Past Events
Research and Opportunities in the Mathematical Sciences at Oak Ridge National Laboratory
Friday, Oct. 8, 2021, 1:25 p.m. through Friday, Oct. 8, 2021, 2:25 p.m.
Zoom
Juan Restrepo (Oregon State University)
I will present a general overview of Oak Ridge National Laboratory research in mathematics and computing. A brief description of my own initiatives and research will be covered as well. I will also describe opportunities for students, postdocs, and professional mathematicians.
Dr. Juan M. Restrepo is a Distinguished Member of the R&D Staff at Oak Ridge National Laboratory. Restrepo is a fellow of SIAM and APS. He holds professorships at U. Tennessee and Oregon State University. Prior to ORNL, he was a professor of mathematics at Oregon State University and at the University of Arizona. He has been a frequent IMA visitor.
His research focuses on data-driven methods for dynamics, statistical mechanics, transport in ocean and uncertainty quantification in climate science.
Scalable and Sample-Efficient Active Learning for Graph-Based Classification
Tuesday, Oct. 5, 2021, 1:25 p.m. through Tuesday, Oct. 5, 2021, 2:25 p.m.
Walter Library 402
Kevin Miller (University of California, Los Angeles)
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels; this is often reflected in a tradeoff between exploration and exploitation, similar to the reinforcement learning paradigm. I will talk about my recent work designing scalable and sample-efficient active learning methods for graph-based semi-supervised classifiers that naturally balance this exploration versus exploitation tradeoff. While most work in this field today focuses on active learning for fine-tuning neural networks, I will focus on the low-label rate case where deep learning methods are generally insufficient for producing meaningful classifiers.
Kevin Miller is a rising 5th year Ph.D. candidate in Applied Mathematics at the University of California, Los Angeles (UCLA), studying graph-based machine learning methods with Dr. Andrea Bertozzi. He is currently supported by the DOD’s National Defense Science and Engineering Graduate (NDSEG) Fellowship and was previously supported by the National Science Foundation's NRT MENTOR Fellowship. His undergraduate degree was in Applied and Computational Mathematics from Brigham Young University, Provo. His research focuses on active learning and uncertainty quantification in graph-based semi-supervised classification.
Long-term Time Series Forecasting and Data Generated by Complex Systems
Friday, Oct. 1, 2021, 1:25 p.m. through Friday, Oct. 1, 2021, 2:25 p.m.
Walter Library 402
Kaisa Taipale (CH Robinson)
Data science, machine learning, and artificial intelligence are all practices implemented by humans in the context of a complex and ever-changing world. This talk will focus on the challenges of long-term, seasonal, multicyclic time series forecasting in logistics. I will discuss algorithms and implementations including STL, TBATS, and Prophet, with additional attention to the data-generating processes in trucking and the US economy and the importance in algorithm selection of understanding these data-generating processes. Subject matter expertise must always inform mathematical exploration in industry and indeed leads to asking much more interesting mathematical questions.
Standardizing the Spectra of Count Data Matrices by Diagonal Scaling
Tuesday, Sept. 28, 2021, 1:25 p.m. through Tuesday, Sept. 28, 2021, 2:25 p.m.
Walter Library 402 or Zoom
Registration is required to access the Zoom webinar.
Boris Landa (Yale University)
A longstanding question when applying PCA is how to choose the number of principal components. Random matrix theory provides useful insights into this question by assuming a “signal+noise” model, where the goal is to estimate the rank of the underlying signal matrix. If the noise is homoskedastic, i.e. the noise variances are identical across all entries, the spectrum of the noise admits the celebrated Marchenko-Pastur (MP) law, providing a simple method for rank estimation. However, in many practical situations, such as in single-cell RNA sequencing (scRNA-seq), the noise is far from being homoskedastic. In this talk, focusing on a Poisson data model, I will present a simple procedure termed biwhitening, which enforces the MP law to hold by appropriately scaling the rows and columns of the data matrix. Aside from the Poisson distribution, this procedure is extended to families of distributions with a quadratic variance function. I will demonstrate this approach on both simulated and experimental data, showcasing accurate rank estimation in simulations and excellent fits to the MP law for real scRNA-seq datasets.
Boris Landa is a Gibbs Assistant Professor in the program for applied mathematics at Yale University. Previously, he completed his Ph.D. in applied mathematics at Tel Aviv University under the guidance of Prof. Yoel Shkolnisky. Boris's research is focused on theory and methods for processing large datasets corrupted by noise and deformations, with applications in the biological sciences.
Handling model uncertainties via informative Goodness-of-Fit
Tuesday, Sept. 21, 2021, 1:25 p.m. through Tuesday, Sept. 21, 2021, 2:25 p.m.
Walter Library 402
Sara Algeri (University of Minnesota, Twin Cities)
When searching for signals of new astrophysical phenomena, astrophysicists have to account for several sources of non-random uncertainties which can dramatically compromise the sensitivity of the experiment under study. Among these, model uncertainty arising from background mismodeling is particularly dangerous and can easily lead to highly misleading results. Specifically, overestimating the background distribution in the signal region increases the chances of falsely rejecting the hypothesis that the new source is present. Conversely, underestimating the background outside the signal region leads to an artificially enhanced sensitivity and a higher likelihood of claiming a false discovery. The aim of this work is to provide a self-contained framework to perform modeling, estimation, and inference under background mismodeling. The method proposed allows incorporating the (partial) scientific knowledge available on the background distribution, and provides a data-updated version of it in a purely nonparametric fashion, and thus, without requiring the specification of prior distributions. If a calibration (or control regions) is available, the solution discussed does not require the specification of a model for the signal, however when available, it allows to further improve the accuracy of the analysis and to detect additional and unexpected signal sources.
I have been an Assistant Professor in the School of Statistics at the University of Minnesota since August 2018. My appointment at UMN started soon after completing my doctoral studies in statistics at Imperial College London (UK). My research interests mainly lie in astrostatistics, computational statistics, and statistical inference. The main purpose of my work is to provide generalizable statistical solutions which directly address fundamental scientific questions, and can at the same time be easily applied to any other scientific problem following a similar statistical paradigm. In line with this, motivated by the problem of the detection of particle dark matter, my current research focuses on statistical inference for signal detection under lack of regularity. I am also interested in uncertainty quantification in the context of astrophysical discoveries.
SIAM Internship Panel
Friday, Sept. 17, 2021, 1:25 p.m. through Friday, Sept. 17, 2021, 2:25 p.m.
Zoom
Montie Avery (University of Minnesota, Twin Cities)
Come learn about the process of finding, interviewing, and getting jobs in industry! Panelists Brendan Cook, Jacob Hegna, Drisana Mosaphir, Cole Wyeth, and Amber Yuan will be here to answer all your questions about finding and participating in internships both before and during the pandemic.
PDE-inspired Methods for Graph-based Semi-supervised Learning
Tuesday, Sept. 14, 2021, 1:25 p.m. through Tuesday, Sept. 14, 2021, 2:25 p.m.
Walter Library 402
Jeff Calder (University of Minnesota, Twin Cities)
This talk will be an introduction to some recent research on PDE-inspired methods for graph-based learning, specifically for problems with very few labeled training examples. We'll discuss various models, including Laplace, p-Laplacian, re-weighted Laplacians, and Poisson learning, to highlight how connections between graph-PDEs and continuous PDEs can be used for analysis and development of new algorithms. The talk will be at an introductory level, suitable for graduate students.
Being Smart and Dumb: Building the Sports Analytics Industry
Friday, Sept. 10, 2021, 1:25 p.m. through Friday, Sept. 10, 2021, 2:25 p.m.
Zoom
Dean Oliver ( NBA's Washington Wizards)
Going from a scientific background into something that people haven't done comes with moments where you don't know what you're talking about... if you talk, that is. Admitting the times you don't know how your work can help and introducing your work when it may be able to help - that timing can be hard. I went from the field I was trained in - environmental engineering and consulting - to a job with no title at first. I had to write a book about how stats can help in basketball. Someone else invented the term "Sports Analytics". This talk is a little bit of that story.
Math-to-Industry Boot Camp VI
Monday, June 21, 2021, 8 a.m. through Friday, July 30, 2021, 5 p.m.
Online
Advisory: Application deadline is March 7, 2021
Organizers:
- Thomas Hoft, University of St. Thomas
- Daniel Spirn, University of Minnesota, Twin Cities
The Math-to-Industry Boot Camp is an intense six-week session designed to provide graduate students with training and experience that is valuable for employment outside of academia. The program is targeted at Ph.D. students in pure and applied mathematics. The boot camp consists of courses in the basics of programming, data analysis, and mathematical modeling. Students work in teams on projects and are provided with training in resume and interview preparation as well as teamwork.
There are two group projects during the session: a small-scale project designed to introduce the concept of solving open-ended problems and working in teams, and a "capstone project" that is posed by industrial scientists. Recent industrial sponsors included D-Wave Systems, Exxonmobil, Los Alamos National Laboratories, Milwaukee Brewers, Starbucks.
Weekly seminars by speakers from many industry sectors provide the students with opportunities to learn about a variety of possible future careers.
Eligibility
Applicants must be current graduate students in a Ph.D. program at a U.S. institution during the period of the boot camp.
Logistics
The program will take place online. Students will receive a $800 stipend.
Applications
To apply, please supply the following materials through the link at the top of the page:
- Statement of reason for participation, career goals, and relevant experience
- Unofficial transcript, evidence of good standing, and have full-time status
- Letter of support from advisor, director of graduate studies, or department chair
Selection criteria will be based on background and statement of interest, as well as geographic and institutional diversity. Women and minorities are especially encouraged to apply. Selected participants will be contacted in April.
Participants
Name | Department | Affiliation |
---|---|---|
Douglas Armstrong | Department of Data Science | Securian Financial |
Yuchen Cao | Department of Mathematics | University of Central Florida |
Samara Chamoun | Department of Mathematics | Michigan State University |
Ana Chavez Caliz | Department of Mathematics | Pennsylvania State University |
Alexander Estes | Institute for Mathematics and its Applications | University of Minnesota, Twin Cities |
Raymond Friend Jr | Department of Mathematics | Pennsylvania State University |
Ghodsieh Ghanbari | Department of Mathematics and Statistics | Mississippi State University |
Marc Haerkoenen | School of Mathematics | Georgia Institute of Technology |
Tony Haines | Department of Computational and Applied Mathematics | Old Dominion University |
Natalie Heer | CH Robinson | |
Thomas Hoft | Department of Mathematics | University of St. Thomas |
Alicia Johnson | Department of Mathematics, Statistics, and Computer Science | Macalester College |
Malick Kebe | Department of Mathematics | Howard University (Washington, DC, US) |
Juergen Kritschgau | Department of Mathematics | Iowa State University |
Marshall Lagani | Department of Data Science | Securian Financial |
Kevin Leder | Department of Industrial System and Engineering | University of Minnesota, Twin Cities |
Ivan Marin | Cargill, Inc. | |
Francisco Martinez Figueroa | Department of Mathematics | The Ohio State University |
Avishek Mukherjee | Department of Mathematical Sciences | University of Delaware (Newark, DE, US) |
Muharrem Otus | Department of Mathematics | University of Pittsburgh |
Smita Praharaj | Department of Mathematics | University of Missouri |
Tanmay Raj | Cargill, Inc. | |
Abba Ramadan | Department of Applied Mathematics | University of Kansas |
Samanwita Samal | Department of Mathematics | Indiana University |
Natalie Sheils | UnitedHealth Group | |
Blerta Shtylla | Pfizer | |
David Shuman | Department of Mathematics, Statistics and Computer Science | Macalester College |
Lauren Snider | Department of Mathematics | Texas A & M University |
Daniel Spirn | University of Minnesota | University of Minnesota, Twin Cities |
Elizabeth Sprangel | Department of Mathematics | Iowa State University |
Kaisa Taipale | Contractual Pricing Group | CH Robinson |
Sijie Tang | Department of Mathematics | University of Wyoming |
Cameron Thieme | Department of Mathematics | University of Minnesota, Twin Cities |
Shuxian Xu | Department of Mathematics | University of Pittsburgh |
Lei Yang | Department of Mathematics | Northeastern University |
Grace Zhang | School of Mathematics | University of Minnesota, Twin Cities |
Miao Zhang | Department of Mathematics | Louisiana State University |
Jennifer Zhu | Department of Mathematics | Texas A & M University |
Ahmed Zytoon | Department of Mathematics | University of Pittsburgh |
Projects and teams
Team 1 — Cargill: Hydrologic Energy Generation Optimization
- Mentor Ivan Marin, Cargill Corporation
- Mentor Tanmay Raj, Cargill Corporation
- Ana Chavez Caliz, Pennsylvania State University
- Francisco Martinez Figueroa, Ohio State University
- Juergen Kritschgau, Iowa State University
- Avishek Mukherjee, University of Delaware
- Smita Praharaj, University of Missouri
- Cameron Thieme, University of Minnesota
- Jennifer Zhu, Texas A & M University
The increased penetration of variable renewable energy (VRE) and phase-out of nuclear and other conventional electricity generation sources will require an additional flexibility in the power grid and a demand to lower the gap between the generation and demand, and how this can influence the energy pricing in the short and long term. Clean water is essential for hydropower generation, and the main source of electrical power generation in Brazil. Due to the limited water resources and the variability of precipitation, there is a need to investigate an optimal management of these resources in order to meet the power grid demand, and predict the power generation capacity, given the historical rain patterns, reservoir water levels and energy demands.
Team 2 — Securian Financial: Predicting Group Life Client Mortality During a Pandemic
- Mentor Douglas Armstrong, Securian Financial
- Yuchen Cao, University of Central Florida
- Samara Chamoun, Michigan State University
- Marc Haerkoenen, Georgia Institute of Technology
- Abba Ramadan, University of Kansas
- Lei Yang, Northeastern University
- Shuxian Xu, University of Pittsburgh
During a pandemic the ability to predict risk for clients becomes paramount to manage risk effectively. The impact that a pandemic has may differ depending on the demographics and regional considerations for each client. This brings in additional complexity to the analysis and forecasting of future risk a client may pose. In this project, students will enrich a simulated client dataset with publicly available data before developing a machine-learning based approach to predict adverse risk of multiple clients.
Team 3 — CH Robinson: Impact of Weather and Agricultural Events on Truckload Cost Per Mile
- Mentor Kaisa Taipale, CH Robinson
- Raymond Friend Jr, Pennsylvania State University
- Ghodsieh Ghanbari, Mississippi State University
- Tony Haines, Old Dominion University
- Malick Kebe, Howard University
- Elizabeth Sprangel, Iowa State University
- Grace Zhang, University of Minnesota
Fresh fruits and vegetables are an important group of commodities in the US commonly transported by truck from fields in predominantly southern growing regions across the US (for instance, from California to the Northeast). While irrigation dampens the effect of rainfall crop yields, temperature and rainfall are still important factors in the timing of fresh fruit and vegetable harvest and thus transport. This work will examine the magnitude of impact of vegetable harvest timing on transportation costs, using external inputs like temperature and rainfall as well as variables intrinsic to the truckload market. Challenges include combining the geographic characteristics of the time series involved: univariate time series methods provide some benefit but stronger results come from exploiting geography and freight characteristics. Bayesian models and causal impact analysis are natural tools for this application.
Team 4 — CH Robinson: CH Robinson Volume Simulation
- Mentor Natalie Heer, CH Robinson
- Mentor Bethany Stai, CH Robinson
- Mentor Michael Chmutov, CH Robinson
- Mentor Kaisa Taipale, CH Robinson
- Muharrem Otus, University of Pittsburgh
- Samanwita Samal, Indiana University
- Lauren Snider, Texas A & M University
- Sijie Tang, University of Wyoming
- Miao Zhang, Louisiana State University
- Ahmed Zytoon, University of Pittsburgh
In Economics there is classically an inverse relationship between the price of an item and the quantity of the item that customers will choose to purchase. If prices increase, customers will purchase fewer items, and if prices decrease customers will choose to purchase more items. If companies can predict the volume change associated with a change in price, they can optimize their pricing strategy for overall profitability max(Unit Price * Volume). The goal of this project is to help CHR be smarter in optimizing our business strategy.
Winter Math-to-Industry Boot Camp
Monday, Jan. 4, 2021, 8 a.m. through Friday, Jan. 15, 2021, 5 p.m.
Virtual
Advisory: Application deadline is Friday, December 4, 2020
2021 Winter Virtual Boot Camp poster
Organizers:
- Jasmine Foo, University of Minnesota, Twin Cities
- Thomas Hoft, University of St. Thomas
- Daniel Spirn, University of Minnesota, Twin Cities
The Winter Math-to-Industry Boot Camp is an intensive, two-week program that provides graduate students with training and experience that is valuable for employment outside of academia. The program is targeted at Ph.D. students in mathematics and statistics. The winter camp consists of pre-camp coursework in the basics of programming, data analysis, and optimization.
During the program, students work in small teams under the guidance of an industry mentor using a variety of streaming technology. The mentor and camp staff will help guide the students in the modeling process, analysis, and computational work associated with a real-world industrial problem. Additional time will be spent on developing professional and networking skills, meeting industry scientists, and participating in a career fair.
Each team will be expected to make a final presentation and submit a written report at the end of the workshop.
Recent industrial sponsors included Cargill, D-Wave Systems, the Mayo Clinic, Securian Financial, World Wide Technology.
Eligibility
Applicants must be current graduate students in a mathematical sciences Ph.D. program at a U.S. institution during the period of the boot camp.
Logistics
The program will take place online. Students will receive a $500 stipend.
Applications
To apply, please supply the following materials through the link at the top of the page:
- Statement of reason for participation, career goals, and relevant experience
- Unofficial transcript, evidence of good standing, and have full-time status
- Letter of support from advisor, director of graduate studies, or department chair
Selection criteria will be based on background and statement of interest, as well as geographic and institutional diversity. Women and minorities are especially encouraged to apply. Selected participants will be contacted in December.
Participants
Name | Department | Affiliation |
---|---|---|
Daniel Alhassan | Department of Mathematics and Statistics | Missouri University of Science and Technology |
Mohamed Imad Bakhira | Department of Mathematics | The University of Iowa |
Yiqing Cai | Gro Intelligence | |
Frankie Chan | Department of Mathematics | Purdue University |
Jorge Cisneros Paz | Department of Applied Mathematics | University of Washington |
Paula Dassbach | Medtronic | |
Jerry Dogbey-Gakpetor | Statistics | North Dakota State University |
Henry Fender | Department of Data Science | ITM TwentyFirst LLC |
Shihang Feng | Applied Mathematics and Plasma Physics | Los Alamos National Laboratory |
Jasmine Foo | School of Mathematics | University of Minnesota, Twin Cities |
Jonathan Hill | ITM TwentyFirst LLC | |
Thomas Hoft | Department of Mathematics | University of St. Thomas |
Salomea Jankovic | Department of Mathematics | University of Minnesota, Twin Cities |
Henry Kvinge | Pacific Northwest National Laboratory | |
Axel La Salle | School of Mathematical and Statistical Sciences | Arizona State University |
Youzuo Lin | Earth and Environmental Sciences Division | Los Alamos National Laboratory |
Sander Mack-Crane | Department of Mathematics | University of California, Berkeley |
Maia Powell | Department of Applied Mathematics | University of California, Merced |
Lee Przybylski | Mathematics | Iowa State University |
Priyanka Rao | Department of Mathematics & Statistics | Washington State University |
Majerle Reeves | Department of Applied Mathematics | University of California, Merced |
Daniel Spirn | University of Minnesota | University of Minnesota, Twin Cities |
Anna Srapionyan | Merrill Lynch | |
Wencel Valega Mackenzie | Department of Mathematics | University of Tennessee |
Christine Vaughan | Department of Mathematics and Mechanical Engineering | Iowa State University |
Elise Walker | Department of Mathematics | Texas A & M University |
Max Wimberley | Department of Mathematics | University of California, Berkeley |
Harrison Wong | Department of Mathematics | Purdue University |
Cancan Zhang | Department of Mathematics | Northeastern University |
Projects and teams
Project 1: Record Linkage: Synthesizing Expert Systems and Machine Learning
- Mentor Jonathan Hill, ITM TwentyFirst LLC
- Mentor Henry Fender, ITM TwentyFirst LLC
- Jorge Cisneros Paz, University of Washington
- Jerry Dogbey-Gakpetor, North Dakota State University
- Majerle Reeves, University of California, Merced
- Elise Walker, Texas A & M University
- Max Wimberley, University of California, Berkeley
- Harrison Wong, Purdue University
Record linkage is a common big data process where shared records in two large datasets are linked based on common fields. Longevity Holdings designed an expert system to automate record linkage between client data and a corpus of death records. This system produces scores that sort record pairs into matches and non-matches. Currently, high and low scores separate cleanly, but mid-tier scores must be manually reviewed. This led us to ask: Can machine learning improve an expert system in record linkage and reduce the size of this review set?
We are working with a variant of the Expectation Maximization (EM) algorithm following the Fellegi-Sunter approach to record linkage. We implemented this algorithm but have not found an optimal configuration for our data. The algorithm is general so we can manipulate many aspects of the input. Our priority is to determine whether there is a configuration that can improve the expert system.
EM is not the only viable approach to this problem. There are a wide range of existing methods that can be applied to record linkage. Our priority is to figure out the pros and cons for each, while trying to exceed EM and expert system performance.
On this project, you will work with real-world data and learn to organize as a team. You will deliver a whitepaper summarizing your process and results. We are most interested in your clear thinking and structured approach to this problem. We will divide into two groups focusing on one of the priorities above. Both groups will receive two validated sets of record pairs, one deriving from obituaries and the other from state and federal records. Our toolset will include python, pandas, and scikit-learn.
Project 2: Data-Driven Computational Seismic Inversion
- Mentor Youzuo Lin, Los Alamos National Laboratory
- Mentor Shihang Feng, Los Alamos National Laboratory
- Frankie Chan, Purdue University
- Salomea Jankovic, University of Minnesota, Twin Cities
- Sander Mack-Crane, University of California, Berkeley
- Priyanka Rao, Washington State University
- Christine Vaughan, Iowa State University
- Cancan Zhang, Northeastern University
Computational seismic inversion turns geophysical data into actionable information. The technique has been widely used in geophysical exploration to characterize the subsurface structure. Such a clear and accurate map of the subsurface is crucial for determining the location and size of reservoirs and mineral features.
Seismic inversion usually presents itself as an inverse problem. However, solving those inverse problems has been notoriously challenging due to their ill-posed and computationally expensive nature. On the other hand, with advances in machine learning and computing, and the availability of more and better data, there has been notable progress in solving such problems. In our recent work [1, 2], we developed end-to-end data-driven subsurface imaging techniques and produced encouraging results when test data and training data share similar statistics characteristics. The high accuracy of the predictive model is built on the assumption that the training dataset captures the distribution of the target dataset. Therefore, it is critical to obtain a sufficient amount of high-quality training set.
In this project, students will work with LANL scientists to study the impact of the training data on the resulting predictive model. In particular, students will explore and develop different techniques to generate high-quality synthetic data that could be used to enhance the training data quality. Through the project, students will have the opportunity to learn deep learning and its applications in computational imaging and the fundamentals of ill-posed inverse problems.
Reference:
[1]. Yue Wu and Youzuo Lin, “InversionNet: An Efficient and Accurate Data-driven Full Waveform Inversion,” IEEE Transactions on Computational Imaging, 6(1):419-433, 2019.
[2]. Zhongping Zhang and Youzuo Lin, “Data-driven Seismic Waveform Inversion: A Study on the Robustness and Generalization,” in IEEE Transactions on Geoscience and Remote Sensing, 58(10):6900-6913, 2020.
Project 3: The Impact of Climate Change on Crop Yield
- Mentor Yiqing Cai, Gro Intelligence
- Daniel Alhassan, Missouri University of Science and Technology
- Mohamed Imad Bakhira, The University of Iowa
- Axel La Salle, Arizona State University
- Maia Powell, University of California, Merced
- Lee Przybylski, Iowa State University
- Wencel Valega Mackenzie, University of Tennessee
Gro is a data platform with comprehensive data sources related to food and agriculture. With data from Gro, stakeholders can make quicker and better decisions. In this project, the students will use data from Gro to quantify the impact of climate change on crop yield, and create visualizations to demonstrate their findings. For example, they can use long-term climate data from Gro, to predict corn yield in Minnesota, 100 years from now. Based on the results, they might be able to conclude that Minnesota will no longer be suitable for growing corn in 100 years, or the areas suitable for corn will shift from the south to the north within Minnesota. Furthermore, they can scale the analysis to the whole globe, and create cool visualizations to show the results.
Data will be provided through Gro API (Python client). For data discovery and visualizations, the students can interact with the Gro web app directly. Once they decide what data to pull from Gro, they can export a code snippet and use the API client to download the data. Data pulled from Gro are in the format of time series, which are called data series. A data series is made up of data points, each with a start and end timestamp. Different data series can come from different sources, and have different frequencies. For example, there are projected monthly precipitation and air temperature from the GFDL B1 model all the way to year 2100, that are available across the whole world.
The deliverables of this project are two-fold: a Jupyter notebook (hosted on Infrastructure provided by Gro) and a visual presentation of the results. It can even be the combination of the two. The Jupyter notebook should be executable end-to-end, from fetching the data from Gro API, to export predictions as files, or as visualizations.