You are browsing archives for

Author: MBDH

Climate Change Affecting Crops in Iowa

December 7, 2021Written by MBDH

By Raleigh Butler

In 2010, the University of Minnesota received a grant from the National Science Foundation to study climate change using data-driven methods. The project included Midwest Big Data Innovation Hub co-PI Shashi Shekhar and a team of researchers from across the country. The research led, in part, to explorations of connections between food, energy, water, and climate change.

Because greenhouse gases contribute heavily to climate change, activities that contribute to their release are becoming more divisive with time. There’s no doubt that the food we eat is becoming an increasingly political statement. According to the 2019 Environmental Protection Agency report, agriculture was responsible for 10% of all greenhouse gas emissions, amounting to 650 million metric tons of CO2. A quarter of those emissions (about 2.5% of all greenhouse gas emissions) come from livestock before they are butchered.

The Coupled Model Intercomparison Project 3, a project of the World Climate Research Programme, predicts when the global average temperature will increase by 2°C. The approximately 0.75°C increase in temperature since 1950 has caused a huge increase in natural disasters. This can be seen by an increase in hurricanes, such as Katrina, and the melting of the polar ice caps, among other issues.

According to the graphic from their report, the global average temperature has already increased by about 1°C (1.8°F) relative to preindustrial levels, and will continue rising to as much as 7°C in some regions by the end of the century.

Climate change is the culprit behind many natural disasters, as more than 170 scientific reports covering 190 extreme-weather events found that around two-thirds of extreme-weather events likely originated from, or were exacerbated by, anthropogenic hazards.

How does this apply to the Midwest? Let’s look at Iowa, where over 90% of its land is used for agriculture. In recent years, extreme-weather events have wreaked havoc on crops.

2020: Inland Hurricanes

Farmers and agricultural specialists were worried in August 2020 when portions of Iowa experienced derechos. Pronounced deRAYchos, these are widespread, long-lived thunderstorms mixed with 100–130 mph winds. According to the National Weather Service, a derecho like this was “a roughly once-in-a-decade occurrence” in the Midwest.

These immensely strong storms destroyed crops and decreased crop output for the season in Iowa. According to the power-outage map published by the University of Wisconsin–Madison’s Cooperative Institute for Meteorological Satellite Studies (CIMSS) below, A quarter of the counties in Iowa caught the worst of the storm. All the affected counties were in the central-east portion of the state.

2021: Drought

There were high hopes that 2021 would bring a better crop return. However, when agricultural scouts crossed Iowa in mid August, they found that the state was suffering from extreme drought.

Although derechos and rain-damaged fields were no longer the center of concerns, 2021 has brought high levels of drought. According to the U.S. Drought Monitor, on August 17, 2021, 79% of Iowa was impacted by some degree of drought.

National Drought Mitigation Center map of the 2021 drought in Iowa — The U.S. Drought Monitor is jointly produced by the National Drought Mitigation Center (NDMC) at the University of Nebraska–Lincoln, the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. Map courtesy of NDMC.

The areas were being scouted out ahead of time for the upcoming 2021 Pro Farmer Crop Tour. Scouts on crop tours have the job of evaluating likely crop production in each region. For more information on crop tours, visit this link.

2021: Storms

Drought became an afterthought just days later. On August 24, 2021, the Midwest experienced multiple storms. Although the severity of the storms did not come close to derechos, they still left behind large paths of downed corn and soybeans. On August 28, 2021, South Dakota and southwest Minnesota even experienced baseball-sized hail.

According to Iowa State University (ISU) Extension Field Agronomist Terry Basol, “The storms hit northeast Iowa farms pretty good, honestly.” Basol said, “It’s amazing the scope of the crop damage,” he continued, concerned about the pace of harvest and crop quality.

Unfortunately, the rain has come too late for many crops, and on top of that, some areas are even flooding. One person, Iowa State University Extension crop specialist Angie Rieck-Hinz, said, “The crop is highly variable. Crop conditions are literally all over the place.”

What’s to Come?

Amidst all these natural disasters and climate change, what can be expected for the future of agriculture in Iowa? In April 2021, the Environmental Defense Fund commissioned KCoe Isom, an agricultural consultancy, to model the potential climate change impacts on Iowa corn, soy, and silage production over the next two decades. According to that site, “Iowa farmers could see statewide gross farm revenues reduced by as much as $4.9 billion per decade. Because with climate change agricultural prices are likely to rise, relative to without climate change, the impact to gross farm revenues from yield impacts will be offset to some degree by higher prices.”

Unfortunately, the increase in climate change (and resulting natural disasters) is likely to continue reducing levels of crop production. This will result in an increase in food prices where those crops are sold, affecting consumers across the country.

The roles for data science and related research around climate and agriculture are growing: in September 2021, the National Science Foundation funded a new multidisciplinary institute led by the University of Illinois, called I-GUIDE, which is focused on better understanding the risks associated with climate change.

Get Involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas. The MBDH has a variety of ways to get involved with our community and activities.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Indiana University, Iowa State University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

ag food water, blog

How Do Scientists Help AI Cope with a Messy Physical World?

December 1, 2021Written by MBDH

By Qining Wang

When we see a stop sign at an intersection, we won’t mistake it for a yield sign. Our eyes recognize the white “STOP” letters printed on the red hexagon. It doesn’t matter if the sign is under sunlight or streetlight. It doesn’t matter if a tree branch gets in the way or someone puts graffiti and stickers on the sign. In other words, our eyes can perceive objects under different physical conditions.

A stop sign. Photo by Anwaar Ali. — *Photo by Anwaar Ali via Unsplash*

However, identifying road signs accurately is very different, if not more difficult, for artificial intelligence (AI). Even though, according to Alan Turning, AIs are systems that can “think like humans,” they can still present limitations in mimicking the human mind, depending on how they acquire their intelligence.

One of the potential hurdles is to correctly interpret variations in the physical environment. Such a limitation is commonly referred to as an “adversarial example.”

What Are Adversarial Examples?

Currently, the most common method to train an AI application is machine learning, a type of AI process that helps AI systems learn and improve from experience. Machine learning is like the driving class an AI needs to take before it can hit the road. Yet machine-learning-trained AIs are not immune to adversarial examples.

Circling back to reading the stop sign, an adversarial example could be the stop sign turning into a slightly darker shade of red at night. The machine-learning model captures these tiny color differences that human eyes cannot discern and might interpret the signs as something else. Another adversarial example could be a spam detector that fails to filter a spam email formatted like a normal email.

Just like how unpredictable individual human minds can be, it is also difficult to pinpoint the exact origin of what and why machine learning makes certain predictions. Neither is it a simple task to develop a machine-learning model that comprehends the messiness of a physical world. To improve the safety of self-driving cars and the quality of spam filters, data scientists are continuously tackling the vulnerabilities in the machine-learning processes that help AI applications “see” and “read” better.

What Are Humans Doing to Correct AI’s Mistakes?

To defend against adversarial examples, the most straightforward mechanism is to let machine-learning models analyze existing adversarial examples. For example, to help the AI of a self-driving car to recognize stop signs under different physical circumstances, we could expose the machine-learning model that controls the AI to pictures of stop signs under different lightings or at various distances and angles.

Google’s reCAPTCHA service is an example of such a defense. As an online safety measure, users need to click on images of traffic lights or road signs from a selection of pictures to prove that they are humans. What users might not be aware of is that they are also teaching the machine-learning model what different objects look like under different circumstances at the same time.

Alternatively, data scientists can improve AI by teaching them simulated adversarial examples during the machine-learning process. One way is to implement a Generative Adversarial Network (GAN).

GANs consist of two components: a generator and a discriminator. The generator “translates” a “real” input image from the training set (clean example) into an almost indistinguishable “fake” output image (adversarial example) by introducing random variations to the image. This “fake” image is then fed to the discriminator, where the discriminator tries to tell the modified and unmodified images apart.

The generator and the discriminator are inherently in competition: The generator strives to “fool” the discriminator, while the discriminator attempts to see through all its tricks. This cycle of fooling and being fooled repeats. Both become better at their own designated tasks over time. The cycle continues until the generator outcompetes the discriminator, creating adversarial examples that are indistinguishable to the discriminator. In the end, the generator is kept to defend against different types of real-life adversarial attacks.

AI Risks and Responses

GANs can be valuable tools to tackle adversarial examples in machine learning, but they can also serve malicious purposes. For instance, one other common application of GANs is face generation. This so-called “deepfake” makes it virtually impossible for humans to tell a real face from a GAN-generated face. Deepfakes could result in devastating consequences, such as corporate scams, social media manipulation, identity theft, or disinformation attacks, to name a few.

This shows how, as our physical lives become more and more entangled with our digital presence, we can never neglect the other side of the coin while enjoying the benefits brought to us by technological breakthroughs. Understanding both would serve as a starting point for practicing responsible AI principles and creating policies that enforce data ethics.

Tackling vulnerabilities in machine learning matters, and so does protecting ourselves and the community from the damage that those technologies could cause.

Learn More and Get Involved

Curious whether you can tell a real human face from a GAN-generated face? Check out this website. And keep an eye out for the Smart & Resilient Communities priority area of MBDH, if you wish to learn more about how data scientists use novel data science research to benefit communities in the Midwest. There are also several NSF-funded AI Institutes in the Midwest that are engaged in related research and education.

AI, blog, Smart, Connected, & Resilient Communities

Teaching During a Pandemic

November 4, 2021Written by MBDH

By Raleigh Butler

This story is part of a series on coronavirus research in the Midwest region. To explore other NSF-funded research addressing the COVID-19 pandemic, please visit the COVID Information Commons, a project of the four NSF Big Data Innovation Hubs.

In early 2020, the USA was brought to a standstill when stores, schools, and other everyday locations closed to help fight the COVID-19 pandemic. This piece will discuss a variety of RAPID awards funded by the National Science Foundation in the Midwest to support research to mitigate a variety of education-related pandemic challenges. Links to the NSF award abstracts will be linked in the article.

Remote Learning Research

A common topic for early school-related COVID-19 research was the exploration of remote or online styles of teaching and learning. In the early stages of the pandemic, it was unclear when schools would be able to reopen. Teachers and administrators were attempting to learn from prior work on remote learning at all levels, kindergarten through college.

For instance, in a project led by researchers from the Chicago Board of Education, researchers discuss the impact of public school students studying computer science remotely. Issues broached include access to the appropriate technology outside of school and possible socioeconomic variables at play. An award to the University of Kansas touches on a similar topic at the graduate level. This work is STEM-focused, addressing COVID-19-related challenges in engineering. One question the researchers ask is: “To what extent do the relationships between perceived e-mentoring support and student outcomes vary by demographics, disciplines, and institutional characteristics?”

A project at the University of Nebraska–Lincoln explores this topic from the faculty perspective. The project goal is described as “to identify cognitive and emotional themes concerning faculty and staff adaptability and community engagement during a crisis compared to those found under typical teaching circumstances.“ Adaptability is a key theme here—professors, regardless of their experience using online-teaching technology, were expected to learn how to do so. In Illinois, the Chicago Public Schools did its own research on forced remote learning and “mitigating the impact” of the sudden transition from in-person to online learning.

Remote Learning Activities

Researchers at the University of Minnesota–Twin Cities put an interesting spin on remote learning with their work on virtual reality (VR). Virtual reality allows learners to immerse themselves in a different world using a pair of electronic goggles or a headset. This device is used to portray a different world, like being “inside” a video game. The Minnesota researchers recognize that “many people, especially young adults, typically being used to active social life, can find this physical/social distancing leading to social isolation. Unfortunately, social isolation is strongly associated with negative outcomes for mental health and therefore represents a serious threat to long-term compliance.” The project aims to promote web-based VR as a way for people to interact safely in a shared environment, despite not actually being physically together.

The need for activities—especially for young learners—is addressed in Indiana University Bloomington’s research project. They have launched a Facebook group called CoBuild19, which works on making STEM activities more accessible to children.

Remote learning has also been addressed in an award granted to the Georgia Research Alliance. This award focuses on “ALOE”—Adult Learning and Online Education. Given the current state of the pandemic, it is immensely difficult, if not impossible, to continue education safely in person. The National AI Institute for Adult Learning and Online Education (AI-ALOE) addresses this by working to move adult-education opportunities online.

Mis/Disinformation

There’s a lot of information available about COVID-19. Some is real, some is misinformation (simply incorrect), and some is disinformation (incorrect with the intent to deceive). Many people have trouble deciding what sources to trust. A project led from the University of Michigan–Ann Arbor plans to follow a sample of university students. The premise of the research is to determine “whether and to what extent people follow recommendations and change behavior.”

Teaching About the Pandemic

Teaching about the pandemic itself is important. A relevant award is an exploration by researchers at the University of Nebraska–Lincoln into using popular media to educate youth on COVID-19-related issues. By using illustrated media such as comics to raise youth awareness of accurate coronavirus-related information, perhaps it’s possible to lessen the mis/disinformation discussed in the University of Michigan’s work. The project proposes assembling “an integrated package of high-quality, widely accessible media and other outreach materials designed to engage middle school youth, educators, and libraries in learning about viruses in relation to COVID-19.”

The researchers, wanting the material to be accessible, propose that “[t]hese resources will be disseminated broadly and at no cost to youth and educators of all kinds, including schools, libraries, museums, and other established networks for formal and informal science education.” Indeed, children’s lives are being altered drastically by COVID-19. It’s important that they know what is occurring to cause such alterations. In fact, the University of Missouri and the University of North Carolina at Chapel Hill collaborated to develop a curriculum for high school students. The curriculum covers epidemics in both scientific and social contexts.

Get involved

The projects described above were all funded by the NSF, which published a related story.

blog, COVID, Education & Workforce Development

Community Engagement through Open Watersheds

November 4, 2021Written by MBDH

By Qining Wang

Lake Michigan and the Chicago skyline. Photo by Muzammil Soorma. — *Photo by Muzammil Soorma via Unsplash*

Many living in and outside of the USA presume clean water to be universally accessible in the USA. In reality, many people are living in a water crisis.

A recent study published in Nature Communications assesses the degree of the clean water crisis. Researchers found that as of August 18, 2020, more than 22,000 community water systems are either serious violators of or in significant noncompliance with the Safe Drinking Water Act. As the researchers point out, “our findings demonstrate that the problem of water hardship in the United States is hidden, but not rare.”

A huge underlying cause is inaccessible data on water quality. Different governmental and state sectors, such as the National Oceanic and Atmospheric Association and the Environmental Protection Agency, collect data on various water sources. Yet, the lack of communication among different sectors creates fragmentation of watershed data. As a result, watershed data is widespread, difficult to locate, and sometimes wholly inaccessible. Such fragmentation significantly limits policymakers’ ability to make informed decisions to improve water quality. Neither would consumers be able to tell when their water is unsafe.

One solution is to create open data hubs that centralize accessible and interpretable data, which would require both governmental and nongovernmental efforts. As such, the creation of open watersheds manifests in interesting intersections of community empowerment, resident engagement, and watershed management. In a recent panel discussion titled “Open watersheds: Innovations in Community Water Data,” four panelists involved in open watersheds in the Midwest discussed the benefits, challenges, and opportunities in open-source environmental monitoring.

This panel discussion is part of the monthly Water Data Forum webinar series, co-hosted by the Midwest Big Data Innovation Hub, the Cleveland Water Alliance, and the Water Environment Federation. The participating panelists were Whitnye Long-Jones, founder and executive director of Organic Connects; Mark App, project manager of the Great Lakes Data Watershed; Barb Horn, expert facilitator and steering committee member of the Water Data Collaborative; and Brandon P. Wong, president and co-founder of Hyfi.

Envisioning Open Watersheds

Each panelist had their own vision for open watersheds. Despite coming from different backgrounds, all agreed on the importance of recruiting community effort. Wong spoke from a technological standpoint by relating open-source technologies to open watersheds. He said: “I think there’s a responsibility to see what’s going on underneath the hood.” When it comes to open watersheds, there should be transparency in the process, from data collection from physical devices to data storage. Wong believes that open technologies will allow people to speak openly about what they know about the watershed, knowing how the data is collected and processed.

Long-Jones mentioned creating connections with community members. Since data scientists tend to use jargon and terminologies to describe water data, it is crucial to train community members to make water data more interpretable. Horns reciprocated this point and talked about cultivating relationships between residents and institutions.

Tools for Open Watersheds

Panelists further discussed building connections and relationships when talking about different tools that can benefit the communities. Horn made a crucial point about building healthy relationships with new technologies that facilitate data collection: “Too often technology comes in with its excitement [. . .], but no one has spent that time helping them build the context on how to use it, so it just becomes a strategy that actually has a short-term gain but not long still sustainability even if it could have.”

Horn also explained using data and technology to serve “wholism.” In other words, we should use new technologies to collect data that foster collaboration and innovation. New technologies should not mean to create competitions that only profit a minority of people. “It’s not the community versus the agency or the Agency. It’s not the company industry against the community. It’s like, how can this [new technology] serve wholism? How can this technology serve us coming together in a whol[ly] innovative way?” Horn said.

Regarding the current challenges and barriers, App discussed how the lack of consistent standards in water data collection creates difficulty in data integration. He suggested creating new visions around watershed data. In those visions, data collection would not merely be the responsibility of isolated entities but would be up to the whole community. Skillful community members such as retired NASA engineers, motivated high school students, and computer professionals in pattern recognition can all contribute to monitoring water quality. He believes that those new visions will be the driving force to create open standards in open watersheds.

Open Watersheds and Beyond

The panelists also discussed the benefits of open watersheds beyond open-source data collection and environmental monitoring. Long-Jones emphasized that the community efforts in open watersheds can greatly benefit areas experiencing disinvestment due to historical redlining. “Now you’re seeing some of that even still continuing when we’re talking about cities [that] are experiencing dirty, unclear drinking water,” she said. Many residents in those communities struggle to meet their basic needs, and their survival priorities come before monitoring the contaminants coming out of their faucets.

In this regard, Long-Jones encourages us to envision communities beyond geographic boundaries and be open-minded and humble when engaging with community members bearing diverse backgrounds. Only by truly listening to each other and understanding where each of us comes from can we realize that open watersheds and improving water quality require everyone’s involvement.

Get involved

You can get more involved with open watersheds by participating in the Cleveland Water Alliance’s Smart Citizen Scientist Initiative, a movement that encourages youth, elder, and underrepresented citizen scientists to collect open-source data on Lake Erie with simple technologies. You can also join upcoming Water Data Forum sessions.

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas, which include Water Quality. The MBDH has a variety of ways to get involved with our community and activities.

blog, water

Midwest researchers address food insecurity and transportation access during the pandemic

October 19, 2021Written by MBDH

By Raleigh Butler and Qining Wang

The University of Michigan received a RAPID award from the National Science Foundation in the early stages of the pandemic to explore improving food-insecurity conditions driven by the pandemic. The project, titled “Improving Transportation Equity to Enhance Food Security for Families Vulnerable to COVID-19,” is led by Robert Hampshire, in collaboration with H. V. Jagadish, Tayo Fabusuyi, and Aditi Misra.

The project builds on earlier NSF-funded research that developed the Transportation Equity Open Knowledge Network (OKN). The researchers integrated data from the Food Security Index and other sources into the Transportation Equity OKN. The researchers proposed to “investigate, and begin to develop mechanisms to address, the lack of access to food (i.e., food insecurity) associated with COVID-19 and the role of transportation challenges leading to food insecurity.” The research builds on prior work to support the development and evaluation of a meal-delivery program, as well as the identification of people and places most at risk of food insecurity due to a lack of access to transportation.

PROJECT GOALS

As a part of the project, the research team provided background context and technical assistance to the City of Detroit’s pilot program that delivers meals to vulnerable families.

The project aims to address the food insecurity as a result of the underlying inequalities exacerbated by the COVID-19 pandemic. During the pandemic, many low-income, marginalized, and vulnerable households struggled with access to food because of reduced public-transit services and inability to access internet services. Consequently, these households cannot place food orders or call for food delivery. Fearful of contracting COVID, many also avoided in-person grocery shopping. Considering the underlying broader social inequality, the food-insecurity situation isn’t just about food. In times of COVID-19, it translates into broader issues of health insecurity.

To address this issue, Hampshire’s team takes a data-driven approach to estimate the number and the key demographics of households facing food insecurity. In addition, they also worked with the City’s pilot program—Covid Food Delivery Program (CFDP)—to provide meal-delivery services for identified food-insecure households that rely on public transit in the City of Detroit or based on health referrals. By making their results publicly available, the team hopes their findings could inform policy makers to create more effective mitigation measures.

PROJECT OUTCOMES

Choosing the City of Detroit as their case study, the team used data from multiple sources to identify the key demographic characteristics of households receiving Supplemental Nutrition Assistance Program/Electronic Benefits Transfer (SNAP/EBT) benefits. The team estimated that 71,600 households across Michigan met criteria for both food and transportation insecurity based on the US Census Public Use Microdata Sample (PUMS), of which 20,800 are from the City of Detroit. Finer segmentation based on geography and household composition were also carried out. By narrowing down the sample size to a finer geographic region, the team can easily replicate their approach for other local regions with more accurate Census tracts and more consistent information on food services.

The team randomly selected 350 patrons from the CFDP data dashboard to investigate the benefits of food delivery during the pandemic. They found that even though the program’s service only accounted for roughly 70% of the households’ weekly food consumption, 86% of the recipients of the program’s service reported having sufficient food each week.

However, many also reported that the food deliveries lack refrigerated items such as dairy and meat. Alarmingly, they also found more than a third of the patrons were first-time beneficiaries of CFPD, suggesting the pandemic is creating new cases of food insecurity in Detroit.

Through the analysis, the research team was able to identify the key benefits and issues of CFDP, which enabled CFDP to secure additional resources to redesign and expand their program. This program has now received an additional $1.5 million that can sustain the program until 2024. The work of the research team serves as an example of data for social good, in which a data-driven approach provides insightful guidance on how to mitigate issues around food insecurity.

Tayo Fabusuyi, the lead author of the project’s report, stated that “by documenting the program’s process issues and demonstrating how food insecurity severity could be estimated for different geographic areas, the program could easily be replicated at city or neighborhood level across the US. The project allows for learnings and adaptations not only by the City of Detroit, but also other cities that may be grappling with similar challenges.” The report closes by saying, “We believe that other cities will benefit from our documentation, learn from our experience and be able to modify similar program designs to address local peculiarities.”

Learn more

If you’re interested in learning more about how data directly connects to societal issues and human lives, consider attending MBDH’s “Smart & Resilient Communities / Data for Social Good” panel discussion, which will be held Thursday, October 28, 2021 – 2:00–3:00 p.m. CT / 3:00–4:00 p.m. ET. This panel includes one of the project’s Co-Principal Investigators, Tayo Fabusuyi.

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other projects we should include here, or to participate in any of our community-led Priority Areas.

blog, COVID

Big Data Neuroscience Workshop Brings Together a Transdisciplinary Research Community

October 13, 2021Written by MBDH

By Erica Joo

Researchers working at the interface of computational neuroscience, big data science, and health analytics held the latest in a series of workshops designed to virtually bring together their community to explore new research and opportunities. The 2021 Advanced Computational Neuroscience Network (ACNN) meeting was held September 2–3, 2021. This is the sixth year that this workshop series was organized since the initial seed funding from the Midwest Big Data Innovation Hub in 2016. Despite this being the second year that the COVID-19 pandemic has led to this being an online meeting, participation remained strong, with over 180 participants from over 40 institutions across the Midwest, USA, and several other countries.

“The success of this workshop series in bringing together researchers across the Midwest has gone beyond our initial expectations,” said co-organizer and ACNN co-founder Franco Pestilli. “Every year for the last six years we have had students, postdocs, and faculty join the events. There is a thirst for connection across the Midwest.”

“This is how I have come to think about the Midwest region: It is similar to Boston or New York City but with a geographical barrier,” Pestilli said. “Large hubs such as those in the East Coast have an incredible amount of talent compressed within a small urban area. That allows researchers to share scientific ideas, results, and resources just by walking into a building at the other side of town. The Midwest has similar talent but spread across an incredibly large geographic region. What our workshop series aimed at doing is to break the barriers to scientific research and education created by the geography of the Midwest region. We did so first by using support by the NSF to bring students and scientists together from across the Midwest.”

“We learned a lot by going virtual,” Pestilli continued. “In 2020, we had over 450 participants, and double that of the years before. This year’s event was hybrid and we learned that it is possible to successfully bring together talent across the Midwest using hybrid events. We think that if more of these events are organized, the data science and neuroscience talents across large U.S. regions can come together more often, and effectively, just like it can more naturally happen in the East Coast hubs. We can break the geographical barriers to science and education in the Midwest. We also think that the southern States possibly have a similar challenge, with talent dispersed across a large geographic area. I am looking forward to expanding our Neuroscience network to the South.”

The 2021 meeting included multiple research presentation sessions, lightning talks, and keynote talks. Dr. Kamil Ugurbil from the University of Minnesota delivered the “Nalbandov Public Lecture” on Harnessing Imaging towards meeting a Central Scientific Challenge of the 21st Century: Understanding Human Brain Function. And Charles Springer from the Oregon Health and Science University presented a keynote lecture on Celebrating the 50th anniversary of first human MRI for non-invasive 3D imaging of water molecules, or protons, bones and soft tissues.

Reports of some exciting new research included recent work of Monica Rosenberg from the University of Chicago on building generalizable models of human behavior using Big Data neuroimaging data, and Archana Venkataraman from Johns Hopkins University, who demonstrated novel strategies for understanding structural and functional brain connectivity and its applications to multidimensional clinical phenotyping.

“The ACNN workshop was fantastic,” said Rosenberg. “It was a great way to hear about cutting-edge theoretical and methodological work in the field and connect with the computational and network neuroscience communities here in the Midwest. I’d love to participate in the future.”

A number of talks and lightning presentations introduced powerful multimodal techniques for data-driven inference in structural, functional, and diffusion imaging (Shella Keilholz), contrasting population-based and individual differences in functional brain networks (Caterina Gratton), and deriving and utilizing proxy measures of brain connectivity (Joaquin Goni). One lightning talk held by Dr. Bradly Alicea from the University of Illinois at Urbana-Champaign was on network science and application to neuroscience and biology. One such presenter, Paul Camacho, who is a neuroscience doctoral student at the University of Illinois at Urbana-Champaign, shared his experience from the workshop.

“The workshop was a fantastic event with a rare balance of world-class keynote speakers and a well-curated set of lightning talks from our Midwest community,” said Camacho. “The level of discussion in each session was greater than I had come to expect from virtual conferences over the past couple of years. Although I did not personally know all of my fellow presenters, there was a sense of camaraderie that is emblematic of the Midwest and very appreciated in the scientific community. As a mark of how successful the workshop was in fostering collaboration, I have noticed an uptick in traffic to the GitHub repositories for the work I presented in my lightning talk.”

Dr. Bradly Alicea from the University of Illinois at Urbana-Champaign held a lightning talk on his research in the application of network science on neuroscience and biology. “The conference went well. I’ve attended other conferences before, and there were some great keynote speakers as well as interesting discussions at this one,” Alicea notes. “I’m looking forward to next year’s conference and hope to present again.”

Next year, the 2022 ACNN meeting is scheduled to be held in person in Texas. “After five tremendous years in the Midwest I relocated to the South, to the University of Texas at Austin,” said Franco Pestilli. “I am currently in the process of exporting the model for the Big Data Neuroscience workshops to the South, building a new team of collaborators across the Southern states. I am sure the Midwest team, Ivo Dinov, Rich Gonzalez, and the others, will continue the work we have initiated in the region. The Midwest Big Data Hub has been fundamental in supporting our activities and I am sure it has interests at stake to continue the ‘good’ that it has been started and to connect the human infrastructure resources the Midwest has available.”

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas.

blog

Building a Midwest Carpentries Community

September 20, 2021Written by MBDH

By Raleigh Butler

The Midwest Big Data Innovation Hub is committed to building data science instructional capacity in the Midwest region, particularly at smaller colleges and universities, such as predominantly undergraduate institutions (PUIs).

One avenue for this is the Midwest Carpentries Community, a partnership between the MBDH and the University of Wisconsin-Madison, under the Hub’s Community Development and Engagement (CDE) incubator program.

The project aims to build “hands-on data science instruction capacity,” by using the existing curriculum and workshop model of The Carpentries, an international member-supported organization that strives to teach data science and coding skills on a global scale. The organization is structured around three lesson programs: Software Carpentry, Data Carpentry, and Library Carpentry, which are “communities of Instructors, Trainers, Maintainers, helpers, and supporters who share a mission to teach foundational computational and data science skills to researchers.”

In this post, we will focus on a discussion with Sarah Stevens, who leads the Midwest Carpentries Community. Stevens is a 2021 member of the Executive Council for The Carpentries. She is also a Data Science Facilitator at the University of Wisconsin–Madison, in the Data Science Hub within the Wisconsin Institute for Discovery and American Family Insurance Data Science Institute.

How did you get involved with The Carpentries?
“I did my undergrad at the University of Illinois. My degree was in molecular and cellular biology, but I did a minor in informatics. And when I came to graduate school, I found that none of my classmates had done any coding and they didn’t know computation. And almost all of them had to learn how to do some computational analysis over the course of grad school. So to help support [them], I started a community of practice around helping each other with our computational needs and learning from one another. I was trying to bring people together not just to discuss the biology in our research, but actually the computation in our research, and in doing so I also got connected with The Carpentries community. There’s been an ongoing Carpentries community since long before my time at the University of Wisconsin-Madison. And my advisor recommended ‘maybe you should sign up for instructor training so you can learn how to teach these things better.’”

What are some of the main projects you’ve worked on during your time there, specifically in the Midwest?
“I’ve been trying to bring together researchers in the Midwest who are either running Carpentries communities of their own or want to get started with Carpentries communities. We’ve been hosting a monthly call to bring those people together to help each other, similar to the community of practice I started in grad school. I’d say probably instructor training is one of the things that I find the most useful and interesting in The Carpentries. I think it’s really cool to talk to other instructors about how to teach, and how to teach using evidence-based research, and how to teach computational skills and learn from one another.”

What are some of the skills that people develop in Carpentries workshops?
“They [the learners] come to learn R, Python, the Unix shell, and Git, but what I really want them to get is a foundation where they believe that they can learn more. I feel like a lot of people come to our workshops feeling like computing and technology is not for them. Maybe they’ve even had bad experiences trying to learn coding in the past. What I really want people to learn and come away with from our workshops is that they can learn this.”

What has been different about doing Carpentries-related activities specifically during the pandemic?
“Moving online has its own challenges. Being a part of a community of instructors, who are also all dealing with this transition to online at the same time, I got to learn a lot from what other people did and how it worked for them. So, as a community, we were able to share tips and tricks and best practices for moving online and learn from one another. That’s really one of the things I love most about The Carpentries community is being able to benefit from other instructors’ experiences.”

“I will say the worst part about moving online is that while I totally respect folks not turning on their video, it’s a little less rewarding to teach to a screen. You do get feedback, like the sticky note feedback we collect in Google forms and people typing in chat, ‘this was a great workshop.’ But you don’t get to see them actually overcome that boundary of ‘I didn’t think I could do it—and I can do it now or this makes sense to me suddenly.’ And so it’s a little less rewarding to teach online, I will say, but I do feel like it’s been a good learning experience of having to pivot and practice these skills in a different way of teaching and checking in with learners.”

You proposed the Midwest Carpentries Community project for the MBDH CDE program—what did you perceive as the need for that?
“I’m seeing communities start to form in other places across the world. And I think it’s really great for creating new Carpentry communities and teaching these important skills across the globe. I was running into people from other institutions who had interacted with The Carpentries in some way. I wanted to be able to share my experience with The Carpentries like at UW–Madison; what works well with the UW–Madison Carpentries community, with other folks in the Midwest and working to learn from them as well.”

“So, what works well at Illinois, what are they doing that we can learn from? Are they creating new workshops that we too could use? That’s where I saw the need—I wanted to be able to support these new instructors and new communities that we’re developing in the Midwest, and learn from the existing communities that have been teaching Carpentries workshops for a while and doing new and interesting things.”

What would you say to someone new to The Carpentries world about why it’s valuable to participate in the community beyond attending a workshop?
In addition to offering the teaching of various skills, Stevens says “I think it’s really valuable. There’s so many things you get from it, you learn a lot about building an inclusive community as that is a big part of the Carpentry community.”

She adds, “I see a lot of networking—developing an interpersonal network and being able to find employment in the future is also a benefit of this, but you make connections with other institutions and learn from them and other organizations across the globe, really, and so it’s a great opportunity to learn from others, not just being in the workshop, but observing other people in our community and their activities they’re up to.”

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in our activities, which include a data science student community and the national BD Hubs monthly webinar on data science education and workforce development.

blog, CDE

Meet the MBDH Fall 2021 science writing and coordination interns

September 9, 2021Written by MBDH

For Fall 2021, the Midwest Big Data Innovation Hub has four new interns joining the team to work on a variety of projects. One intern, Sushma Mahadevaswamy, will be working on project and events coordination. Three others, Raleigh Butler, Erica Joo, and Qining Wang, will be science writers, helping to amplify the many community-led projects in the Hub’s 12-state region. All will learn about the range of activities and communities the MBDH is involved in, and will receive mentoring and have opportunities for career development.

The MBDH has a number of events planned for Fall 2021, including ongoing webinar series (Water Data Forum, Data Science Student Groups), a new research development series called the Collaboration Cafe, and a two-day Regional Community Meeting, open to all.

To help develop these events, and do outreach to our student community, Sushma Mahadevaswamy has joined the MBDH team as a project coordination and events intern. She’s currently pursuing her master’s degree in information management at UIUC. Previously, she was a software developer for 3 years at Cisco. Hailing from the silicon city of India, she’s well versed in cloud computing, problem solving and algorithms (she knows her Big O’s), and software development.

While working at Cisco, she handled application security, across six cross-geographical teams based in India and the USA, through collaboration and communication. She loves to organize events to motivate her team. She’s a vibrant individual, who was an MC for various global events. Her strengths lie in development as well as efficient management of projects.

Her goal is to bridge the gap between technical and business aspects of product/project management. She’s excited to put her skill set to good use at MBDH. She will be engaging with the student community to organize knowledge-sharing events that will enrich the data science community.

In her spare time, she usually paints or goes on a hike. She’s done three Himalayan treks and hopes to ascent Mt. Everest one day. She also believes in giving back to the society and she regularly volunteers to teach underprivileged children. Her favorite quote is, “Make a difference, not a living.”

With programmatic activities ranging from the MBDH’s partnerships in its Community Development and Engagement (CDE) program, to other Priority Area work, exciting new projects in the region, and the events described above, there is a lot for the science-writing interns to draw from. They will be focused on telling the stories of the projects and the people—researchers, students, partners, and collaborators—and how the work they are doing is impacting the Midwest region, the nation, and the world.

Raleigh Butler is one of the three science writers interning at MBDH for the fall semester. Her undergraduate degree was a dual major in Linguistics and French at the University of Tennessee, Knoxville. She recently got her MS degree in UIUC’s Journalism program, graduating summa cum laude. Between the two degrees, she pursued a post-bac, focusing on introductory science courses.

Raleigh views science writing as a wonderful opportunity to combine STEM and the humanities. She aspires to “translate” technical verbiage into phrasing easily understood by the average reader. She emphasizes, “during these times of great scientific developments—not to mention health-related developments—it’s critical that the wider population have an understanding of what’s going on. By providing a reliable source of information that is also more understandable, perhaps we can assist in this education process.” Indeed, people frequently want to learn without necessarily reading a full-length technical article.

She believes that access to easy-to-understand material instead of difficult-to-parse journal articles will reach the population more successfully and wants to do her best on that front. For example, recently, she has been writing about COVID-19.

Raleigh says “I’m extremely excited about this opportunity to begin pursuing my dream job and to learn more about the field.”

Qining Wang (she/her) also joins MBDH this semester as a science-writing intern. Born and raised in China, Qining moved to the USA in 2013 and received her BA degree in chemistry from Rutgers University in 2018. She is now in her fourth year of pursuing a PhD in chemistry at Northwestern University. Co-advised by Prof. Joe Hupp and Prof. Justin Notestein, she synthesizes heterogeneous catalysts supported on metal-organic frameworks and investigates their gas-phase reactivities.

Aside from conducting scientific research, Qining is also conscious of the broader impact of science. She strives to inform the public of the progress in science and technology by making cutting-edge science more accessible to a lay audience. She wants to tell the stories of scientific discoveries and scientists through a curious lens without invoking intimidating equations and jargon. Therefore, in addition to writing, she also explores different approaches to effectively communicate science, such as videos, podcasts, and social media.

Qining says, “there are so many barriers to accessing and understanding science, from the intricate language scientists use to talk about science to the academic publications behind paywalls. As a scientist, I am responsible for removing those barriers.”

Erica Joo (she/her) is the third science-writing intern at MBDH this semester. As a junior at the University of Illinois at Urbana-Champaign, Erica is pursuing her BS degree in Molecular and Cellular Biology with a minor in Journalism. Additionally, she is an undergraduate researcher in Dr. Joe Qiao’s lab, and her research project is focused on meiotic checkpoint pathways and investigating certain enzymes involved with DNA repair pathways.

While being involved on the frontlines as a healthcare worker during the pandemic, she noticed a disparity in information about COVID-19, especially with the perpetuation of misinformation across the media. Erica recalls. “I felt that I wanted to be a part of the change that the world desperately needed at the time.” Combining her two passions, science and writing stories, was a catalyst in the evolution of her life. Erica has a strong interest in social issues and science research, and as a biology student herself, she understands the difficulty in understanding science at face value. “Navigating from one discipline to the other, I’m ultimately trying to create a common ground in my versatility.”

She aspires to take her experiences and academic background to not only help readers make sense of the science behind various types of research but to also address questions that the general public may wonder about and make it easily accessible. With high hopes and ambitions, Erica imparts, “from my experience in both fields, my job is always to write effectively so that audiences without extensive knowledge on a particular field can also learn and develop their own thoughts.”

MBDH Executive Director John MacMullen said, “We’re excited to have such a talented group of interns who bring a diverse set of skills and experiences to the Hub this semester. We look forward to seeing the work they produce and having the community engage with them on the wide range of data science activities happening across the region.”

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in our activities, which include a data science student community.

blog, news

Researcher Profile: Aditya Kulkarni

September 4, 2021Written by MBDH

By Raleigh Butler

On May 19, 2021, five researchers joined the COVID Information Commons (CIC) “Lightning Talks” webinar hosted by the National Science Foundation-funded Big Data Innovation Hubs. Each speaker was involved in COVID-19 research and gave a brief presentation on their project.

One of the presenters, Minnesota high school student Aditya Kulkarni, was almost indistinguishable from the other researchers in terms of his preparation and professional presentation.

Kulkarni is currently about to go into his senior year. He has been taking college classes since seventh grade. He started off just taking dual-enrollment math courses and now takes all of his classes at the University of Minnesota.

Though he has always been fascinated with programming and data science, the COVID-19 pandemic spurred Kulkarni on to explore data related to that specific issue. He submitted a paper entitled “Human Mobility Patterns Linked to COVID-19 Prone Locations” to the COVID Information Commons (CIC) Student Paper Challenge. His paper won third place, and he was invited to present the research alongside his more senior colleagues on the CIC webinar.

Needless to say, all this is an impressive feat, so I sat down and spoke with him a bit about his interests, school life, and hopes for the future.

How has taking college courses so early in your school career affected you? Do you think you’re more driven or serious than normal?
“Yeah, I think it’s actually been pretty helpful, because . . . I do feel like there’s some differences between taking high school classes and college classes. I mean, high school classes are like, fine—you have your different social groups, but with college, you’re also able to get exposed [to] the cutting-edge research that’s happening, [in] these fields that you’re learning about.”

Do you do dual-enrollment classes where the professors come to your high school, or do you go to the university?
“In this term, the high school isn’t really involved. I’m basically just like a college student traveling to campus coming back later in the evenings. And I’m still in the class with the other college students interacting with them, doing projects.”

Yeah, I was going to ask, if you were socially involved with college students; if you’re more mature than most people your age, then that would be something to appreciate.
“Yeah, and . . . it’s not like people even treat me weird. I just blend in with everyone else, just participating in things.”

Did you take any programming classes? And if so, like, did you enjoy them?
Kulkarni stated that his school offered a small programming course. “It was called Hour of Code. So there was a website, and we would have around an hour a day for one week. And we would just spend [time] seeing how to develop code, mainly block code. But at that time, it was kind of interesting to me seeing how I was able to create things just by dragging and dropping things. And yeah, it was pretty interesting. And [I] think it was mainly animation based . . . just making things move on the screen doing simple tasks. But from there, I think I saw the power and the capabilities that were there with coding.”

Do you and your peers participate in datathons, hackathons, and other kinds of science and computing activities?
This coming term, Kulkarni said, “the high school [won’t be] really involved,” but in the past, he started a STEM-related club at his high school and was very active in terms of connecting fellow students with professionals. Students from the club also team up to participate in hackathons and datathons. Kulkarni says he finds these competitions interesting “especially if there’s a sponsor, I’ll do something related to what they’re doing.”

For the CIC Student Paper Challenge, Kulkarni focused on a data set obtained from SafeGraph.com. This site tracks device movement (no personal information tied in) across the USA. Kulkarni used the available information to create related datasets and compare similar locations in Minnesota. For instance, he found 15 public places with June-July outbreaks and 15 places with no June-July outbreaks. His results show that longer-duration visits to an establishment are associated with COVID outbreaks. He received feedback and mentoring from Midwest Big Data Hub co-PI Shashi Shekhar, a professor of computer science at the University of Minnesota. His final paper is available online in the Columbia University Academic Commons repository.

Are there opportunities for you to build on this specific project that you submitted?
Currently, Kulkarni is pursuing “another direction of economic metrics.” “Even though it’s a human mobility data set, seeing the economic aspect in terms of socioeconomic groups, how [those people] were affected during the pandemic, and then their mobility in terms of that.”

So, I get the feeling you’re wanting to officially pursue computer science and data. If you had to choose a specific subfield to go into, what would you choose?
“I think I would actually go [into] data science. I think that’s the main thing. Then AI, with data sets, just seeing what are the possibilities to explore.” He went on to emphasize how technology could be of use in terms of bettering health situations and other human issues, “there’s just so much [COVID] data going further beyond into the predictive capabilities that can just be done with this much data. Because if there’s a future pandemic, which even though happens pretty rarely, if it happens, then maybe there’s something that we can learn from this one and apply it to the future.”

So, basically, what you like about research is the ability to help and provide insight into what can make the world a better place; is that how you would say it?
“Just because I can, through this mode . . . I can help the community as . . . a broader world or even as a small, small subsection. That’s a way where I can contribute to society, I guess.”

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led activities. The MBDH also has a data science student community, with a monthly webinar. Learn more about the COVID Information Commons webinar series and community.

blog, COVID

Big data aids PPE research

April 14, 2021Written by MBDH

By Barbara Jewett

This story is part of a series on coronavirus research in the Midwest region

Many researchers in the Midwest received awards from the National Science Foundation last year for developing novel masks and other personal protective equipment.

One of those researchers, Leonardo P. Chamarro, an associate professor in the Department of Mechanical Engineering at the University of Illinois at Urbana-Champaign, was awarded a special one-year, $200,000 RAPID grant to design a 3D-printable medical mask inspired by the nasal structures of animals. Working with Associate Professor Sunghwan Jung at Cornell University and Assistant Professor Saikat Basu at South Dakota State University, the team hopes their design addresses mask shortages and improves existing face protection by providing an open-source template for use with 3D printers.

The team captured small aerosol droplets that can carry viruses from inhaled air using a combination of copper-based filters and twisted periodic thermal gradients induced by spiral copper wires that mimic nasal pathways. The aerosol capture was articulated by modulating the dynamics of flow structures in the convoluted geometry (a vortex trap) and by thermophoresis action along the respirator’s internal walls (a thermal trap). Cyclic cold/hot temperature changes on the walls, along with ionic activity from the copper material, is used to inactivate the trapped viruses.

Dr. Chamorro took time away from his research to answer five questions about his COVID-19 research:

What’s the problem you’re trying to solve, and how is your team addressing it?
We are focused on exploring ways to mitigate the COVID-19 pandemic transmission and understand the role of turbulence [in virus spread]. In particular, we are collaborating with Sunny Jung at Cornell University and Saikat Basu at South Dakota State University in the development of a novel bio-inspired protective mask based on thermal and vortex traps. [We are also collaborating] with researchers at Purdue, Rensselaer Polytechnic Institute, the National Autonomous University of Mexico, and Tsinghua University in Beijing in the development of an autonomous robot for scanning, data mining, and disinfection. [In another project] we are also collaborating with a team at Northwestern on the description of contaminated droplet dynamics. My team uses theory, state-of-the-art flow diagnostics tools at various scales, and in-house analysis tools.

What’s changed since this project started last year?
It is a question that has many layers. The more we learn, the more we realize that several fundamental gaps need to be addressed to prepare for the next pandemic. Changes have occurred at various levels.

What data are you working with? Are there data challenges you’re dealing with? Are you using public data resources? Are you producing data that others are using?
We focus on the dynamics of droplets and aerosols and the interaction with closed domains at a range of scales. It requires performing experiments, capturing three-dimensional particle and flow dynamics, and, consequently, we produce our data. High-fidelity tracking of many particles and flow filed simultaneously in space and time is not trivial; however, my team has developed the needed technology to face those challenges.

Is your team seeking collaborators, subject matter experts, or other resources that you’d like to put a call out for?
Yes, we would very much like to collaborate at the fundamental and applied levels on various pressing problems, including, but not limited to, the role of turbulence across scales, ventilation, and boundary conditions.

Where can people learn more about your progress?
So far, we have contributed to two peer-reviewed papers. One paper in Extreme Mechanics Letters on the performance of various fabrics in homemade masks and another paper is in advanced stages of review in PNAS. My group also gave four technical talks on COVID research at the last American Physical Society in November, and we are updating our webpage to share recent findings.

Other PPE Projects
There are numerous other PPE projects in the Midwest that received Rapid Response Research grants. Here are a few of them:

Safely returning to using reusable equipment, including some PPE, is the focus of an award to Andrea Hicks, an assistant professor of civil and environmental engineering at the University of Wisconsin–Madison. You can read more about her work here.

Producing masks that capture and neutralize viral pathogens by adapting a decade of work developing a proprietary composite nanofiber material for water filtration is the focus of collaborators David Cwiertny, a professor of civil and environmental engineering and director of the Center for Health Effects of Environmental Contamination at the University of Iowa, and Nosang Myung, the Keating Crawford Endowed Professor in Chemical and Biomolecular Engineering at Notre Dame. Cwiertny received an award for this research project and Myung also received an award. You can read more about their work here and also here.

Developing smart face masks embedded with battery-free sensors to assess proper fit and monitor health is the focus of the award received by Northwestern’s Josiah Hester, an assistant professor of electrical and computer engineering. You can read about his work here.

Developing a new self-sanitizing medical face mask that deactivates viruses on contact earned an award for Northwestern materials science professor Jiaxing Huang. You can read about his work here.

Exploring coating the surface of PPE with copper and zinc oxide nanoparticles to limit the spread of viral particles is the subject of an award for Robert DeLong, an associate professor in the Nanotechnology Innovation Center at Kansas State.

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other projects we should include here, or to participate in any of our community-led Priority Areas.

blog, COVID

MBDH Learning Innovation Fellows program – first cohort projects

April 9, 2021Written by MBDH

The Midwest Big Data Innovation Hub Learning Innovation Fellows Program, housed at the University of Michigan School for Environment and Sustainability, enables teams to form for work toward better understanding of the intersections of the Hub’s “Cyberinfrastructure and Data Sharing” and “Data Science Education and Workforce Development” themes.

Our fellows work with faculty and teaching staff to create innovative interactive data analysis activities that can nest within sustainability science case studies. They design, prototype, and pilot these features in classrooms within the MBDH network. The program leverages talent and resources from two existing, open-source science learning environments. Gala (www.learngala.com) is a community-based, responsively designed sustainability science learning environment. Quantitative Undergraduate Biology Education and Synthesis (QUBESHub, or Qu) is a virtual center for faculty development and open educational resource sharing (https://qubeshub.org) that has had long-term support from NSF, formalizing and professionalizing open educational resources.

Through a series of virtual “Networkshops,” we connect undergraduate data science majors, graduate/professional students, faculty, and professionals. We can thus be inclusive, incorporating into classrooms problem-driven, data-rich material that speaks to lived infrastructural and environmental challenges from a range of communities across our region, and beyond. The team includes the following:

Leadership—

Rebecca Hardin (PI) is an anthropologist and Associate Professor at the University of Michigan School for Environment and Sustainability (UMSEAS), where she leads collaborations on the open-source, open-access learning platform Gala (www.learngala.com) and research group on Digital Justice. Rebecca also coordinates the Environmental Justice Field of Specialization and related Certificate program at UMSEAS.

Ann E. Russell (Co-PI) is an ecosystems ecologist, with special expertise in the biogeochemistry of tropical ecosystems. She is an Associate Adjunct Professor in the Department of Natural Resource Ecology and Management at Iowa State University, and PI of the NSF Research Collaborative network ALIVE: Authentic Learning in Virtual Environments.

M. Drew Lamar (Co-PI) is a mathematician and Associate Professor of Biology at William & Mary. His teaching and research are highly interdisciplinary in nature, using techniques and concepts from mathematics, statistics, biology, and computational sciences. Drew is Co-PI and Director of Cyberinfrastructure for the Quantitative Undergraduate Biology Education and Synthesis (QUBES) virtual center, with an interest and passion in open-source software development, quantitative biology education, and development of education gateways.

Ed Waisanen (Program Manager) is Program and Platform Lead for Gala (learngala.com). He has a master’s degree in Natural Resources and Environment from the University of Michigan, with a focus in Environmental Informatics and a background in multimedia production. Ed is focused on developing tools and communities that emphasize curation, open exchange, and narrative approaches to deepen learning.

Teams—

Data Learning for Restoration Ecology

Kyra Hull (Fellow) is a native of Grand Rapids, Michigan, and a first-year graduate student at Grand Valley State University, studying Biostatistics. Kyra is working on the following case about forest restoration, which is bilingual (Spanish and English versions): https://www.learngala.com/cases/a3224235-cdc0-44fc-a98b-46735dfef6c9

Karen Holl (Faculty Advisor) is a Professor of Environmental Studies at the University of California, Santa Cruz. Her research focuses on understanding how local and landscape-scale processes affect ecosystem recovery from human disturbance and using this information to restore damaged ecosystems. She advises numerous public and private agencies on land management and restoration; recently, she has been working to improve outcomes of the effort of the many large-scale tree-growing campaigns.

Data Learning to Address Groundwater Contamination

Saba Ibraheem (Fellow) is a second-year Health Informatics student at the University of Michigan, focusing on data analytics and research in health care. Saba is working on the following case, which is bilingual (English and French versions): https://www.learngala.com/cases/dioxane-plume

Rita Loch-Caruso (Faculty Advisor) is a toxicologist in the Department of Environmental Health Sciences at the University of Michigan School of Public Health, with a research focus in female reproductive toxicology and, in particular, mechanisms of toxicity related to adverse pregnancy outcomes such as premature birth.

Alan Burton (Faculty Advisor) is a Professor at the School for Environment and Sustainability and the Department of Earth and Environmental Sciences at the University of Michigan. His research focuses on sediment and stormwater contaminants and understanding contaminant bioavailability processes, effects, and ecological risk at multiple trophic levels. He is also a specialist in ranking stressor importance in human-dominated watersheds and coastal areas.

Data Learning in Livestock Ecologies

Daniel Iddrisu (Fellow) is a second-year student in Masters in International and Regional Studies, with a specialization in Africa, at the University of Michigan. He earned a BA degree in Integrated Community Development from the University for Development Studies, Tamale, Ghana. His research focuses on health, development, gender, and environmental health. The case he is working on takes place on the Greek Island of Naxos, but comprises skills for modeling and analyzing human/livestock interactions more broadly: https://www.learngala.com/cases/livestock-grazing

Johannes Foufopoulos (Faculty Advisor) is an Associate Professor at University of Michigan’s School for Environment and Sustainability, who focuses his lab research on fundamental conservation biology questions and on issues related to the ecology and evolution of infectious diseases. Major research projects examine how habitat fragmentation, invasive organisms, and global climate change result in species extinction.

Data Learning on Safari

Rahul Agrawal Bejarano (Fellow) has a background in computer science and he is currently working on a master’s degree at the University of Michigan School of Environment and Sustainability, with a concentration in Sustainable Systems. Rahul uses data from a diverse range of sources to shed light on today’s environmental challenges and develop innovative solutions, and is working on identifying climate-related vulnerabilities to our supply chains. He is working on this case, about the interactions of various wildlife species in the Serengeti: https://www.learngala.com/magic_link?key=oOTYOXyDRpmY_yM4AFlnXQ

Charles Willis (Faculty Advisor) is a Teaching Assistant Professor, Biology Teaching and Learning at the University of Minnesota. He is currently interested in the research and development of pedagogy practices for non-major biology students. In particular, he is focused on studying student-student and instructor-student feedback in online spaces. His research is also concerned with understanding how changing environments shape plant diversity on both evolutionary and ecological time scales. Currently, he is focused on using historical specimen data to study how historic climate change (over the past century) has impacted plant phenology and diversity across North America.

Jeffrey A. Klemens (Faculty Advisor) is an Assistant Professor of Biology at Thomas Jefferson University, where he serves as program director for the undergraduate biology curriculum. His current research activities are focused on the use of agent-based models to describe habitat use by organisms in the urban environment and the role of active learning in science education, particularly the use of systems thinking and other modeling techniques to improve student understanding of complex phenomena.

Data Learning in Detroit’s Eastern Market

Ghalia Ezzedine (Fellow) is a second-year master’s student studying Health Informatics. She is interested in leveraging data and digital tools to improve population health. In her free time, she likes to try new recipes, work out, and occasionally jump off a bridge or airplane. She chose this case study because of her interest in nutrition, and the shift in foods available at this iconic marketplace: https://www.learngala.com/cases/2b92db37-de87-4321-a531-510dea225189

Josh Newell (Faculty Advisor) is an Associate Professor in the School for Environment and Sustainability at the University of Michigan. He is a broadly trained human-environment geographer, whose research focuses on questions related to urban sustainability, resource consumption, and environmental and social justice. His research approach is often multiscalar and integrative and, in addition to theory and method found in geography and urban planning, he draws upon principles and tools of industrial ecology and spatial analysis.

blog, CDE, Education & Workforce Development