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Presenter: Fabio Cassini (University of Verona)
Collaborator(s): Lukas Einkemmer
Title: Efficient 6D Vlasov simulation using the dynamical low-rank framework Ensign
Presenter: Peiyi Chen (University of Wisconsin-Madison)
Collaborator(s): Irene Gamba, Qin Li and Li Wang
Title: Recover Phonon Relaxation Time using Boltzman Transport Equation
Presenter: Jack Coughlin (University of Washington)
Title: Robust, Conservative Dynamical Low-Rank Vlasov Solutions via Macro-Micro Decomposition
Presenter: Pranab J Deka (KU Leuven)
Collaborator(s): Lukas Einkemmer, Ralf Kissmann
Title: Exponential Methods for Anisotropic Diffusion
Presenter: Federica Ferrarese (Università degli studi di Ferrara)
Title: Control plasma instabilities via an external magnetic field in a Vlasov-Poisson system.
Presenter: Antoine C.D. Hoffmann (EPFL (Ecole Polytechnique Fédérale de Lausanne))
Collaborator(s): B.J. Frei, P. Giroud-Grampon, P. Ricci
Title: A gyrokinetic moment-based approach for multi-scale multi-fidelity turbulence simulations
Presenter: Dimitrios Kaltsas (University of Ioannina)
Collaborator(s): P. J. Morrison, E. Tassi, G. N. Throumoulopoulos
Title: Using Dirac constraints to enforce quasineutrality in Maxwell-Vlasov dynamics
Presenter: Guillaume Le Bars (EPFL (Ecole Polytechnique Fédérale de Lausanne), Swiss Plasma Center)
Collaborator(s): G. Le Bars, J. Loizu, J.-P. Hogge, S. Guinchard, S. Alberti, A. Cerfon, F. Romano, J. Genoud, I. Gr. Pagonakis
Title: FENNECS: a flexible code to simulate non-neutral plasmas trapped in Penning-like annular potential wells
Presenter: Martina Prugger (Max Planck Institute for Plasma Physics)
Collaborator(s): Nicolas Crouseilles, Lukas Einkemmer
Title: An Exponential Integrator for the Drift-Kinetic Model
Presenter: Stefan Schnake (Oak Ridge National Laboratory)
Collaborator(s): Eirik Endeve, Steven Hahn, Cory Hauck, Coleman Kendrick, Phil Snyder, Miroslav Stoyanov
Title: An Adaptive Sparse-grid Discretization (ASGarD) for High-dimensional Kinetic Problems
Presenter: Kai Schneider (Aix-Marseille Université)
Collaborator(s): Philipp Krah, Xi-Yuan Yin, Julius Bergmann, Jean-Christophe Nave
Title: Zooming into Vlasov–Poisson using a Characteristic Mapping Method
Presenter: Rostislav-Paul Wilhelm (RWTH Aachen University)
Title: Discussion of potentials and draw-backs of using the Numerical Flow Iteration to solve the Vlasov equation
Presenter: Yukun Yue (University of Wisconsin, Madison)
Title: Control of Plasma Instability in Vlasov-Poisson System
Presenter: Hamad El Kahza (University of Delaware)
Collaborator(s): William Taitano, Jing-Mei Qiu, Luis Chacon
Title: Krylov-based Implicit Low-rank Method for Nonlinear Fokker-Planck Models.

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Presenter: Mishal Assif P K (University of Illinois at Urbana-Champaign)
Collaborator(s): Yuliy Baryshnikov
Title: Classifying Spaces for Data-Driven Analysis of Dynamical Systems
Presenter: Gisela Daniela Charó (University of Buenos Aires)
Collaborator(s): Denisse Sciamarella
Title: Templex : a method to study the topological fingerprinting of dynamical systems
Presenter: Juan Carlos Díaz-Patiño (Instituto de Neurobiolog√≠a)
Collaborator(s): Zeus Gracia-Tabuenca, Isaac Arelio Ríos, Nelsiyamid López Guerrero, Sarael Alcuauter.
Title: Topological Data Analysis applied to the study of the functional brain connectome.
Presenter: Mario R Gomez Flores (Ohio State University)
Collaborator(s): Facundo Mémoli
Title: Discriminative Power of Persistence Sets
Presenter: Shengli Jiang (University of Wisconsin, Madison)
Collaborator(s): Nanqi Bao, Alexander D. Smith, James J. Schauer, Reid C. Van Lehn, Manos Mavrikakis, Nicholas L. Abbott, Victor M. Zavala
Title: Scalable Extraction of Information from Spatio-Temporal Responses of Liquid Crystals Using Topology
Presenter: Shu Kanazawa (Kyoto University)
Collaborator(s): Yasuaki Hiraoka, Jun Miyanaga, Kenkichi Tsunoda
Title: Large deviation principle for persistence diagrams of random cubical filtrations
Presenter: Nkechi Nnadi (Wayne State University)
Collaborator(s): Daniel Isaksen (Supervisor)
Title: A Distance for Simplicial Complexes in Topological Data Analysis
Presenter: Osman Berat Okutan (Max Planck Institute for Mathematics in the Sciences)
Collaborator(s): Soheil Anbouhi, Washington Mio
Title: Analysis of Functional Data on Geometric Domains
Presenter: Sarah Percival (Michigan State University)
Collaborator(s): Enrique Alvarado, Robin Belton, Kang-Ju Lee, Sourabh Palande, Emilie Purvine
Title: Adaptive Covers for Ball Mapper
Presenter: Chun Yin Siu (Cornell University)
Collaborator(s): Gennady Samorodnitsky, Christina Lee Yu, Caroline He
Title: Betti Numbers of Preferential Attachment Complexes
Presenter: Živa Urbančič (Durham University)
Collaborator(s): Jeffrey Giansiracusa
Title: Ladder Decompositions in Persistent Homology
Presenter: Xinyi Wang (Michigan State University)
Collaborator(s): Erin Wolf Chambers, Elizabeth Munch, Sarah Percival
Title: A Distance for Geometric Graphs via the Labeled Merge Tree Interleaving Distance

A version of this article appeared in the March 2023 issue of Amstat News.

Scientists from across the disciplines, including statisticians and mathematicians, have spent decades making the case that our planet is rapidly warming due to anthropogenic emissions of carbon dioxide (CO2) and other greenhouse gasses. The catastrophic consequences of global warming are becoming undeniably obvious. Scientists, economists, and social scientists are now collaborating to improve our understanding and predictions of how Earth’s changing climate will impact humanity and the ecological and social systems upon which life relies.

As a key element of its mission, IMSI, the NSF-funded Institute for Mathematical and Statistical Innovation, held a Long Program on the mathematics and statistics needed for “Confronting Global Climate Change,” Sept. 19-Dec. 9, 2022. IMSI is designed to convene applied mathematicians, statisticians, and scientists who research topics with major societal implications. This long program was divided into six workshops that explored a range of key scientific issues that have direct bearing on humans’ understanding of our warming planet, the regional impacts on annual weather resulting from this evolving dynamical system, and predicting future risk, hazards, and damages due to extreme weather events and the social cost of carbon.

The Earth’s climate is a complex dynamical system whose physics takes place on spatiotemporal scales ranging from nanometers to kilometers, and microseconds to centuries. Modeling such a complex system and making predictions about future outcomes (that is, how climate begets weather), is further complicated by unknowns in, for example, the physics of aerosols and clouds. To improve some of the current shortcomings in climate models, researchers turn to statistical and machine learning strategies that use existing weather & climate data sets to further improve the outputs of these models in predicting future weather and climate.

Much of the discussion throughout the program focused on the verifiability, validity, and uncertainty quantification of climate models, particularly when applying these models to future weather and climate prediction at spatiotemporal scales that are useful for risk planning and adaptation to a warming planet. One key technical challenge lies in “downscaling” the relatively low resolution of the global climate models to the high resolution needed to make local climate impact studies of key variables such as precipitation and temperature. High resolution climate models are particularly important to those responsible for managing the socioeconomic risks and impacts of extreme events. For example, events like flooding and heat waves have major impacts on farmers, emergency management agencies, insurance companies, and just about everyone else thinking long-term about where to live, build, or work. Spatial downscaling introduces uncertainties which are necessary to quantify (i.e., uncertainty quantification), reduce, and communicate, so that the public and policy makers can rely on climate scientists and meteorologists with increasing confidence in their decision making. Linda Mearns (University Corporation for Atmospheric Research) is developing tools to deal with the uncertainties inherent in spatial downscaling from global climate models (ca. 20 km spatial resolution) to regional climate models (ca. 4 km). Her particular focus is on improving the statistical relationships between large scale atmospheric phenomena and local climate (temperature and precipitation) so that policy makers, for example, have more reliable tools for risk planning.

Generally, peoples’ experiences of climate change are two-fold: They experience the slow evolution of annual patterns in local weather, such as droughts becoming a way of life, winters that aren’t as cold, and summers that seem muggier than they used to be. Or they are caught by the seeming onslaught of fearsome extreme events such as freak heat waves in typically cool summer regions, two new seasons of extensive extremes – wildfire and hurricane – and massive flooding on the regional scale of states and nations. Mathematicians and statisticians are working with climate scientists to explore new approaches to understanding how the frequency, intensity, and global distribution of extreme events may be changing as a result of climate change. These mathematical scientists have much to contribute here, particularly in developing tools for quantifying and predicting rare and extreme events within a system that is non-stationary and for which there is a shortage of relevant data.

One major challenge in understanding the dynamics of extreme events is that the climate and weather systems under study are changing due to a warming earth. As such, the known geographic distribution of phenomena such as wind, temperature, humidity, and precipitation, that are based on historical data may not follow its current spatiotemporal statistical distributions in the future. By definition, extremes are the events that occur in the tails of these distributions. Yet there is evidence to support the conclusion that as the distributions change with global warming, phenomena thought to be extremes today may be less extreme, in the statistical sense, in the future, that is, more likely to occur in the future than in the past. Karen McKinnon (University of California, Los Angeles) noted that the upper tail of temperature distributions is getting longer than the lower tail; that is, the distribution is skewed toward the hot end. This suggests there will be more heat waves than unusually cold days in the future. In addition, she showed that globally, very humid areas are getting more humid, and dry areas are getting drier. Both of these observations have major implications for human health (impacts of heat waves coupled with high humidity) and so-called “fire weather” – extended hot, dry conditions that amplify the probability of wildfires.

Freddy Bouchet (Ecole Normale Superieure de Lyon) observed that heat waves cause more deaths than all other high temperature weather events combined over the period he examined. Given that these heat waves are relatively rare, he is developing prediction tools that are effective for small data sets. The impact of hot, humid weather on human health is a major area of scientific debate. Jane Baldwin (University of California, Irvine) and Matt Huber (Purdue University) each have research projects underway trying to reconcile differing opinions among physiologists and epidemiologists about how extremes in heat and humidity contribute to heat stroke and death. Physiologists argue that hot, moist conditions (high wet bulb temperature) increase human health risks, while epidemiologists observe that the data don’t back up this conclusion at the population level. Baldwin is using her tools as an atmospheric scientist to reconcile this disconnect. Huber takes as a given that there are thresholds in wet bulb temperature above which mammals’ (including humans) ability to function (dissipate metabolic heat from the core of the body) declines precipitously. These types of weather conditions will become more frequent and extreme in tropical and subtropical regions (that is, in many developing nations), with negative impacts on human health and regional economies due to declines in peoples’ ability to sustain outdoor labor (agriculture and construction, for example).

Kristi Ebi (University of Washington) is working to quantify excess deaths during heatwaves caused by the statistical extremity of the heat wave and anthropogenic climate change. Attributing excess heatwave deaths directly to climate change is difficult because of the complex set of factors that contribute to each death, such as individual vulnerability to heat & humidity, socioeconomic status and access to cooling, local baseline weather, and other factors. Her conclusion, after taking these factors into account, is that there will be statistically significant excess deaths during heat waves that are attributable to climate change.

Angel Hsu (University of North Carolina, Chapel Hill) has similar concerns as Ebi, but she is focusing on the urban heat island effect, recognizing that even within a relatively small geographic region like a city, different neighborhoods will experience extreme heat differently based on local conditions. One of the biggest predictors in urban heat islands is the relative absence of trees and other “greening.” Hsu showed that tree cover in cities correlates with per capita income, and that poor people and people of color are more susceptible to living in urban heat islands. She and her colleagues are developing tools using citizen science, available socioeconomic data, and mapping to measure and predict neighborhood-level urban temperatures. These tools can guide mitigation strategies such as targeted tree planting, thus illustrating that detection and attribution are important for using science to drive policy decisions and resource allocation in regions where potential impacts are large and resources may be limited.

Another major concern among earth scientists is the loss of Arctic sea ice and the ice sheets of Antarctica and Greenland. These climate change-driven phenomena will contribute significantly to catastrophic sea level rise, and in the case of reductions of Arctic sea ice, accelerate ocean warming which will also accelerate the melting of sea ice and the ice sheets. This positive feedback has scientists very concerned, primarily because of the fear of irreversible tipping points leading to coastal inundation faster than threatened regions can adapt.

Scientists’ work to understand the melting of sea ice and the ice sheets, which is complicated by our incomplete understanding of the physics of ice and of ice-air-ocean interactions. Model development and forecasting depend critically on reliable data, which, in the case of phenomena at continental scales in remote areas, like ice sheets, requires remote sensing via a constellation of earth-observing satellites. Fortunately, NASA and other agencies are launching missions that will augment or replace the instruments on the current Earth Observing System such as the NASA Surface Biology & Geology Mission, which will provide 30 meter resolution data at a broad spectrum of wavelengths providing information on, for example, snow, ice, and water cover, photosynthetic activity, and urbanization.

Scott Martin (University of Washington) discussed NASA’s upcoming SWOT (Surface Water and Ocean Topography) satellite that will provide data on ocean height, rivers, and surface water such as lakes. These data will improve modeling and prediction of many critical factors, including flooding and available water for agriculture and urban use. Importantly, with regard to sea ice and the ice shelves, SWOT will also help scientists understand the interactions between ocean currents and ice, and the extent to which those dynamics ultimately result in sea level rise. This last area of research is of particular interest to Momme Hall (Brown University) who is developing models to understand how large storms create ocean waves and the impact of those waves on sea ice. Similarly, Monica Martinez Wilhelmus (Brown University) is developing statistical descriptions of sea ice fields to provide a better understanding of the effect of large scale ocean eddies on sea ice. Scientific and policy advancements regarding climate change rely critically on the interconnectedness between NASA’s satellite measurements of the earth; climate scientists improving their physics-based models of sea ice, the ice sheets, and sea level rise; and statisticians who are providing innovative data synthesis and analysis tools for testing models and making predictions about our future climate.

There is also considerable attention being paid to the social cost of carbon, particularly resulting from flooding, changes in agriculture, and attributing economic damages to specific carbon emitters. Ian Bollinger (BlackRock) noted that 15% of the world’s population and 20% of capital assets are in coastal regions (which comprise 2% of global land area). In the absence of adaptation, coastal losses due to sea level rise and tropical cyclones (e.g., hurricanes) could increase by a factor of 100 by the year 2100, whereas there is a 10-fold capacity to reduce risk now via adaptation. BlackRock is developing high resolution (building-level) physical hazard models for coastal regions that also discount future economic value resulting from climate impacts.

Frances Davenport (Colorado State University) noted that floods are among the most common and destructive natural hazards, with an average of 82.7 million people impacted each year and \$34 billion in damages. Climate scientists are now able to quantify the attribution of these damages to climate change. For example, they estimate that up to \$26 billion of the $90 billion in damages from Hurricane Harvey are due to the influence of climate change in amplifying this extreme weather event. Adding to this body of work, Marshall Burke (Stanford University), is developing models for attributing the negative impacts on national economies to the carbon emissions from a different nation. He has developed a scientific framework for quantifying attribution, economic losses, and damage payments “owed” by emitting nations to those nations that have suffered losses. For example, he estimates that US carbon emissions have caused about \$1 trillion in damages to Brazil since 1980.

Andy Hultgren (University of Illinois Urbana Champaign) studies the social cost of carbon by estimating global caloric reductions resulting from climate-induced losses in agricultural productivity. Globally, the highest sources of calories are maize, soy, rice, wheat, cassava, and sorghum. Climate change will cause losses in yield from these crops (except rice) resulting in declines in global caloric availability. Hultgren estimates a daily global caloric loss of 130 kCal per person, per degree C increase in global mean surface temperature. Further, the richest regions in the world – the global breadbaskets – and the poorest regions that rely more on subsistence farming, will have the greatest losses in agricultural productivity and their ability to feed the world.

The partnership of economists, mathematicians, statisticians, social scientists, and climate scientists illuminates a much more complex picture of the dire consequences of the social cost of carbon. They are extending the predictions of future impacts on the earth’s natural systems to the lives of actual people and where they live, while also quantifying the costs of either doing nothing or investing now in mitigation and adaptation. Federal investments in this research, such as the collaborative opportunities provided by IMSI, are critical to advancing our understanding of the complex interconnectedness of Earth and human systems, anthropogenic impacts, and developing strategies for carbon reduction while also mitigating and adapting to the effects of our warming planet.

Note: The author thanks Dorit Hammerling and Bo Li for their editorial input on this article.

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Presenter: James Franke (University of Chicago)
Title: Unsupervised classification of full-disk geostationary satellite images for tropical cyclone analysis
Presenter: Takuya Kurihana (University of Chicago)
Title: Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder
Presenter: Elena Orlova (University of Chicago)
Collaborator(s): Haokun Liu, Raphael Rossellini, Rebecca Willett, Benjamin Cash
Title: Enhancing Subseasonal Climate Forecasting with Climate Model Ensembles and Machine Learning
Presenter: Ivan Sudakow (The Open University)
Title: Machine Learning-Based Emulators of Sea Ice Surface
Presenter: Claire Valva (Courant Institute of Mathematical Sciences)
Collaborator(s): Edwin P Gerber
Title: A data-driven analysis of the Quasi-Biennial Oscillation with Koopman modes
Presenter: Yinling Zhang (University of Wisconsin, Madison)
Collaborator(s): Nan Chen
Title: A Causality-Based Learning Approach for Underlying Dynamics of Complex Dynamical Systems

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Presenter: Matthew Bonas (University of Notre Dame)
Collaborator(s): Stefano Castruccio
Title: Calibration of Spatio-Temporal Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks
Presenter: Samuel F Gailliot (Texas A&M University, College Station)
Collaborator(s): Matthias Katzfuss and Trevor Harris
Title: Climate Change Detection and Attribution via Bayesian Transport Maps
Presenter: Charles Kulick (University of California, Santa Barbara (UCSB))
Collaborator(s): Sui Tang, Jinchao Feng, Mengyang Gu
Title: Scalable Model Selection of Particle Swarming Models with Gaussian Processes
Presenter: Eva Murphy (Clemson University)
Collaborator(s): Whitney Huang
Title: Modeling of wind speed and wind direction through a conditional approach

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Presenter: Matthew Bonas (University of Notre Dame)
Collaborator(s): Stefano Castruccio
Title: Calibration of Spatio-Temporal Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks
Presenter: Jian Cao (Texas A&M University, College Station)
Collaborator(s): Joseph Guinness Marc G. Genton Matthias Katzfuss
Title: Vecchia Gaussian-Process Regression and Variable Selection
Presenter: Moses Chan (Northwestern University)
Collaborator(s): Matthew Plumlee
Title: Applying Variational Inference on High-Dimensional Gaussian Process with Inducing Points
Presenter: Haoyuan Chen (Texas A&M University, College Station)
Collaborator(s): Dr. Liang Ding, Dr. Rui Tuo
Title: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
Presenter: Debangan Dey (NIH – National Institutes of Health)
Collaborator(s): Andrew Finley, Abhirup Datta, Sudipto Banerjee
Title: Graphical Nearest Neighbor Gaussian Process Models for Big Spatial Data
Presenter: Youssef Fahmy (Cornell University)
Collaborator(s): Joe Guinness
Title: Multivariate Matérn Vecchia Approximations and Optimization for Multivariate Matérn Models
Presenter: Haoxiang Feng (Michigan State University)
Collaborator(s): Nian Liu
Title: Computationally Efficient Estimators for Ornstein-Uhlenbeck Processes on Fixed Domains
Presenter: Christopher Geoga (Rutgers University)
Collaborator(s): Michael L. Stein
Title: A Scalable Method to Exploit Screening in Gaussian Process Models with Noise
Presenter: Whitney Huang (Clemson University)
Collaborator(s): Yu-Min Chung; Yu-Bo Wang; Jeff Mandel; Hau-Tieng Wu
Title: Predicting high frequency biomedical signal using synchrosqueezing transform and locally stationary Gaussian process regression
Presenter: Felix Jimenez (Texas A&M University, College Station)
Collaborator(s): Matthias Katzfuss
Title: Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
Presenter: Myeongjong Kang (Texas A&M University, College Station)
Collaborator(s): Matthias Katzfuss
Title: Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference
Presenter: Charles Kulick (University of California, Santa Barbara (UCSB))
Collaborator(s): Sui Tang, Jinchao Feng, Mengyang Gu
Title: Scalable Model Selection of Particle Swarming Models with Gaussian Processes
Presenter: Kaiyu Li (University College London)
Collaborator(s): Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol
Title: Multilevel Bayesian Quadrature
Presenter: Xubo Liu (University of California, Santa Barbara (UCSB))
Collaborator(s): Mengyang Gu
Title: Scalable marginalization of latent variables for correlated data
Presenter: Mary Salvana (University of Houston)
Collaborator(s): Mikyoung Jun
Title: Global 3D Bivariate Nonstationary Spatial Modeling of Argo Ocean Temperature and Salinity Profiles
Presenter: Annie Sauer (Virginia Polytechnic Institute & State University (Virginia Tech))
Collaborator(s): Robert B. Gramacy and David Higdon
Title: Active Learning for Deep Gaussian Process Surrogates
Presenter: Julia Walchessen (Carnegie-Mellon University)
Collaborator(s): Amanda Lenzi and Mikael Kuusela
Title: Learning Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihood Functions
Presenter: Stephen A Walsh (Virginia Polytechnic Institute & State University (Virginia Tech))
Collaborator(s): Dave Higdon, Annie Sauer, Marco A. R. Ferreira, Stephanie Zick
Title: A Deep Gaussian Process Framework to Quantify Uncertainty of Tropical Cyclone Precipitation Forecasts
Presenter: Lu Zhang (University of Southern California (USC) Medical School)
Title: Bayesian Predictive Stacking Under Spatial Process Settings
Presenter: Yingchao Zhou (Iowa State University)
Title: Can spatial data benefit from conformal prediction?

IMSI 2022-23 Flyers

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The American Physical Society (APS) awarded the 2022 Excellence in Physics Education Award to the TEAM-UP Task Force. Bo Hammer, the Executive Director of the Institute for Mathematical and Statistical Innovation (IMSI), was a leader in founding and directing TEAM-UP prior to his arrival at the University of Chicago. 

In 2018 and 2019, TEAM-UP conducted a study to examine the reasons for persistent underrepresentation of African American students graduating in physics and astronomy. The task force identified five factors responsible for success in physics and astronomy for African American students and provided corresponding recommendations to address each of the factors. The goal of TEAM-UP’s recommendations is to double the number of physics and astronomy bachelor’s degrees awarded to African American students by 2030.

Hammer was the Senior Director at the American Institute of Physics (AIP) and Staff Liaison to the AIP Liaison Committee on Underrepresented Minorities (LCURM). LCURM proposed that AIP fund a national task force to address the decline in African Americans earning bachelor’s degrees in physics and astronomy. Hammer, along with TEAM-UP Project Manager Arlene Modeste Knowles, assembled a team of 10 volunteers with the purpose of creating evidence-based recommendations for the physics and astronomy communities to increase African Americans earning degrees in these fields.

Since the 1950s, AIP has collected information on the number of people teaching and studying physics and astronomy. In the late-1990s and early-2000s, the number of bachelor’s degrees awarded in physics and astronomy was at an all-time low. AIP aimed to increase that number through a national initiative focused on educational, mentoring, and student culture in physics and astronomy departments at colleges and universities in the United States. As a result, degrees earned in this field doubled in about 15 years.

However, this was not the case for all students. While degrees earned were increasing, the fraction of African American students earning bachelor’s degrees in physics was unchanged and, in some cases, had decreased. In other departments across campus, African American students were earning bachelor’s degrees at a higher rate than the overall student body. The question was obvious: What was happening in physics? What was causing African American students to walk away from physics and succeed in different fields?

Physics and astronomy departments typically have small student bodies, which means that faculty should know each of their students, their education plan, and their future career goals. Beyond education, particularly given the small class size, faculty should know their students as individuals and help them successfully graduate. From the task force’s observations, Hammer summarized the situation, “African American students don’t need fixing; oftentimes it’s department culture that is broken, and so faculty need to own it.  And fix it.”

TEAM-UP, the National Task Force to Elevate African American representation in Undergraduate Physics and Astronomy, set out to quantify their observations. They conducted site visits of departments who were the top awarders of bachelor’s degrees to African American students. Of these departments, the majority were at Historically Black Colleges and Universities (HBCUs). They found that HBCUs were carrying a disproportionate burden in educating African American physics majors. In other cases, university departments would have one faculty member who strived to support diversity within their department’s student body. In these cases, TEAM-UP observed that when this faculty member would leave the department, many of the programs that they put into place would cease. 

For there to be any long-term change, Hammer explained that the effort must be integrated into the department as a whole. The “lone champion” model is fragile; the burden of change cannot be carried by one individual.

The goal of the TEAM-UP report is to at least double the number of bachelor’s degrees awarded annually to African American students, to at least 500 degrees in physics and 25 degrees in astronomy each year by the year 2030. Hammer notes that even at 500 per year, that’s less than one African American bachelor’s degree per degree-granting physics department in the US. The TEAM-UP report presents their evidence and details a roadmap for departments to self-evaluate their own culture and steps to change their culture with the goal of increasing bachelor’s degrees earned by African American students. Currently, there are 47 TEAM-UP university departments that have committed to supporting the task force’s recommendations to create a supportive environment in physics and astronomy. Realizing TEAM-UP’s goal will be a crucial step in equalizing representation of African Americans in physics and astronomy.