Organizers
Ritwik Gupta
(UC Berkeley)
Ritwik Gupta (he/him) is a Ph.D. student at the University of California, Berkeley. His research focus is on computer vision for humanitarian assistance and disaster response, as well as the development of policies related to the use of machine learning in developing areas. His research has been used widely by organizations such as the United Nations, CAL FIRE, and the International Red Cross. Ritwik is a Graduate Fellow with the Berkeley Risk and Security Lab, a Research Fellow at Berkeley's Human Rights Center, and a Fellow at Berkeley's AI Policy Hub. Ritwik is the Director of the Berkeley AI Research Climate Initiative which brings together researchers to work on AI research through the lens of climate change and helps to translate that work into real-world applications.
ritwikgupta [at] berkeley [dot] edu Google ScholarLaura Mansfield
(Stanford University)
Laura Mansfield (she/her) is a postdoctoral researcher at Stanford University. Her research focuses on Bayesian statistics and machine learning methods for the development of subgrid-scale gravity wave parameterizations in climate models, in order to improve the representation of stratospheric dynamics. She works on both the calibration and uncertainty quantification of conventional and on machine learning parameterizations. She completed her Ph.D. in Gaussian processes for climate change projection from the University of Reading and Imperial College London in 2021. She is the convener for the AGU session on Machine Learning Subgrid-Scale Parameterizations. She will be participating virtually.
lauraman [at] stanford [dot] edu Google ScholarTian Zheng
(Columbia University)
Tian Zheng (she/her) is Professor and Department Chair of Statistics at Columbia University. She is Chief Convergence Officer and Education Director of the NSF Science Technology Center “Learning the Earth with AI and Physics”. In her research, she develops novel methods for exploring and understanding patterns in complex data from different application domains such as biology, psychology, climate modeling, etc. In 2016, she designed Applied Data Science, a project-based learning course that offers mini data-intensive challenges. She was Associate Director for Education at Columbia’s Data Science Institute from 2017 to 2020 and a faculty advisor for Columbia Statistics Club from 2016 to 2021. She has organized and served as a judge in 10+ data competitions and hackathons.
tian.zheng [at] columbia [dot] edu Google ScholarMargarita Geleta
(UC Berkeley & Stanford University)
Margarita Geleta (she/her) is a Ph.D. student at the University of California, Berkeley, affiliated with Berkeley AI Research (BAIR) and the Stanford Biomedical Data Science Department (Stanford DBDS). She has led research on multimodal deep steganography at the Image Processing Group (UPC), developed algorithms for genotype compression, simulation, and imputation in the Bustamante Lab at the Stanford School of Medicine, and interned at Amazon.com as an Applied Scientist. In addition to teaching at UPC, UC Irvine, and tech academies, Margarita has organized workshops and career fairs with +200 participants and co-organized Europe’s biggest student hackathon with +700 participants. She will be participating virtually.
geleta [at] berkeley [dot] edu Google ScholarJerry Lin
(UC Irvine)
Jerry Lin (he/him) is a PhD candidate at UC Irvine and graduate research assistant in the NSF Learning the Earth with Artificial Intelligence and Physics (LEAP) Center working on neural network emulators of convection and radiation coupled inside climate models. His current work involves developing and coupling stochastic parameterizations, and he previously developed push-button capabilities for multi-hundred ensemble tests for coupled hybrid physics-ML climate simulations. He is a co-author and active maintainer for ClimSim and an organizer for the upcoming Kaggle competition. He has previously managed a student-run data science course at UC Berkeley.
jerryL9 [at] uci [dot] edu Google ScholarYongquan Qu
(Columbia University)
Yongquan Qu (he/him) is a Ph.D. candidate at Columbia University. His primary research delves into the intersection of scientific machine learning and computational methods, aiming to enhance the modeling and understanding of turbulence in the atmospheric boundary layer. He also works on developing a hybrid framework that integrates differentiable programming, probabilistic machine learning with data assimilation in the context of climate projections. He is a graduate research assistant of the NSF Science and Technology Center “Learning the Earth with Artificial Intelligence and Physics” (LEAP). He is also affiliated with the “Multiscale Machine Learning In Coupled Earth System Modeling” (M2LInES) project.
yq2340 [at] columbia [dot] edu Google ScholarMaja Rudolph
(Bosch Research)
Maja Rudolph (she/her) is a Senior Research Scientist at the Bosch Center for Artificial Intelligence, where she works in the field of deep probabilistic modeling. She completed her Ph.D. in Computer Science at Columbia University, advised by David Blei, in 2018. Maja holds an MS in Electrical Engineering from Columbia University and a BS in Mathematics from MIT. Her research lies at the intersection of Bayesian machine learning and deep learning, with a focus on deep probabilistic sequence models, neural transformation learning, and embedding methods. She will be participating virtually.
marirudolph [at] gmail [dot] com Google ScholarMike Pritchard
(NVIDIA corporation & University of California, Irvine)
Mike Pritchard (he/his) is the Director of Climate Simulation at NVIDIA, an associate professor of Earth System Science at UC Irvine, and the Institutional Integration Director for the NSF Learning the Earth with Artificial Intelligence and Physics (LEAP) Center. He works at the interface between next- generation climate simulation and machine learning. His main focus is on accelerating cloud-resolving climate simulations using physics-informed machine learning. He is also interested in reinforcement learning approaches to climate model calibration, understanding the limits of autoregressive weather simulations trained on observational data, and AI-assisted low-latency analysis of large high-resolution climate simulation datasets. He will be participating virtually.
mpritchard [at] nvidia [dot] com Google Scholar