DSAI HUB Grants

On Tuesday, February 11, 2025, the Data Science Initiative (DSI) hosted the highly anticipated Seed Grant Showcase, bringing together a diverse community of researchers, students, and professionals to celebrate groundbreaking data science research. Held in a dynamic and collaborative environment, the event provided a platform for the 2024 Seed Grant recipients to share their innovative projects, engage in thought-provoking discussions, and explore future research collaborations.

Despite the crisp winter evening, the atmosphere inside was lively, as attendees immersed themselves in the latest advancements supported by the DSI Seed Grant program. The event underscored the initiative’s commitment to fostering interdisciplinary research and driving impactful discoveries across various domains.

A highlight of the evening was a special session where previous years' Seed Grant winners spoke to attendees, sharing insights on their research journeys and the impact of their projects. Their presentations provided valuable perspectives on how the program has influenced their work and fostered continued innovation.

Following this, the event transitioned into an engaging poster reception, where this year’s Seed Grant recipients showcased their work and engaged with fellow researchers. Attendees had the opportunity to delve into complex topics, ask insightful questions, and connect with experts working at the forefront of data science applications. The evening highlighted the breadth and depth of research made possible by the Seed Grant program, reinforcing its role in advancing knowledge and innovation.

The following research posters were presented during the reception:

As the evening concluded, the Seed Grant Showcase reaffirmed the value of data science in addressing complex challenges and advancing interdisciplinary collaboration. The event not only celebrated the achievements of the 2024 Seed Grant recipients but also inspired conversations that will shape the future of data-driven research. 

Attendees shared that, while they may not be researchers themselves, the conversations after the presentations were incredibly valuable. Many expressed excitement about seeing the innovative projects funded through DSI and appreciated the opportunity to share their work with fellow faculty and researchers interested in data science. The event also provided a great chance to make meaningful connections with colleagues, and attendees enjoyed the lightning talks, poster session, and social gatherings as excellent ways to engage with others and foster collaboration.

With the success of this year’s showcase, the DSI looks forward to continuing its mission of supporting innovative projects that push the boundaries of data science and impact a wide array of scientific fields. 

The DSI and its members congratulate the 2024 awardees and look forward to learning about their research progress at our next showcase.

We are thrilled to announce the recipients of the 2023 Graduate Assistantship Awards, showcasing the innovative research supported by the Data Science Initiative. 

Please join us in celebrating the achievements of the following individuals, whose remarkable contributions extend across various colleges and programs:

Claire Menard 

Response of Crop Genomes to Environmental Stress

•    School/Department: CFANS (College of Food, Agricultural and Natural Resource Sciences)

•    Program: Agronomy and Plant Genetics, Plant and Microbial Biology

Evan Dastin-Van Rijn

Blackbox Optimization of Cognitive Control with Neuromodulation 

• School/Department: CSE (College of Science and Engineering)

• Program: Biomedical Engineering

Drew Swartz

Effectiveness and Producers Perceptions of Camera-based Technology detecting early stages of lameness in dairy cows

• School/Department: College of Veterinary Medicine

• Program: Veterinary Population Medicine, Veterinary Medicine

Kate Dembny

Functional Connectivity as a Biomarker for Working Memory Dysfunction in Parkinson's Disease

• School/Department: CSE (College of Science and Engineering)

• Program: Biomedical Engineering

Jack Wolf

Innovative Statistical Methods for Personalized Treatment Decisions in Cancer Clinical Trials

• School/Department: School of Public Health, Division of Biostatistics

• Program: Biostatistics

Leikun Yin

Sustainable Cashew Plantation Expansion in West Africa: Balancing Industry Growth, Poverty Reduction, and Conservation using Deep Learning and Geospatial Data

• School/Department: CFANS (College of Food, Agricultural and Natural Resource Sciences)

• Program: Bioproducts and Biosystems Engineering, Bioproducts and Biosystems Science, Engineering and Management

Chris Wojan

Can biodiversity reduce Lyme disease prevalence? A data-driven approach

• School/Department: College of Biological Sciences

• Program: Ecology, Evolution, and Behavior

Mathew Fischbach

Enhancing Parkinson’s Disease risk prediction with genetic interaction-based machine learning models

• School/Department: CSE (College of Science and Engineering)

• Program: Computer Science and Engineering, Bioinformatics and Computational Biology

Jiacheng Liu

Identify potentially avoidable blood draws via estimating coagulation measurements for pediatric patients treated by heparin infusion

• School/Department: CSE (College of Science and Engineering)

• Program: Computer Science and Engineering

Jingxuan Deng

Optimizing Mineral Carbon Storage through High-performance Computing and Machine Learning

• School/Department: CSE (College of Science and Engineering)

• Program: Department of Earth and Environmental Sciences, Earth Sciences

Xiangyu Zhang

Searching for new signals under high background: a novel inferential framework

• School/Department: CLA (College of Liberal Arts)

• Program: School of Statistics

Cooper Gray

Novel Insights into CSF Flow Alterations to Inform Future Treatment of Neurodegenerative Diseases

• School/Department: CSE (College of Science and Engineering)

• Program: Mechanical Engineering

Seiya Wakahara

Precision Nitrogen and Irrigation Management of Potatoes based on Multi-source Data Fusion using Machine Learning and Crop Growth Modeling

• School/Department: CFANS (College of Food, Agricultural and Natural Resource Sciences)

• Program: Soil, Water, and Climate, Land and Atmospheric Science

Haoyu Yang

Personalized Disease Progression Modeling Using Privileged Information

• School/Department: CSE (College of Science and Engineering)

• Program: Computer Science and Engineering

The total amount awarded for this round is an impressive $678,864.83. Congratulations to all the awardees! The next round of applications will be in Oct. 2024.

Sincerely,

The Data Science Initiative

We're excited to share the news of our recent Seed Grant and Graduate Assistantship Awards, reflecting the collaborative spirit within the Data Science Initiative. 

Join us in congratulating the following individuals whose outstanding contributions span diverse colleges and programs:

Chang Ge 

Privacy-Preserving Data Publishing using Large Language Models

• College of Science and Engineering, Computer Science and Engineering

Ruihang Zhang, Kun Zhang

Prediction of Wall Shear Stress in Open Cavity Models: An Integrated Study using CFD models and Data-Driven Sparse Sensing

• College of Science and Engineering, Civil Engineering

• College of Science and Engineering, Mechanical Engineering

Lance Augustin

CITE-seq Analysis of Bacterially Delivered Anticancer Immunotherapy

• UMN Medical School, Surgery

Rick Jansen, Christy Henzler, Natalia Calixto Mancipe

Identifying Gene Interacting Networks in PDAC Using Deconvolution and Integrative Multi-omics Analysis

• Clinical and Translational Science Institute

• Minnesota Supercomputing Institute

Emil Lou

Defining Patterns of Spatial Genomic Changes in Clinical Specimens Before and After Cancer Treatment

• UMN Medical School, Hematology, Oncology, and Transplantation

Erich Kummerfeld, Michael Bronstein, Alexander Rothman

Experimentally Dissecting Inter-analyst Reliability in Data Science: the Roles of Vague Questions and Imprecise Interpretations

• Institute for Health Informatics, 

• UMN Medical School, Psychiatry and Behavioral Sciences

• College of Liberal Arts, Psychology

Yulong Lu

Towards Robust Deep Learning Methods for Multiscale Simulation in Heterogeneous Materials

• College of Science and Engineering, Mathematics

Jue Hou, Ju Sun, Margy McCullough-Hicks, Christopher Streib, Rui Zhang

Data Science Methods to Enable Real-world Evidence for Supporting Stroke Care (DRESS)

• School of Public Health, Biostatistics and Health Data Science

• College of Science and Engineering, 

• UMN Medical School, Neurology, Computational Health Sciences

Harrison Quick

Creating a Synthetic Vital Statistics Data Repository for Minnesota

• School of Public Health, Biostatistics and Health Data Science

Ardeshir Ebtehaj, Michael Steinbach

Advancing Data Science Foundation for Shrinking the Uncertainties in Bottom-up Estimates of Methane Emission from Arctic Lakes

• College of Science and Engineering, Civil, Environmental, and Geo-Engineering, Computer Science and Engineering

Rajesh Rajamani, Robert McGovern

Home-Based Individualized Analysis of Postural Instability in PD Patients

• College of Science and Engineering, Mechanical Engineering

• UMN Medical School, Neurosurgery

Chad Myers

Multi-task deep learning models for neurodegenerative disease risk prediction

• College of Science and Engineering, Computer Science and Engineering

Cara Santelli, Tianhong Cui, Chang Ge, Yao-Yi Chiang

Prioritizing Tribal Data Benefits and Governance in Developing and Applying Chemical Sensors for Water Quality Monitoring

• College of Science and Engineering, Earth and Environmental Sciences, Mechanical Engineering, Computer Science and Engineering

Lin Zhang, Joe Koopmeiners

Development of novel statistical imaging partitioning tools for interpretable lesion-wise cancer detection using 3D prostate imaging data

• School of Public Health, Biostatistics and Health Data Science

Levi Teigen, Christopher Staley

Determination of Functional Drivers of Lewy Body Disease among the Intestinal Microbiota

• CFANS, Food Science and Nutrition

• UMN Medical School, Medical School

Stephen Guy, Rachel Hawe

Data-driven Metrics for Upper-Limb Motor Assessment in Children with Neurodevelopmental Disorders

• College of Education and Human Development, Computer Science and Engineering, Biomechanics and Neuromotor Control

Zhang Rui, Steve Johnson, Anne Blaes

CancerLLM: Development of a Cancer-domain Large Language Model to Extract diagnostic information

• UMN Medical School, Computational Health Sciences

• Institute for Health Informatics, Oncology

Ju Sun, Zhaosong Lu

Stochastic Optimization for Constrained Deep Learning

• College of Science and Engineering, Industrial and Systems Engineering

Steffen Ventz

Robust Bayesian Transfer learning

• School of Public Health, Biostatistics and Health Data Science

We express gratitude for the diverse colleges and programs that participated in this joint effort, furthering the field of data science. The total amount awarded for this round is an impressive $972,058.00.

Congratulations to all the awardees, and best wishes to the applicants for the upcoming round!

Sincerely,

The Data Science Initiative 

Explore a snapshot of the extensive research initiatives backed by our funding across diverse colleges and departments. Witness the collective brilliance that defines and energizes our dynamic data science community.