10:30 AM - 10:45 AM: Registration Check-In

300 Pomerene Hall

10:45 AM - 11:30 AM: Forum Welcome

301 Pomerene Hall

11:30 AM - 1:00 PM: Poster Presentations & Research Partner Table Session

320 Pomerene Hall

Students and postdocs will present posters, while representatives from researcher resources at Ohio State and beyond will be available to discuss offerings and opportunities.

Student & Postdoc Presenters

John Baker, Critical Violations in Ohio's Restaurants
Neeraja Balasubrahmaniam, Discovering Microbial Indicators of Indoor Environmental Dampness Using Metatranscriptomics
J. D. Bourke, America's Small Business Innovation Machine
Jelena Brkljacic, Can Past Data Tell Us More About Future Orders? Increasing Efficiency of Sandwich Prep at Oxley's Cafe
Rachel Carr, Oxley's by the Numbers
Amanda Carroll, 4 Health Inspection Quick Facts
Chelsea Cobranchi, Ohio Health Inspections
Amy Cogswell, Government Funding of Small Business Technology Innovation in Millions of Dollars 2012-2021
Vishal Dey, Improving Compound Activity Classification via Deep Transfer and Representation Learning
Ruixuan Ding, Examining the Relationship Between Soil Moisture and Methane Emissions from a Landfill Site in Ohio
Wenxin Du, Quantitative Approaches Toward Equitable Virtual Reality
Eshita Eva, Evaluation of the Variable Importance of 1-km Downscaled Soil Moisture for the CONUS using Random Forest
Jacob Gardner, Assessing the Impact of New PFAS Regulations on Water Affordability in the State of Ohio
Jacob Gardner, Assessing the Financial Capability of Ohio Water Utilities to Meet New PFAS Limits
Yuntian He, FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
Lim Jaeyeong, Tailored Adaptation Strategies for Power Transmission Infrastructure Considering Regional Differences in Climate Change Efforts
Jenna Kline, Intelligent Cyberinfrastructure for Remote Sensing with Autonomous Aerial Robotics: Applications in Animal Ecology and Digital Agriculture
Matthew Kohut, Fuel Efficiency and Driving Behaviors
Jessica Kulp, Data for Healthy Communities: Development of a Supplemental High School Course
Ren Leaflight, US Department of Defense: Green Technology Innovation Funded Through the SBIR/STTR Programs
Grace Mackessy, Driving Behaviors: Comparing Fuel Efficiency with Telematics Data
Mazin Mahrouqi, Weather vs. Power Grid: A Machine Learning Approach to Predict Transmission Line Interruptions
Khyati Malik, Modeling Carbon Sequestration Through Land Use Changes in the Great Lakes Region
Pranav Maneriker, Interactive Fairness Auditing
Yi Mao, Time-Constrained Restless Multi-Armed Bandits with Applications to City Service Scheduling
Simon Nabors, Examining the Impact of Traffic on Fuel Efficiency
Neena Naikar, Route Optimization Algorithm for the City of Powell
Lingyu Ouyang, Feathers, Fields, and Fowl Cholera: Unveiling the Role of Land Cover in Fowl Cholera Outbreaks for Midwest Commercial Poultry Industry
Ximena Oyarzun Gonzalez, Patterns of Anticoagulant Describing Practices After an ED Visit Due to GI Bleeding
Bo Peng, HAM: Hybrid Associations Models for Sequential Recommendation
Xueqiao Peng, Using REinforcement Learning for Multi-Objective Cluster-Level Optimization of Non-Pharmaceutical Interventions for Infectious Disease
Micaela Richter, Understanding Racial and Ethnic Disparities in COVID-19 Outcomes Using an Agent-Based Simulation
Alexys Rodriguez Avellaneda, Examining Socioeconomic Disparities in Power System Fragility: A Case Study of Hurricane Effects in Florida
John Ruff, When Should I Order from Oxley's?
Oluwadamilola Salau, Beyond Water Bodies: Urban Pluvial Flood Risk in Columbus, Ohio
Sarikaa Sridhar, Towards Sustainable Knowledge Gap Identification with TinyML
Robert Sunderhaft, Deep Learning Improvements for Sparse Spatial Field Reconstruction
Yang Tang, OSU Digital Twin Engine Initiative
Zhanassyl Teleubay, Agricultural Drought Monitoring and Deep Learning-Based Prediction
Alyssa Tryansky, Oxley's Cafe Build Your Own Breakfast Sandwich Analysis from August 2019-May2022
Katie Vatke, Optimization of Rapid Sand Filtration for Passive Water Treatment
Claymore Wade, Columbus Trips on I-270 Behaviors for Eco-Friendliness
Wenyu Wang, Assessing the Impact of the 2021 Texas Winterstorm Disaster on Social Group Vulnerability and Resilience: A Spatial Flow Analysis and Deep Learning Approach
Yixuan Wang, Evaluating the Coal-to-Gas Policy in China: Heterogeneous Effects and Implications for Energy Transition
Sebrina Zeleke, When to Use Demographic Data in Healthcare Models: A Bias-Responsible Approach

Research Partners

Byrd Polar & Climate Research Center | Center for Clinical & Translational Science | Center of Microbiome Science | CHRR | Environmental & Social Sustainability Lab | Foods for Health | Global Arts + Humanities Discovery Theme | Infectious Diseases Institute | Office of Knowledge Enterprise | Ohio Supercomputer Center | Research Commons | STEAM Factory 

12:00 PM - 1:00 PM: Lunch

2:40 PM - 3:40 PM: Student Flash Talks

301 Pomerene Hall

Mohammed Ahmed | Goonmeet Bajaj | James Cross | Sara Johnson | Xiaoyu Liang | Hyeseon Shin | Zach Smith

Wrap up rap by Zeps, Event Rap

3:50 PM - 5:00 PM: Researcher Flash Talks

301 Pomerene Hall

Kelsea Best | Jean-Michel Guldmann | Athar Hanif | Md.Mohaimenul Islam | Dane Morey

Wrap up rap by Baba Brinkman, Event Rap

5:00 PM - 7:00 PM: Networking reception

320 Pomerene Hall

Party wrap up rap by Baba Brinkman and Zeps, Event Rap

8:30 AM - 9:00 AM: Light breakfast

320 Pomerene Hall

9:00 AM - 10:30 AM: Workshop

Hydrogen at Scale(s): From Process Design to Societal Shifts

301 Pomerene Hall

Discussion abstract

Hydrogen’s flexibility, both in terms of its use as a fuel source (in combustion applications or fuel cell technologies) and its potential sources (from water in electrolysis or fossil fuels), are part of its appeal to meet our needs as society moves to a decarbonized energy system.  However, the generation, distribution, and use of hydrogen will require substantial innovation at in all domains of our energy system.  At the molecular and process scales, the production of hydrogen will require an understanding of the dynamics of hydrogen generation to develop new generation technologies that can scale up to meet growing needs.  At the regional scale, there will be substantial changes to our infrastructure systems to build new (or repurpose existing) distribution networks and storage needs to buffer for inter-seasonal demand variations.  At the societal scale, there will be a need to rapidly deploy technologies capable of using hydrogen fuel to meet energy needs and design social interventions that support the communities that may be impacted by transitions in our energy systems. 

In this ideation workshop, participants will learn from researchers at Ohio State who are working on questions of hydrogen at scale.  Afterwards, participants will engage with interdisciplinary colleagues from across the University in ideation breakout sessions to identify fundable research questions around hydrogen and potential collaborators to enable growing the University’s portfolio in Hydrogen research. 

Wrap up rap by Baba Brinkman, Event Rap

Hosted by:

Daniel Gingerich, PhD, Assistant Professor, Civil, Environmental and Geodetic Engineering
Ohio State University

Panelists:

• Jeff Bielicki, PhD, Associate Professor in the Department of Civil, Environmental and Geodetic Engineering and Glenn College of Public Affairs; Ohio State University
• Jeffrey Jacquet, PhD, Associate Professor in the School of Environment and Natural Resources; Ohio State University
• Martina Leveni, PhD, LEGACY Postdoctoral Scholar in the Department of Civil, Environmental and Geodetic Engineering; Ohio State University

12:00 - 1:00 PM: Lunch

2:30 - 4:00 PM: Closing Keynote & Student Awards

301 Pomerene Hall

Student Awards Presentation wrap up rap by Zeps, Event Rap
 

CLOSING KEYNOTE

Knowledge-Guided Machine Learning: A New Framework for Accelerating Scientific Discovery and Addressing Global Environmental Challenges

Vipin Kumar, PhD
Regents Professor and William Norris Endowed Chair
Computer Science and Engineering
Director, CSE Data Science Initiative
University of Minnesota
Research funded by NSF (AI Institutes, Expeditions in Computing, BIGDATA,  INFEWS, STC, GCR, and HDR programs), DARPA, ARPA-E, and USGS

Closing Keynote rap by Baba Brinkman, Event Rap

Abstract

Process-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to incomplete or inaccurate representations of the physical processes being modeled. There is a tremendous opportunity to systematically advance modeling in these domains by using state of the art machine learning (ML) methods that have already revolutionized computer vision and language translation. However, capturing this opportunity is contingent on a paradigm shift in data-intensive scientific discovery since the “black box” use of ML often leads to serious false discoveries in scientific applications.  Because the hypothesis space of scientific applications is often complex and exponentially large, an uninformed data-driven search can easily select a highly complex model that is neither generalizable nor physically interpretable, resulting in the discovery of spurious relationships, predictors, and patterns. This problem becomes worse when there is a scarcity of labeled samples, which is quite common in science and engineering domains.

This talk makes the case that in real-world systems that are governed by physical processes, there is an opportunity to take advantage of fundamental physical principles to inform the search of a physically meaningful and accurate ML model.  While this talk will illustrate the potential of the knowledge-guided machine learning (KGML) paradigm in the context of environmental problems (e.g., Ecology, Hydrology, Agronomy), the paradigm has the potential to greatly advance the pace of discovery in a diverse set of discipline where mechanistic models are used, e.g., weather forecasting, and pandemic management.

Biography

Dr. Vipin Kumar is a Regents Professor and holds William Norris Chair in the department of Computer Science and Engineering  at the University of Minnesota.  His research spans data mining, high-performance computing, and their applications in Climate/Ecosystems and health care.  He also served as the Director of Army High Performance Computing Research Center (AHPCRC) from 1998 to 2005.  He has authored over 400 research articles, and co-edited or coauthored 11 books including two widely used text books ``Introduction to Parallel Computing", "Introduction to Data Mining", and a recent edited collection, “Knowledge Guided Machine Learning”. Kumar's current major research focus is on knowledge-guided machine learning and its applications  to understanding the impact of human induced changes on the Earth and its environment. Kumar’s research on this topic is funded by NSF’s AI Instititues, BIGDATA, INFEWS, STC, GCR, and HDR programs, as well as ARPA-E, DARPA, and USGS.   He has recently finished serving as the Lead PI of a 5-year, $10 Million project, "Understanding Climate Change - A Data Driven Approach", funded by the NSF's Expeditions in Computing program.   Kumar is a Fellow of the AAAI, ACM, IEEE, AAAS, and SIAM.  Kumar's foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society's highest awards in high performance computing, and Test-of-time award from 2021 Supercomputing conference (SC21).