Morteza Karimzadeh

Morteza Karimzadeh on 9News: Facebook data helps ��Ƶ researchers predict COVID surges

Anonymous — Mon, 11 Apr 2022 19:18:13 +0000

Morteza Karimzadeh on 9News: Facebook data helps ��Ƶ researchers predict COVID surges Anonymous (not verified) Mon, 04/11/2022 - 13:18

Professor Morteza Karimzadeh said the data helps him and his team measure people's movements and connectivity between locations.

Researchers at the University of Colorado are using freely available data sets from Facebook to forecast COVID cases and hospitalizations for counties and states across the country. Assistant Professor of Geography Morteza Karimzadeh said the data helps him and his team measure people's movements and connectivity between locations.

"These are anonymized aggregate data sets," Karimzadeh said. "Facebook, for instance -- Meta, as it’s called now -- knows how many users it has in each of these counties. It also knows how many of these users are friends with each other on social media. So you can look at this data set and see what proportion of users in these two counties, Denver and Boulder counties, are friends on Facebook. That kind of gives you a measure of how strong the social ties are between these two different counties."

[video:https://vimeo.com/698347200]

"We can capture the relationship between counties and states better based on social media friendships. That is just a little mind-blowing to me," Karimzadeh said. "For instance, how connected is Boulder County to Jefferson County and then to Denver County? So if you see a surge in Boulder County or in Denver County, how much of that spills over to Jefferson County?"

Their predictions, like any other modeling, have a level of uncertainty and probability.

"We are not fortune tellers," he said with a laugh. "That is why we submit a range for each county and state."

But their estimations can also be really helpful, warning health officials of a potential surge.

“Initially, I was skeptical that a social media data set would help us improve our results, but to my surprise, it actually helps," Karimzadeh said.

They are one of many teams across the country that submit their forecasts to be collected into a central repository at the University of Massachusetts Amherst. The repository is called the COVID-19 Forecast Hub. Agencies like the CDC then use the information from the repository to notify people about virus trends.

"We can rely on these forecasts and do preventative intervention so the surge doesn't actually happen," Karimzadeh said. "We could essentially put the brakes on the virus before it had a chance to take a toll."

He said the Facebook data sets complement other data they're using to make these predictions, like hospitalizations and deaths.

While they don't know how long COVID will be with us, they'll continue to harness the power of social media.

"It's one of the biggest data sets that's out there that we can use, and as long as it's out there we will try to tap it as long as it helps with the modeling efforts," Karimzadeh said.

He said they haven't started using social media data for predicting other diseases yet. But Karimzadeh believes it has potential for forecasting things like the flu.

“If there are warnings of potential surges in the region and health officials advise us to change our behavior for a little while, I think it would be good if we all heeded that advice," he said. “And if it doesn’t happen, then that’s good news. It’s not that the health officials were wrong. It’s just that we prevented that."

See the original original web article on 9news.com.

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How AI, social media data could help predict the next COVID surge

Anonymous — Tue, 22 Mar 2022 14:49:13 +0000

How AI, social media data could help predict the next COVID surge Anonymous (not verified) Tue, 03/22/2022 - 08:49

Lisa Marshall

In the summer of 2021, as the third wave of the COVID-19 pandemic wore on in the United States, infectious disease forecasters began to call attention to a disturbing trend.

The previous January, as models warned that U.S. infections would continue to rise, cases plummeted instead. In July, as forecasts predicted infections would flatten, the Delta variant soared, leaving public health agencies scrambling to reinstate mask mandates and social distancing measures.

“Existing forecast models generally did not predict the big surges and peaks,” said geospatial data scientist Morteza Karimzadeh, an assistant professor of geography at ��Ƶ Boulder. “They failed when we needed them most.”

New research from Karimzadeh and his colleagues suggests a new approach, using artificial intelligence and vast, anonymized datasets from Facebook could not only yield more accurate COVID-19 forecasts, but also revolutionize the way we track other infectious diseases, including the flu.

Their findings, published in the International Journal of Data Science and Analytics, conclude this short-term forecasting method significantly outperforms conventional models for projecting COVID trends at the county level.

Karimzadeh’s team is now one of about a dozen, including those from Columbia University and the Massachusetts Institute of Technology (MIT), submitting weekly projections to the COVID-19 Forecast Hub, a repository that aggregates the best data possible to create an “ensemble forecast” for the Centers for Disease Control. Their forecasts generally rank in the top two for accuracy each week.

“When it comes to forecasting at the county level, we are finding that our models perform, hands-down, better than most models out there,” Karimzadeh said.

Analyzing friendships to predict viral spread

Most COVID-forecasting techniques in use today hinge on what is known as a “compartmental model.” Simply put, modelers take the latest numbers they can get about infected and susceptible populations (based on weekly reports of infections, hospitalizations, deaths and vaccinations), plug them into a mathematical model and crunch the numbers to predict what happens next.

AI has revolutionized everything, from the way we interact with our phones to the development of autonomous vehicles, but we really have not taken advantage of it all that much when it comes to disease forecasting.” -- Morteza Karimzadeh

These methods have been used for decades with reasonable success but they have fallen short when predicting local COVID surges, in part because they can’t easily take into account how people move around.

That’s where Facebook data comes in.

Karimzadeh’s team draws from data generated by Facebook and derived from mobile devices to get a sense of how much people travel from county to county and to what degree people in different counties are friends on social media. That matters because people behave differently around friends.

“People may mask up and social distance when they go to work or shop, but they may not adhere to social distancing or masking when spending time with friends,” Karimzadeh said.

All this could influence how much, for instance, an outbreak in Denver County might spread to Boulder County. Often, counties that are not next to each other can heavily influence each other.

In a previous paper in Nature Communications, the team found that social media data was a better tool for predicting viral spread than simply monitoring people’s movement via their cell phones. With 2 billion Facebook users worldwide, there is abundant data to draw from, even in remote regions of the world where cell phone data is not available.

Notably, the data is privacy-protected, stressed Karimzadeh.

“We are not individually tracking anyone.”

The promise of AI

The model itself is also novel, in that it builds on established machine-learning techniques to improve itself in real-time, capturing shifting trends in the numbers that reflect things like new lockdowns, waning immunity or masking policies.

Over a four-week forecast horizon, the model was on average 50 cases per county more accurate than the ensemble forecast from the COViD-19 Forecast Hub.

“The model learns from past circumstances to forecast the future and it is constantly improving itself,” he said.

Thoai Ngo, vice president of social and behavioral science research for the nonprofit Population Council, which helped fund the research, said accurate forecasting is critical to engender public trust, assure that communities have enough tests and hospital beds for surges, and enable policy makers to implement things like mask mandates before it’s too late.“The world has been playing catch-up with COVID-19. We are always 10 steps behind,” Ngo said.

Ngo said that traditional models undoubtedly have their strengths, but, in the future, he’d like to see them combined with newer AI methods to reap the unique benefits of both.

He and Karimzadeh are now applying their novel forecast techniques to predicting hospitalization rates, which they say will be more useful to watch as the virus becomes endemic.

“AI has revolutionized everything, from the way we interact with our phones to the development of autonomous vehicles, but we really have not taken advantage of it all that much when it comes to disease forecasting,” said Karimzadeh. “There is a lot of untapped potential there.”

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Behzad Vahedi: Outstanding Student Presentation Award

Anonymous — Wed, 23 Feb 2022 21:51:32 +0000

Behzad Vahedi: Outstanding Student Presentation Award Anonymous (not verified) Wed, 02/23/2022 - 14:51

Behzad Vahedi received the Outstanding Student Presentation Award (OSPA) from the American Geophysical Union (AGU) for his presentation at the 2021 Fall Meeting. This award is for the presentation titled "A Comparison Of Classic Deep Learning Architectures For Sea Ice Classification From SAR". Behzad's advisor Morteza Karimzadeh, post doc Dr. Benjamin Lucas, and collaborators in the National Snow and Ice Data Center (NSIDC) and ��Ƶ Denver were Behzad's co-authors on this presentation. According to AGU, "This honor is awarded for only the most exceptional presentations during AGU Fall Meeting 2021."

Presentation Abstract

During the last decade, advances in the state-of-the-art deep learning models, in particular convolutional neural networks, have facilitated significant improvements in image recognition tasks. In fact, on the benchmark ImageNet dataset, the state of the art is now recognized as performing better than human. As a result, many adjacent tasks, including image recognition in remote sensing, have adopted these state-of-the-art models with little investigation into their transferability. For instance, the common image datasets—from which pre-trained model weights are derived or modern architectures are evaluated on—contain R-G-B images of everyday items such as animals, symbols, and vehicles. Needless to say, this is very different from the contents of a standard optical or radar image acquired by a satellite.

In this work we explore this idea of transferability in the context of the classification of sea ice type from Sentinel-1 SAR imagery. We compare the performance of a basic CNN with 4 significant models from the field of computer vision—AlexNet, ResNet, VGG, and Inception—in the task of classifying sea ice in a region of the Chukchi Sea, a sea of the Arctic Ocean. We extend these experiments further to compare models that have been pre-trained on the ImageNet dataset with models where the parameters are randomly initialized, to demonstrate whether pre-trained models are beneficial for this application. The performance of models is compared using overall accuracy and F-1 score. Finally, we hypothesize why some models perform better on our dataset than the others, and we conclude by explaining how the results inform our model choice for future sea ice classification projects.

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Faculty News Spring 2021

Anonymous — Mon, 03 May 2021 23:20:08 +0000

Faculty News Spring 2021 Anonymous (not verified) Mon, 05/03/2021 - 17:20

Waleed Abdalati testified to Congress for the House Select Committee on the Climate Crisis.

Waleed Abdalati

On April 15, 2021, Professor Abdalati testified in a hearing held by the U.S. House of Representatives Select Committee on the Climate Crisis. The hearing was titled “Making the Case for Climate Action: The Growing Risks and Costs of Inaction.”

While Dr. Abdalati spoke to the underlying physical mechanisms of climate change, their current expressions, and future implications, other witnesses included the city manager for Tybee Island in Coastal Georgia, who talked about the challenges they face in confronting sea level rise; a lawyer from Mississippi, who spoke to the ethnic and racial inequities associated with climate change; and an economist, who spoke to the social costs of climate change.

Dr. Abdalati remarked, "hearing these different perspectives on climate change really drove home the geographic dimensions of climate change in a very powerful way, as well as the critical role that geography serves in meeting these challenges.”

Also see information about the hearing and Dr. Abdalati's testimony

Holly Barnard

Holly Barnard Awarded Boulder Faculty Excellence in Leadership & Service Award for 2020-2021

Chosen from amongst many excellent nominees, Professor Holly Barnard was selected as a recipient of the BFA Faculty Excellence in Leadership and Service Award this year.

She was jointly nominated by the Department of Geography and the Institute of Arctic and Alpine Research (INSTAAR) for her leadership in advancing justice, equity, diversity and inclusion in both units, across the ��Ƶ campus, and beyond. In addition, Professor Barnard has also taken on numerous leadership roles in the discipline, through the American Geophysical Union and through her past service as Lead Program Manager for the Hydrological Sciences Program at NSF.

Additionally, Dr. Barnard was also recently honored for her efforts to promote diversity by the Arts & Sciences Council of the College of A&S.

Emily Yeh Winner of the 2020-2021 Outstanding Faculty Mentor Award

Emily Yeh

Dr. Yeh was chosen as a winner of the Graduate School's 2020-2021 Outstanding Faculty Mentor Award. Her nomination dossier was full of praise for providing help and encouragement to her advisees. She is well known for giving much of her time, energy, and intellect to support graduate students and the mission of graduate education.

Yaffa Truelove

Yaffa Truelove Wins Best Urban Studies Article 2020

The Urban Studies Best Article is awarded by the editors to the authors of the most innovative and agenda-setting article published in a given year. Eleven articles were shortlisted by the editors from those published in print copy in 2020. Urban Studies Journal is an international journal for research in urban and regional studies.

Dr. Truelove's article, Disambiguating the southern urban critique: Propositions, pathways and possibilities for a more global urban studies, has been voted by the editors as the Urban Studies Best Article for 2020. In agreement with the publisher, SAGE, they will be allowing Open Access to the article on the journal’s website.

Katherine Lininger

Professor Katherine Lininger was part of a team given a ��Ƶ Outreach Award for the outreach proposal, “��Ƶ Restoration Ecology Experimental Learning Program”, by the ��Ƶ Boulder Outreach Awards Committee. The effort was led by Tim Seastedt, professor emeritus of INSTAAR and EBIO, and also included Professor Sharon Collinge from the Department of Environmental Studies. The project builds on three years of work, partnering with the non-profit Wildlands Restoration Volunteers (WRV). WRV uses service-learning and education programs to help local youth build relationships with nature. Over 70% of the youth engaged in the program come from low-income, minority, or at risk populations. As a result, underserved youth will have access to the many benefits of a connection to nature and will be able to explore potential careers in environmental sciences and see their own capacity to make a positive impact.

Their team was selected from amongst a very competitive pool of 40 applications.

Morteza Karimzadeh

Morteza Karimzadeh awarded RIO Seed Grant

They will use foot-traffic data to identify business patrons’ residence neighborhoods and the level of recovery in consumption by residents living in those neighborhoods as characterized by different sociodemographic, political and economic conditions.

The 2021 Research & Innovation Seed Grant program resulted in 16 new grants with approximately $700,000 being awarded to ��Ƶ Boulder faculty across disciplines, with each grant providing up to $50,000 in funding.

Heide Bruckner

MeatOut Day: Heide Bruckner Interviewed on Colorado Public Radio

In February, word started to get out that Gov. Jared Polis has proclaimed the 20th as MeatOut Day. The day, which was started in 1985 by the Farm Animals Rights Movement, is meant to encourage non-vegetarians to consider moving toward a plant-based diet.

Heide Bruckner's research involves the intersections of food and identity. “Meat has always been politicized and meat-eating tied to a lot of perceptions of American identity and masculinity, especially here in the American West,” she said.

Dr. Bruckner favors the idea of a MeatOut Day, as a way to introduce people to the idea that they can reduce the amount of meat in their diet without becoming a full vegetarian.

“There is a large area in between that all-or-nothing approach that we really should explore,” she said. “Realistically, one day isn’t going to radically shift perception, change behaviors or reduce meat consumption. But I do believe it can provide an opening for some to consider the role that meat plays in their diet.”

The governor’s office notes that Polis issues numerous proclamations each year celebrating the state’s agricultural sector, including Colorado Ag Day, Farm Bureau Day, and Rocky Ford Cantaloupe Day. But that has not stopped the criticism. After word of the proclamation got out, several state Senators spoke out against MeatOut Day on the chamber floor.

John O'Loughlin: Ukrainian Resident’s Divided Views on the Donbas Conflict

John O’Loughlin

Two new articles from The Washington Post and Global Voices both feature research conducted by Professor John O’Loughlin of Geography and IBS's Program on International Development and his colleagues.

On February 12, 2015 the Minsk II accords left the Donbas region territorially divided with the Ukrainian government controlling the western side and two separatist entities controlling the eastern side. O’Loughlin and his research team conducted a survey on both sides and they found drastically different opinions regarding the war in Donbas.

On the eastern side, they found trust in the authorities to be higher among residents; however, on the western side, they found trust in the authorities to be much lower among residents. Their survey also shows that residents disagree on the future status of Donbas as an autonomous region.

The team also made a research presentation on the results at George Washington University’s Institute for European, Eurasian and Russian Studies in February; the video of the talk is available on Youtube.

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Morteza Karimzadeh awarded RIO Seed Grant

Anonymous — Tue, 30 Mar 2021 00:01:04 +0000

Morteza Karimzadeh awarded RIO Seed Grant Anonymous (not verified) Mon, 03/29/2021 - 18:01

Morteza Karimzadeh

Morteza Karimzadeh and Terra McKinnish were awarded the ��Ƶ RIO Seed Grant for the project entitled “Recovering from a Pandemic: Unraveling Neighborhood Geographic Disparities in Consumer and Business Behavior in 2021”. They will use foot-traffic data to identify business patrons’ residence neighborhoods and the level of recovery in consumption by residents living in those neighborhoods as characterized by different sociodemographic, political and economic conditions.

Grant Reviewers' Comments

"The combination of spatial data with consumption and foot-traffic data will be an important contribution. This study is well-poised to ask a lot of other questions, perhaps larger in their scope. Regional analysis might uncover cultural and political insights regarding human behavior as we deal with the pandemic. This is such a great proposal, keep an eye on the bigger questions that can be answered by understanding how we have or haven't responded to the pandemic."
"This is an excellent proposal on a timely and pressing question - the effects of covid-19 on neighborhoods. The project seeks to empirically tackle the disproportionate impact of the pandemic on different neighborhoods. It's original, innovative and empirically sound."
"Excellent project design with innovative methods, situated in the literature, and addressing a significant gap in the scholarship with broader societal consequences."

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Scientists aim to fuse Earth data to help classify, map sea ice

Anonymous — Thu, 17 Dec 2020 07:00:00 +0000

Scientists aim to fuse Earth data to help classify, map sea ice Anonymous (not verified) Thu, 12/17/2020 - 00:00

Colorado Arts and Sciences Magazine

Earth-orbiting satellites and other instruments collect huge amounts of data, each providing a different lens through which scientists can map the environment. Some instruments measure reflections of visible light or radar waves, while others measure elevation.

These diverse observations need to be harmonized and combined for studying the Earth’s surface. But the sheer size and differences in the data pose a challenge to this synchronization—one that researchers at the University of Colorado hope to tackle with the help of artificial intelligence (or AI).

The researchers’ project, called “Harmonized Earth,” is led by Morteza Karimzadeh, assistant professor of geography at ��Ƶ Boulder, and his collaborators at the National Ice and Snow Data Center (NSIDC) and ��Ƶ Denver’s Department of Computer Science.

Morteza Karimzadeh

Harmonized Earth recently won a three-year, $1.2 million grant from the National Science Foundation to create algorithms and cyber-infrastructure for harmonizing heterogeneous big-data products including satellite imagery and in situ, or on site, observations in a cloud computing environment.

The technologies developed by the Harmonized Earth project are expected to be useful to a variety of applications including for climate change research as sea ice is a key indicator. For this NSF-supported effort, however, the focus will be on creating high-resolution maps of sea ice extent and thickness, a challenging scientific task, according to the researchers, due to the elusive and constantly changing nature of sea ice.

With the increasing availability of high-resolution remote sensing products such as synthetic-aperture radar and lidar technology, there is a renewed desire for tackling this challenge, scientists note.

However, bridging data science and geoscience together is key in successfully harnessing these large heterogeneous data for sea-ice mapping, Karimzadeh said, adding:

“This project brings geospatial data scientists, geoscientists, and computer scientists to tackle this challenge using the state-of-the-art in machine learning and earth observations for seamless data fusion, machine learning and analysis.”

Karimzadeh noted that one challenge to using AI to fuse data for sea-ice mapping is that AI relies on data labels created by humans.

“Our goal is to create efficient infrastructure for minimal human effort in labeling these huge datasets across different types,” Karimzadeh said, adding: “The algorithms then ‘learn’ from these labels in an interactive manner.”

Walt Meier, senior research scientist at NSIDC and a member of the research team, concurred, adding that another area where Harmonizing Earth could provide benefit is the creation of ice charts for human activities in and near sea-ice regions. Now, that effort is largely done manually, “experts looking at images and interpreting where there is ice,” and because it is manual, it is difficult to analyze much data from synthetic-aperture radar, he said.

“Our methods will help automate much of this process, which will allow much more data to be analyzed for the ice charts and improve those charts,” Meier added.

NSIDC scientist Andrew Barrett commented that machine-learning methods such as deep neural networks could help scientists map larger areas at high resolution.

“Ice analysts can then focus on checking, correcting and packaging ice maps for users in a similar way to how meteorologists use output from numerical weather prediction models along with other sources of information to produce weather forecasts,” Barret said.

On a broader scale, Meier said, better interpretation of different kinds of data “will help us better understand how sea ice changes seasonally and over the years to give us better sense of how the ice cover is changing and improve models to understand how it will change in the future.”

Harmonized Earth will be integrated into NSF’s EarthCube software ecosystem for maximum adoption and ease of use, to make federally funded data more useful to researchers, including those who make sea-ice projections used by the shipping industry.

Our methods will help automate much of this process, which will allow much more data to be analyzed for the ice charts and improve those charts."

To that end, Karimzadeh noted that the project also aims to produce “computational notebooks” for students with little or no background in data-science methods in geoscience.

EarthCube is a growing community of scientists from the geosciences, as well as geoinformatics researchers and data scientists. It began as a joint effort between the NSF Directorate for Geosciences and the Division of Advanced Cyberinfrastructure and has attracted an evolving, dynamic virtual community of more than 2,500 contributors, including Earth, ocean, polar, planetary, atmospheric, geospace, computer and social scientists, educators and data and information professionals.

Through this community-driven development, many successful open-source projects have been made available to researchers and practitioners, and Harmonized Earth will add earth data fusion and sea ice mapping to the capabilities of EarthCube.

Harmonized Earth is a collaboration between Principal Investigator Karimzadeh, Andrew Barrett, Walt Meir and Siri Jodha Khalsa of NSIDC; and Farnoush Banaei-Kashani of ��Ƶ Denver Computer Science.

The project, “Data Capabilities: Enabling Analysis of Heterogeneous, Multi-source Cryospheric Data,” is described in Award #2026962 and Award #2026865.

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NSF award will support Karimzadeh and team in fusing heterogeneous large earth data for sea ice mapping

Anonymous — Fri, 11 Dec 2020 01:46:53 +0000

NSF award will support Karimzadeh and team in fusing heterogeneous large earth data for sea ice mapping Anonymous (not verified) Thu, 12/10/2020 - 18:46

Morteza Karimzadeh

Dr. Morteza Karimzadeh, assistant professor of Geography at ��Ƶ Boulder and his collaborators at the National Snow and Ice Data Center (NSIDC) and ��Ƶ Denver’s Department of Computer Science were recently awarded a three-year $1.2M grant by the National Science Foundation¹ to support their project ‘Harmonized Earth’. Harmonized Earth is a collaborative research and capabilities development effort for creating algorithms, models, software systems, and cyber-infrastructure for harmonizing heterogeneous big data products (including satellite imagery and in situ observations) in a cloud environment for various downstream tasks. The technologies developed are expected to be extendable to a variety of applications, but for this project, the focus will be on classification and mapping of sea ice.

Dr. Morteza Karimzadeh

Sea ice is an important component of the climate system and a key indicator of climate change. Sea ice is spatiotemporally dynamic, exhibiting a variety of evolving ice types that need classification for scientific analysis or operational planning. The mapping of sea ice at high spatial and temporal resolutions remains a scientific challenge. With the increasing availability of high-resolution remote sensing products such as synthetic-aperture radar and lidar, there is a renewed desire for tackling this challenge. However, bridging data science and geoscience is key in successfully harnessing these large heterogeneous data for sea ice mapping. This project brings geospatial data scientists, geoscientists, and computer scientists together to tackle this challenge using the state-of-the-art in machine learning and earth observations for seamless data fusion, machine learning and analysis. Harmonized Earth will be integrated into the interoperable environment of NSF EarthCube for maximum adoption and ease of use.

Earthcube is a growing community of scientists across all geoscience domains, as well as geoinformatics researchers and data scientists. EarthCube started as a joint effort between the NSF Directorate for Geosciences and the Division of Advanced Cyberinfrastructure, and has attracted an evolving, dynamic virtual community of more than 2,500 contributors, including earth, ocean, polar, planetary, atmospheric, geospace, computer and social scientists, educators, and data and information professionals. Through this community-driven development, many successful open-source projects have been made available to researchers and practitioners alike, and Harmonized Earth will add earth data fusion and sea ice mapping to the capabilities of EarthCube.

Harmonized Earth is a collaboration between the Principal Investigator Dr. Morteza Karimzadeh (��Ƶ Boulder Geography), Andrew Barrett (NSIDC), Walt Meir (NSIDC), Siri Jodha Khalsa (NSIDC) and Farnoush Banaei-Kashani (��Ƶ Denver Computer Science).

¹Data Capabilities: Enabling Analysis of Heterogeneous, Multi-source Cryospheric Data, Award #2026962 and Award #2026865

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2019 Fall Newsletter Published

Anonymous — Wed, 11 Dec 2019 23:30:51 +0000

2019 Fall Newsletter Published Anonymous (not verified) Wed, 12/11/2019 - 16:30

The 2019 Fall Newsletter has been published and is available for viewing. The newsletter is packed with department news, alumni updates, and articles by faculty and students.

Contents:

Message from the Department Chair, pg 2
Editors' Comments, pg 3
Mark Serreze Named Distinguished Professor, pgs 4-5
The Boulder Affordable Housing Research Initiative, pgs 6-7
Introducing Morteza Karimzadeh, Assistant Professor, pgs 8-9
Introducing Heide Bruckner, Instructor, pgs 9-10
Emily Yeh Sabbatical Notes: Post-disaster trajectories in mountainous Chinese village, pgs 11-12
Alumni Updates, pg 13
Department News, pg 14
Donor Support, pgs 15-16

All previous newsletters are on our Newsletters page.

For a more enjoyable reading experience, open the newsletter file and adjust your browser window to the same size as the newsletter page. The Table of Contents and other links are active within the document.

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Morteza Karimzadeh

Morteza Karimzadeh on 9News: Facebook data helps ��������Ƶ researchers predict COVID surges

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How AI, social media data could help predict the next COVID surge

Analyzing friendships to predict viral spread

The promise of AI

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Behzad Vahedi: Outstanding Student Presentation Award

Presentation Abstract

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Faculty News Spring 2021

Waleed Abdalati testified to Congress for the House Select Committee on the Climate Crisis.

Holly Barnard Awarded Boulder Faculty Excellence in Leadership & Service Award for 2020-2021

Emily Yeh Winner of the 2020-2021 Outstanding Faculty Mentor Award

Yaffa Truelove Wins Best Urban Studies Article 2020

Katherine Lininger

Morteza Karimzadeh awarded RIO Seed Grant

MeatOut Day: Heide Bruckner Interviewed on Colorado Public Radio

John O'Loughlin: Ukrainian Resident’s Divided Views on the Donbas Conflict

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Morteza Karimzadeh awarded RIO Seed Grant

Grant Reviewers' Comments

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Scientists aim to fuse Earth data to help classify, map sea ice

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NSF award will support Karimzadeh and team in fusing heterogeneous large earth data for sea ice mapping

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2019 Fall Newsletter Published

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Morteza Karimzadeh on 9News: Facebook data helps ��Ƶ researchers predict COVID surges