requires new computational approaches that turn observational, scientifically "weak" data into strong scientific results and can computationally test domain theories at scale.
In this talk, I will describe novel computational methods that leverage digital activity traces at the scale of billions of actions taken by millions of people. These methods combine insights from data mining, social network analysis, and natural language processing to generate actionable insights about our physical and mental well-being. Specifically, I will describe how massive digital activity traces reveal unknown health inequality around the world. I will demonstrate that modeling how fast we are using search engines enables new types of insights into sleep and cognitive performance. Further, I will describe how natural language processing methods can help improve mental health services for millions of people in crisis.
Bio: Tim Althoff is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His research advances computational methods to improve human well-being, combining techniques from Data Mining, Social Network Analysis, and Natural Language Processing. Tim holds Ph.D. and M.S. degrees from the Computer Science Department at Stanford University, where he worked with Jure Leskovec. Prior to his Ph.D., Tim obtained M.S. and B.S. degrees from the University of Kaiserslautern, Germany. He has received several fellowships and awards including the SAP Stanford Graduate Fellowship, Fulbright scholarship, German Academic Exchange Service scholarship, the German National Merit Foundation scholarship, a Best Paper Award by the International Medical Informatics Association, and the SIGKDD Dissertation Award 2019. Tim's research has been covered internationally by news outlets including BBC, CNN, The Economist, The Wall Street Journal, and The New York Times.
This video is closed captioned.
![[ASL] Toward Total Scene Understanding for Autonomous Driving—Drago Anguelov (Waymo)](https://i.ytimg.com/vi/pK5ChzMsfE0/mqdefault.jpg "[ASL] Toward Total Scene Understanding for Autonomous Driving—Drago Anguelov (Waymo)
Ben Taskar Distinguished Memorial Lecture
Title: Toward Total Scene Understanding for Autonomous Driving
Speaker: Drago Anguelov (Waymo)
Host: Anat Caspi
Date: January 25, 2024
Abstract: Machine learning has proven to be a key ingredient in building a performant and scalable Autonomous Vehicle stack, spanning key capabilities such as perception, behavior prediction, planning and simulation and evaluation. I will describe recent Waymo research on performant ML models and architectures that help us handle the variety and complexity of real world environments. I will also discuss how progress in building Autonomous Driving agents can impact people with disabilities and cover some current open questions about how to further enhance embodied AI agent capabilities with ML.
Bio: Drago joined Waymo in 2018 to lead the Research team, which focuses on pushing the state of the art in autonomous driving using machine learning. Earlier in his career he spent eight years at Google; first working on 3D vision and pose estimation for StreetView, and later leading a research team which developed computer vision systems for annotating Google Photos. The team also invented popular methods such as the Inception neural network architecture, and the SSD detector, which helped win the Imagenet 2014 Classification and Detection challenges. Prior to joining Waymo, Drago led the 3D Perception team at Zoox.
This video is closed captioned.
A version of this video without ASL interpretation is available here: https://youtu.be/zCJO7ONdPZM.")
![[Audio Descriptions] ArticuTool: A Modular Active End-Effector for Robot Assisted Feeding](https://i.ytimg.com/vi/rSqVBN42ZG0/mqdefault.jpg "[Audio Descriptions] ArticuTool: A Modular Active End-Effector for Robot Assisted Feeding
For a version without audio descriptions, visit https://youtu.be/0QLWAM63kqw")
