Research Pilot Teams

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TDAI partners with other OSU entities and resources to advance interdisciplinary science, scholarship and creative expression by a diverse group of teams. 

Find out more about TDAI Research Pilot Awards program.

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2020 Pilot Award Recipients

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Team

  • Inés Valdez, Political Science, Colleges of Arts and Sciences
  • Mark Moritz, Anthropology, Colleges of Arts and Sciences
  • TDAI core faculty Dennis Hirsch, Moritz College of Law
  • Dana Howard; Bioethics, College of Medicine; Philosophy, College of Arts and Sciences
  • Samantha Krening, Integrated Systems Engineering, College of Engineering
  • Samuel Malloy; Battelle Center for Science, Engineering, and Public Policy, College of Public Policy
  • Srinivasan Parthasarathy, Computer Science and Engineering, College of Engineering

Abstract

Through an integrated approach, we will develop a vibrant interdisciplinary community that uses a critical approach to data analytics. This community will design a curriculum in Social, Political and Ethical Dimensions of Data Science, including modules for the data analytics major, a graduate interdisciplinary specialization, and a certificate open to students, faculty, professionals, and broader audiences.

By being led by an interdisciplinary team, addressing head on questions of the broader impact of research, and designing curriculum innovations to prepare students to address social and political dimensions of data science, the project will be highly competitive for extramural funding.

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Team

  • Jian Chen, Eric Fosler-Lussier, D.K. Panda, Srini Parthasarathy, Rajiv Ramnath, Hari Subramoni; College of Engineering
  • Raghu Machiraju, College of Engineering, College of Medicine
  • Nathan Doble, Dean Vannasdale; College of Optometry
  • Anil Parwani, College of Medicine

Additional collaborators: Jose Otero, Neuroscience; Zaibo Li, Molecular Biology and Cancer Genetics Program; David Liebner, Medical Oncology

Abstract

Humans and machines collaborate in the analysis of multi-modal, multi-dimensional images in order to extract scientific insights in health and medicine. However, the exponential increase in the complexity, speed and volume of the images has outpaced human cognitive ability. Ground-breaking paradigms in computing and related areas need to be discovered.

A proposal to the NSF-Expeditions program in 2019 did not succeed because gaps were noted in problem definition and in the research approaches. Thus, the primary goal of this seed effort is to do the preliminary work needed to write successful proposals to funding agencies, while at the same time making discoveries through proof-of-concepts (POC). Specific outcomes are: (1) POCs and preliminary research data (2) definitions of the research problems (3) team formation, (4) designed pathways for broader impact and participation all leading to (5), a set of compelling proposals to specific funding agencies.

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Team

  • José Javier Otero, Pathology/Neuropathology, College of Medicine
  • Catherine Czeisler, Pathology/Neuropathology, College of Medicine
  • Raghu Machiraju, Computer Science, College of Engineering; Bioinformatics, College of Medicine

Abstract

The diagnostic workup of brain tumors requires a panel of immunohistochemical stains with a subset of tumors requiring additional molecular testing to reach a diagnostic category recognized by the World Health Organization. In the United States and worldwide, scarce resources are available to perform these tests, so methods that improve pre-test probabilities and decrease false positive results have significant clinical and financial impact.Our long-term goal is to improve and standardize testing and diagnoses for brain tumor patients worldwide by validating new diagnostic workflows using digital imaging, immunohistochemical tests, open source computing platforms and machine learning algorithms to improve diagnostic capabilities. The Ohio State University is the first US cancer center to transition to complete whole slide imaging, and therefore by evaluating this pre-existing clinical dataset we are in a unique position to generate a significant, vertical advance in improving diagnostic accuracy in neuropathology with modern Pathology Informatics approaches.

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Principal Investigators

  • Zaibo Li (PI), Pathology Informatics, Department of Pathology, Wexner Medical Center
  • Raghu Machiraju (Co-PI); Computer Science and Engineering, College of Engineering; Biomedical Informatics, College of Medicine
  • Anil V. Parwani (Co-PI), Pathology Information, Department of Pathology, Wexner Medical Center

Abstract

Breast cancer is the most frequent cancer in women with estimated cases of 268,600 in 2019 and the second most frequent cause of cancer death among women within the United States with estimated death of 41,760.[1, 2] More than half of breast cancer cases are estrogen receptor (ER)-positive.[3] The combined anti-estrogen therapy and chemotherapy have significantly reduced recurrence frequency and improved survival rate in certain populations of ER-positive breast cancer patients.[4] However, these therapies have toxic side effects; therefore, identifying patients who are more likely to benefit from chemotherapy is important. Oncotype DX (Genomic Health, Redwood City, CA) is a widely accepted molecular test to predict recurrence risk and chemotherapy benefits for early stage ER-positive/HER2-negative breast cancer patients. However, Oncotype DX is prohibitively expensive test with current list price of $4,350. The Oncotype DX assay assesses the expression of 21 genes involved in proliferation, invasion, estrogen and HER2 pathways to generate a RS to predict possibility of recurrence and chemotherapy benefit.[5, 6] However, multiple studies have suggested that standard histopathologic variables (including tumor grade, tubule formation, nuclear pleomorphism and mitotic activity) together with breast cancer biomarkers (ER, PR, HER2), can provide information similar to that provided by the Oncotype DX RS.[7-10]

Given the ability of deep learning to discern unknown relationships, we propose to develop deep learning model using digital whole slide image (WSI) obtained from histopathologic slides breast cancer and breast cancer biomarker results to serve as a proxy for Oncotype DX.

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Team

  • Matthew Tong, DO (PI), College of Medicine
  • Daniel Addison, MD (Co-I), College of Medicine
  • TDAI core faculty Ping Zhang, PhD (Co-I, TDAI Affiliate), College of Medicine, College of Engineering
  • Preethi Subramanian, Davis Heart and Lung Research Institute
  • Orlando P. Simonetti, PhD (Mentor, Co-I, TDAI Affiliate), College of Medicine
  • Richard Gumina, MD, PhD (Mentor, Co-I), College of Medicine

Abstract

The National Quality Strategy in 2011 adopted aims for better and affordable health care through promoting treatment strategies in cardiovascular disease. Large scale registry databases are capable of providing population-level data in an efficient and cost-effective method in improving quality and health care costs. We propose that, through a large cardiac magnetic resonance (CMR) registry database, CMR will provide higher diagnostic accuracy in a cost-effective manner.

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2017 Seed Grant Recipients

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Team 

  • TDAI core faculty Jim Hood, PI; Assistant Professor, Department of Evolution, Ecology, and Organismal Biology; College of Arts and Sciences
  • Jay Martin, PI; Professor, Department of Food, Agricultural and Biological Engineering, College of Food, Agricultural, and Environmental Sciences and College of Engineering

Overview Phosphorus (P) from the Maumee River watershed is the primary driver of harmful algal blooms in the western basin of Lake Erie. In 2017, Ohio, Michigan and Ontario developed a management plan to reduce P loading from the Maumee watershed by 40%. This plan requires substantial changes in land use within the watershed; yet, as with most P reduction plans, the potential efficacy of these efforts are uncertain due to a lack of understanding of the dynamics linking fertilizer application to riverine P yields. One of the primary areas of uncertainty concerns the dynamics of P within stream and rivers. In fact, many of the watershed models that have been used to forecast the ability of management plans to reduce P runoff typically ignore, or naively represent riverine P cycles. The watershed models contain mechanistic riverine P cycle equations that require empirically measured parameter values that are unknown. Therefore, most watershed models assume these processes have a net-zero effect on P yields. This is a critical knowledge gap because the magnitude of P retention and transformation (e.g., conversion of biologically recalcitrant to available forms of P) in rivers is highly variable in time and space. While some studies report low rates of P retention and transformation, others indicate that as much as 60% of available P can be removed in transport. Rivers can also be hotspots of transformations between available and recalcitrant P, shaping the biological reactivity of P exports. Thus, in-stream processes have the potential to influence the magnitude, timing, and availability of P exports from rivers. Unfortunately, aside from several time series of riverine P exports, there is no information about P cycling in the Maumee River.

Our research will clarify the role in-stream processes play in P retention and transformations in the Maumee watershed and improve the capacity of watershed models to mechanistically model these dynamics. This research need exists because (a) there is large uncertainty surrounding the potential role of riverine P cycles in shaping P export, (b) this uncertainty will likely increase with changes in land use and climate, and (c) the watershed models which are the basis for management decisions ignore these potentially important processes. Here, we propose research that will provide pilot data that will allow our group to pursue funding to expand the scope of this research through Ohio Sea Grant, NSF, USDA, and other state and federal agencies. We will also include outreach efforts to insure that our results inform future regional management plans. Our objectives are to (1) determine the importance of riverine P cycles in shaping P exports and (2) use our results to improve the capacity of watershed models to characterize in-stream processes.

Seed Grant Amount: $29,830

Matching Support (not required): $38,265

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Team

  • Anish Arora, PI; Department of Computer Science and Engineering, College of Engineering
  • Harvey Miller, Co-PI; Bob and Mary Reusche Chair in Geographic Information Science, Director of the Center for Urban and Regional Analysis, and Professor, Department of Geography, College of Arts and Sciences

Overview 

Persistent, wide-area counting of pedestrians and cyclists is a key yet unserved need in a host of urban applications. Existing data sets in the US are especially limited in their spatio-temporal coverage. We propose to develop and demonstrate low-power, low-cost wireless sensor networks as a basis for addressing this need, and thereby enhance existing data sets as well as support analytics for planning and safety applications. This yearlong effort will pilot a wireless sensor network along the segment of the Olentangy River Road Bike Trail that goes through OSU’s campus and analyze the resulting dataset.

Award Amount: $30,000

Matching Support (not required): $32,719

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2016 Seed Grant Recipients

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Team 

  • TDAI core faculty Margaret Kalcic, PI; Assistant Professor, Department of Food, Agricultural and Biological Engineering, College of Food, Agricultural, and Environmental Sciences
  • TDAI core faculty Steven Quiring, Co-PI; Professor, Atmospheric Sciences Program, Department of Geography, College of Arts and Sciences

Overview 

Agricultural lands in watersheds draining to western Lake Erie are under increasing pressure to maintain high crop yields while reducing the environmental, and particularly water quality, impacts of intensive agriculture. In August 2014, a harmful algal bloom in western Lake Erie compromised the water supply of the city of Toledo and led to a three-day “do not drink” advisory for the city and surrounding areas. This was due to a combination of agricultural nutrient runoff and climatic conditions; Lake Erie’s western basin has had several larger algal blooms in other recent years (2011 and 2015). In February 2015, the U.S. states and Canadian province bordering Lake Erie agreed to a phosphorus load reduction to the lake that scientists expect will reduce the size of algal blooms (and other nutrient enrichment problems in the lake) to a more manageable level. The primary source of Lake Erie’s nutrient enrichment is fertilizers and manures applied to agricultural lands that run off or leach into streams, rivers, and ultimately the lake. Decision-makers acknowledge that reaching water quality goals will be challenging and may reduce crop yields in the region. At the same time, agricultural stakeholders are already adapting their growing techniques to maintain high yields in a climate that has hotter and drier summers and wetter springtime conditions, when getting on the field is critical. The current political pressure to improve water quality, when added to the extreme events farmers are already experiencing due to climate variability and change, creates an opportunity for farmers and other stakeholders in the region to adopt more resilient farming practices. Despite significant advances in quantifying how climate change will impact agricultural production, significant knowledge gaps remain, particularly related to future climate extremes (i.e., floods, droughts). These gaps include: (1) spatial and temporal scale mismatches, (2) lack of agreement between climate models and scenarios, and (3) impacts that are a result of complex coupled human-natural systems are difficult to accurately define and simulate. The goal of this project is to support decision-making under deep uncertainty by identifying an optimal set of conservation practices for agricultural production given future climate changes.

Award amount: $30,000

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Team 

  • Erin Lin, PI; Assistant Professor, Department of Political Science, College of Arts and Science
  • TDAI core faculty Rongjun Qin, Co-PI; Assistant Professor, Department of Civil, Environmental and Geodetic Engineering and Department of Electrical and Computer Engineering, College of Engineering

Overview 

The weapons left behind from war—carpet bombs, cluster munitions, landmines—pose a significant risk to the communities left to live on that land. However, unexploded ordnance (UXO) are difficult to find, oftentimes hidden underneath several inches of ground. It remains a challenge for farmers, clearance teams, and aid organizations to identify contaminated areas. Instead of traditional, high-risk in-person surveying, we will develop a remote-sensing method to identify the location of unexploded ordnance. This method combines declassified U.S. Air Force data on payload drop sites from the secret bombing of Cambodia with high-resolution maps of terrain features and contemporary vegetation. By developing machine-learning methods on the images, we can identify an important element of post-bombardment geography: bomb craters, which provide a physical evidence of the number of bombs that have detonated within each drop zone. The U.S. Air Force data provide the number of bombs that were dropped in each payload, with the coordinates of each drop site. Since, for each payload, we know the total number of bombs dropped and the number of bombs detonated, we can estimate the number and density of unexploded ordnance left within each drop zone. As part of the process, we will use moderate resolution multispectral imagery to identify crop activity and yield, a new method that employs both temporal and reflective spectral statistics to generate granular data on cultivation and crop productivity. These remotely sensed images have been captured, rendered, and disseminated every eight days since 1998 through a U.S. government initiative, so we can develop a rich sense of land use patterns for the past two decades. The impact of UXO on the quantity of arable land can be estimated from these data using maximum likelihood techniques or a threshold regression model. By the end of data collection, we will be able to estimate the degree to which UXO change the total area of active agricultural land, and how persistent this effect is, from 1998 to present day. The impact of UXO on arable land will be reported to raise the awareness of the importance of de-mining.

Award amount: $29,960

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Team 

  • Derek Houston, PI; Associate Professor, Otolaryngology-Head and Neck Surgery, College of Medicine; Director, Buckeye Hearing and Development Center at Wexner Medical Center
  • Amanda Miller, Co-PI; Senior Researcher, Department of Linguistics, College of Arts and Sciences
  • Eric Fosler-Lussier, Faculty Participant; Professor, Department of Computer Science and Engineering, College of Engineering
  • Andrew Plummer, Post-Doctoral Researcher, Department of Linguistics, College of Arts and Sciences
  • Jongmin Jung, Post-Doctoral Researcher, Department of Otolaryngology-Head and Neck Surgery, College of Medicine

Overview 

Speech sound production is fundamental to oral communication. Studies in the mid-1990s showed dire real-world consequences of speech differences found between toddlers who could hear their own early vocalizations and those who could not. Speech sound disorders are observed in 2 – 25% (median = 8-9%) of children, with the higher proportion found in younger children. Better understanding of children’s articulatory mechanisms by building early development models will directly aid Speech Language Pathologists teaching young children to become more competent communicators. We propose to develop novel methods for estimating articulatory configurations and motor control strategies used by infants and young children to produce vocalizations, and build models of speech production that are appropriate for infants and toddlers. Throughout the duration of the project, speech-language pathologists from Nationwide Children’s Hospital who are working in the Buckeye Center for Hearing and Development will be actively involved in providing input about their needs and the challenges they face in working to improve the articulation of children experiencing speech sound disorders.

Award Amount: $26,300.75

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Team 

  • Jihun Hamm, Research Scientist, Department Computer Science and Engineering, College of Engineering
  • Per Sederberg, Associate Professor, Department of Psychology, College of Arts and Sciences TDAI affiliate
  • Mikhail Belkin, Associate Professor, Department of Computer Science and Engineering, College of Engineering

Overview By understanding how people go through each day along with what they perceive and learn during real-life events, we can have a tremendous impact on many fields. For example, a population-level model of what people do when and where can improve urban planning and public resource management. An individualized model of daily experience can be used for computerized personal assistants or detecting changes in one’s behavioral pattern that might be linked to an illness. Statistical patterns of daily events have been mined from databases such as American Time Use Survey (ATUS), which consists of timelines of daily activity and places collected from a large number of (n~50,000) participants. Yet, with a phone interview-based survey like ATUS, it is practically impossible to collect data in a time scale beyond a few days. Nor can we obtain fine-grained measurements of individual experiences from surveys with enough detail to differentiate patterns of, e.g., people with mild cognitive impairment and healthy groups. However, the situation has changed with the proliferation of smartphones, with an estimated 200 million devices in the U.S. alone. Smartphones are an enabling technology for collecting behavioral data unobtrusively from people in their everyday environments, through continuous sensing of location, motion, audio, image, and calling and messaging activity. The PIs and their collaborators have worked on building such a life logging system, which we have validated and is currently ready to deploy for data collection initiatives. Using rich sensory and meta-data acquired from the system, we intend to develop machine-learning algorithms to quantify daily human experience by event segmentation, classification, and prediction. The specific aims:

  • Capture daily experience of individuals using continuous sensory recordings from smartphones
  • Build accurate machine learning algorithms for segmenting, classifying, and predicting events from sensory data
  • Conduct a pilot study with 20 participants from healthy and mildly cognitively-impaired group to: a) quantify changes in the structure of experience that predict disease, and b) lay the foundation for a memory intervention

Award amount: $30,000

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2015 Seed Grant Recipient

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TDAI awarded its inaugural seed grant to Kui Xie, Cyphert Distinguished Professor in the College of Education and Human Ecology’s Department of Educational Studies and the director of the Digital Learning Research Lab.

Award amount: $24,970