Uploaded February 2024 | Updated September 2026, 2 weeks ago
Allen School Colloquia Series
Title: The Radical Solution: Machine Learning and the Hydroxyl Radical for Air Quality and Climate
Speaker: Qindan Zhu (MIT)
Abstract: The hydroxyl radical (OH) lies at the nexus of climate and air quality as the primary oxidant for both reactive greenhouse gases and many hazardous air pollutants. Lacking direct observations, interannual trends of OH either in urban areas or at continental-to-global scale are not well understood due to the short lifetime and high spatial heterogeneity of OH. This talk will describe two examples from my research predicting OH using a synthesis of chemistry/climate model, satellite observations and machine learning (ML). At the urban scale, I will present OH predictions for 49 North American cities between 2005 and 2014 and show how these OH predictions can be used to inform the best control strategy of ozone pollution in each city. I will also present the ML predicted OH trends at continental-to-global scale. As the primary loss pathway for methane is its reaction with OH, I will utilize these OH predictions to interpret observed methane trends. Finally, I will provide an overview of OH sensitivity to factors modulated by climate and discuss how OH is expected to respond to future climate change.
Bio: Qindan is a NOAA Climate & Global Change Postdoc Fellow working with Arlene Fiore at MIT, focusing on fully coupled chemistry-climate models and chemistry-climate interaction. She earned her bachelor's degree with dual majors in environmental science and mathematics from Peking University in 2017 and completed her PhD in Earth and Planetary Science at the University of California, Berkeley, under the guidance of Ron Cohen. Her research encompasses satellite observations of air pollutants, airborne flux measurements, weather sensitive emissions and hydroxyl radical chemistry. A special emphasis in her work is the synthesis of machine learning, models, and observations to advance the understanding of the interplay between air quality and climate change.
This video is closed captioned.
Allen School Colloquia Series
Title: The Radical Solution: Machine Learning and the Hydroxyl Radical for Air Quality and Climate
Speaker: Qindan Zhu (MIT)
Abstract: The hydroxyl radical (OH) lies at the nexus of climate and air quality as the primary oxidant for both reactive greenhouse gases and many hazardous air pollutants. Lacking direct observations, interannual trends of OH either in urban areas or at continental-to-global scale are not well understood due to the short lifetime and high spatial heterogeneity of OH. This talk will describe two examples from my research predicting OH using a synthesis of chemistry/climate model, satellite observations and machine learning (ML). At the urban scale, I will present OH predictions for 49 North American cities between 2005 and 2014 and show how these OH predictions can be used to inform the best control strategy of ozone pollution in each city. I will also present the ML predicted OH trends at continental-to-global scale. As the primary loss pathway for methane is its reaction with OH, I will utilize these OH predictions to interpret observed methane trends. Finally, I will provide an overview of OH sensitivity to factors modulated by climate and discuss how OH is expected to respond to future climate change.
Bio: Qindan is a NOAA Climate & Global Change Postdoc Fellow working with Arlene Fiore at MIT, focusing on fully coupled chemistry-climate models and chemistry-climate interaction. She earned her bachelor's degree with dual majors in environmental science and mathematics from Peking University in 2017 and completed her PhD in Earth and Planetary Science at the University of California, Berkeley, under the guidance of Ron Cohen. Her research encompasses satellite observations of air pollutants, airborne flux measurements, weather sensitive emissions and hydroxyl radical chemistry. A special emphasis in her work is the synthesis of machine learning, models, and observations to advance the understanding of the interplay between air quality and climate change.
This video is closed captioned.
![[Audio Descriptions] I Am CSE Overview](https://i.ytimg.com/vi/nvSgXXABE0k/mqdefault.jpg "[Audio Descriptions] I Am CSE Overview
In this mash-up of footage from our collection of I Am CSE videos, three Allen School student researchers describe their work in robotics, accessibility, and ubiquitous computing—and explain why the Allen School is a great place to do leading-edge research.
A version of this video without audio descriptions is available here: https://youtu.be/Yzz_nTIohwQ")
![[Audio Descriptions] I Am CSE: Melanie Sclar](https://i.ytimg.com/vi/oXbk5FnpUnE/mqdefault.jpg "[Audio Descriptions] I Am CSE: Melanie Sclar
Melanie Sclar, a graduate student in the Allen School’s Natural Language Processing group, explains her work to advance the ability of large language models to engage in human-like reasoning known as “theory of mind” and why the Allen School is a big destination for researchers in AI.
This video is closed captioned.
A version of this video without audio descriptions is available here: https://youtu.be/OGQzgUVKwb0.")
![[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.")
