Workshop Speaker Bios
Dr. Harriet Kung
Deputy Director for Science Programs, Office of Science, U.S. Department of Energy
Nicki Hickmon (Argonne National Laboratory)
Nicki is the AI4ESP PI and ARM Associate Director for Operations. Beyond leading AI4ESP, she directs instrument and data services operations bringing observations to users around the world. Interests are AI4ESP vision, digital twins, AI enhanced data management and quality control.
Gary Geernaert (DOE BER)
Director of DOE’s EESSD, in Office of Science/BER, that in turn supports university and laboratory research to advance the predictability of climate and environmental change using capabilities such as the ARM, EMSL, and HPC user facilities.
Barb Helland (DOE ASCR)
Barb Helland is DOE’s Assoc. Director of Science for ASCR. She leads strategy and funding for applied mathematics, software, ML, artificial intelligence, and hardware system research, and she oversees DOE’s Leadership Class Computing facilities at ORNL, LBNL, and ANL.
Haruko Wainwright (Lawrence Berkeley National Laboratory)
Haruko Wainwright is a staff scientist at Lawrence Berkeley National Laboratory. Her research focuses on environmental informatics, aiming to improve understanding and predictions in Earth and environmental systems through mechanistic modeling and machine learning.
Forrest Hoffman (Oak Ridge National Laboratory)
Forrest develops and applies Earth system models at scale to investigate the global carbon cycle and biogeochemical cycle feedbacks and applies machine learning methods to problems in landscape ecology, ecosystem modeling, remote sensing, and large-scale climate data analytics.
Scott Collis (Argonne National Laboratory)
Scott Collis is an Atmospheric Scientist at Argonne. With a focus on precipitation processes he uses open source software and diverse analysis techniques (including AI) to garner insight from remotely sensed data. Edge computing enthusiast, weather nerd and science communicator.
Rick Stevens (Argonne National Laboratory)
My research spans the computational and computer sciences from high-performance computing architecture to the development of tools and methods for bioinformatics, cancer, infectious disease and other problems in science and engineering.
Grace Kim (Booz Allen Hamilton)
Scientific research in oceanography and climate modeling. Application of new technologies for Earth observation and modeling. Developing frameworks to facilitate scientific collaboration. Advancing solutions that combine human ingenuity and artificial intelligence.
Prabhat Ram (Microsoft)
Prabhat supports AI+HPC workloads with the Azure HPC team at Microsoft. In the recent past, Prabhat led the Data and Analytics Services team at NERSC and the Big Data Center collaboration between NERSC, Intel, Cray, UC Berkeley, UC Davis, NYU, UBC, Oxford and Liverpool.
Kirk Borne (DataPrime Inc.)
Dr. Borne is Chief Science Officer at AI startup DataPrime Inc. He is a career data professional, data scientist, and research astrophysicist. He is interested in applications of AI and machine learning to many science disciplines, particularly space and earth sciences.
Ruby Leung (Pacific Northwest National Laboratory)
My research broadly cuts across multiple areas in regional/global modeling and analysis of climate and water cycle including orographic precipitation, mesoscale convective systems, extreme events, monsoon, and land processes, land-atmosphere and aerosol-cloud interactions.
Amy McGovern (University of Oklahoma)
Amy McGovern is a Lloyd G. and Joyce Austin Presidential Professor in the School of Computer Science and School of Meteorology at the University of Oklahoma. She directs the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography.
Pierre Gentine (Columbia University)
Pierre Gentine is Maurice Ewing and J. Lamar Worzel professor at Columbia University and studies the terrestrial water and carbon cycles. Gentine is recipient of the NSF, NASA and DOE early career awards, and AGU Global Environmental Changes Early Career and AMS Meisinger award.
Beth Drewniak (Argonne National Laboratory)
Dr. Drewniak works on developing land surface models. Her interests are wide ranging and include agriculture and human management practices, biogeochemical cycling, root dynamics, biogenic emissions, and optimized carbon allocation in plants.
Giri Prakash (Oak Ridge National Laboratory)
Expertise in building next-generation FAIR data and computing architecture to the increasing demands of data volume, rates, and complexity. Research interest are enabling open science by adapting AI/ML capabilities in scientific data center operations, data access and discovery
Christa Brelsford (Oak Ridge National Laboratory)
Cities concentrate interactions across human, natural, and infrastructure systems. I use data science tools from economics, geography, network science, and spatial statistics to describe co-evolutionary processes between human systems and our physical context, mostly in cities.
Charuleka Varadharajan (Lawrence Berkeley National Laboratory)
I am a biogeochemist and data scientist studying impacts of climate change and hydrological disturbances on water quality. My expertise is in using ML and statistical models for hydrological predictions and analysis, FAIR data management, data integration and visualization.
Chaopeng Shen (Pennsylvania State University)
Shen’s pioneering work showed how deep learning (DL) offers a full suite of prediction services for hydrologic and water quality variables. His recent differentiable parameter learning work shows the promise of systematically fusing process-based models and DL to gain knowledge.
Rob Ross (Argonne National Laboratory)
Rob Ross is a Senior Computer Scientist at Argonne National Laboratory and the Director of the DOE SciDAC RAPIDS Institute for Computer Science, Data, and Artificial Intelligence. Rob’s research interest is in system software for high performance computing.
Mavrik Zavarin (Lawrence Livermore National Laboratory)
Dr. Zavarin's research is focused on experimental and modeling efforts to understand and simulate reactive transport in the environment. His recent interests are in the application of data mining and data science in the geosciences.
Science-guided data sciences and machine learning enhanced physics for earth systems sciences and engineering, especially for the integrated water cycle including hydrometeorological extremes under climate change, with a focus on characterizing variability, uncertainty, and risks
Nathan Hodas (Pacific Northwest National Laboratory)
I conduct research and development of deep learning applied to scientific problems. My current focus is on few-shot learning and AI assurance.
Nathan Urban (Brookhaven National Laboratory)
I work in climate uncertainty quantification, climate adaptation, surrogate/emulator/reduced order models, decision making under uncertainty, optimal experimental design, and hybrid physical-ML modeling and SciML.
Tapio Schneider (California Institute of Technology)
Atmosphere and climate dynamics; cloud and climate modeling; data assimilation and machine learning.
Alison Appling (United States Geological Survey)
Alison Appling is a water data scientist with the U.S. Geological Survey. She uses machine learning to predict water quality in rivers and lakes, and she leads a broader USGS effort to advance data-driven approaches for predicting water quality and quantity.
Po-Lun Ma (Pacific Northwest National Laboratory)
I am an atmospheric modeler working on understanding, and better representing, processes and environmental factors that affect the prediction of aerosols and clouds and their roles in the climate system.
Frank Alexander (Brookhaven National Laboratory)
My interest is in the development and application of tools from physics and machine learning toward the solution of problems involving complex systems. This includes optimal resource allocation under constraints as applied to challenges in biology, climate etc.
Matthew Hoffman (Los Alamos National Laboratory)
My research centers on projecting future sea-level rise, with a focus on the contribution from ice sheets. My research interests include ice-sheet dynamics and physical processes, the interactions of ice sheets and the polar ocean, and solid earth impacts on regional sea level.
Xingyuan Chen (Pacific Northwest National Laboratory)
I mainly work on data-model integration for watershed hydrobiogeochemistry. We leverage machine learning methods to address various challenges in hyper-resolution watershed modeling, including parameter estimation, uncertainty quantification, scaling, and others.
Laure Zanna (New York University)
We aim to advance the fundamental understanding of ocean dynamics and its role in the climate system to provide reliable climate projections. Our current work include ocean warming, sea level extremes, physics-aware subgrid scale parameterizations with machine learning.
Katie Dagon (National Center for Atmospheric Research)
Katie Dagon is a climate scientist at the National Center for Atmospheric Research. Her research focuses on modeling the impacts of climate change on climate variability and extreme events. She is also interested in machine learning approaches to climate science and modeling.
Maria Molina (National Center for Atmospheric Research)
My research interests are focused on the application of machine learning and deep learning methods to better understand and predict phenomena across a range of scales, with a specific focus on modes of variability on subseasonal to longer timescales.
Line Pouchard (Brookhaven National Laboratory)
Line’s expertise is in Reproducibility in AI-driven scientific discovery at exascale, transparency and FAIR data, and provenance in computational workflows in a variety of application domains. She has been at DOE for over 20 years, previously ORNL.
My focus is in the intersection of high performance computing, machine learning for science, co-design of ML-focused or traditional architectures and algorithms, performance portability of scientific software, and combinatorial scientific computing.
Jim Ang (Pacific Northwest National Laboratory)
Jim is the Chief Scientist for Computing in the Physical and Computational Sciences Directorate, and PNNL lead for DOE’s Advanced Scientific Computing Research program. He also leads the Data-Model Convergence LDRD initiative to accelerate scientific discovery via co-design.