My research interests lie broadly in the area of artificial intelligence (AI) for social impact, particularly spanning the fields of multi-agent systems and data science for conservation and public health.
My research spans robotics, computer vision, and machine learning with a focus on enabling autonomy in dynamic, unstructured, or remote environments across field robotics applications (air, land, sea, and space). In particular, my group focuses on problems that rely on limited labeled data.
My research focuses on natural hazards and disaster information, everything from understanding where disaster data comes from, how it’s used, and its implications to design improved disaster information systems that prioritize the human experience and lead to more effective and equitable outcomes.
My lab takes a user-centered and data-driven approach. We aim to understand user needs and the effect of data on users’ decisions through qualitative research, such as focus groups or workshops. We then design new information systems through geospatial/GIS analysis, risk analysis, and statistical modeling techniques. We often work with earth observation, sensor, and survey data. We consider various aspects of disaster information, whether it be the hazard, its physical impacts, its social impacts, or a combination of the three.
I also focus on the communication of information, through data visualization techniques, and host a Risk and Resilience DAT/Artathon to build data visualization capacity for early career professionals.
Our research group studies how and why an organism’s traits (“phenotypes”) evolve in natural populations. Explaining the mechanisms that generate and regulate patterns of phenotypic diversity is a major goal of evolutionary biology: why do we see rapid shifts to strikingly new and distinct character states, and how stable are these evolutionary transitions across space and time? To answer these questions, we generate and analyze high-throughput “big data” on both genomes and phenotypes across the 18,000 species of reptiles and amphibians across the globe. Then, we use the statistical tools of phylogenetic comparative analysis, geometric morphometrics of 3D anatomy generated from CT scans, and genome annotation and comparative transcriptomics to understand the integrated trait correlations that create complex phenotypes. Currently, we are using machine learning and neural networks to study the color patterns of animals vouchered into biodiversity collections and test hypotheses about the ecological causes and evolutionary consequences of phenotypic innovation. We are especially passionate about the effective and accurate visualization of large-scale multidimensional datasets, and we prioritize training in both best practices and new innovations in quantitative data display.
Professor Schwarz is an experimental particle physicist who has performed research in astro-particle physics, collider physics, as well as in accelerator physics and RF engineering. His current research focuses on discovering new physics in high-energy collisions with the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. His particular focus is in precision measurements of properties of the Higgs Boson and searching for new associated physics using advanced AI and machine learning techniques.
Dr. Xhabija joined the Department of Natural Sciences in September 2022 as an Assistant Professor of Biochemistry. Her laboratory aims to understand the effects of toxins on early embryonic development utilizing embryonic stem cells because they provide a new tool and opportunity to investigate the impact of environmental exposures and their interactions with genetic factors on human development and health. To fully realize these potentials, she believes that it is important to understand the molecular basis of the defining characteristic of the stem cells. More specifically, she is interested in investigating how stem cells play a role in shaping the expression program during development and how mechanisms of self-renewal and differentiation during mammalian development regulate cellular fate decisions.
Michael is an Assistant Professor of Energy Systems at the University of Michigan’s School for Environment and Sustainability and PI of the ASSET Lab. He researches how to equitably reduce global and local environmental impacts of energy systems while making those systems robust to future climate change. His research advances energy system models to address new challenges driven by decarbonization, climate adaptation, and equity objectives. He then applies these models to real-world systems to generate decision-relevant insights that account for engineering, economic, climatic, and policy features. His energy system models leverage optimization and simulation methods, depending on the problem at hand. Applying these models to climate mitigation or adaptation in real-world systems often runs into computational limits, which he overcomes through clustering, sampling, and other data reduction algorithms. His current interdisciplinary collaborations include climate scientists, hydrologists, economists, urban planners, epidemiologists, and diverse engineers.
“Neighborhood Environments as Socio-Techno-bio Systems: Water Quality, Public Trust, and Health in Mexico City (NESTSMX)” is an NSF-funded multi-year collaborative interdisciplinary project that brings together experts in environmental engineering, anthropology, and environmental health from the University of Michigan and the Instituto Nacional de Salud Pública. The PI is Elizabeth Roberts (anthropology), and the co-PIs are Brisa N. Sánchez (biostatistics), Martha M Téllez-Rojo (public health), Branko Kerkez (environmental engineering), and Krista Rule Wigginton (civil and environmental engineering). Our overarching goal for NESTSMX is to develop methods for understanding neighborhoods as “socio-techno-bio systems” and to understand how these systems relate to people’s trust in (or distrust of) their water. In the process, we will collectively contribute to our respective fields of study while we learn how to merge efforts from different disciplinary backgrounds.
NESTSMX works with families living in Mexico City, that participate in an ongoing longitudinal birth-cohort chemical-exposure study (ELEMENT (Early Life Exposures in Mexico to ENvironmental Toxicants, U-M School of Public Health). Our research involves ethnography and environmental engineering fieldwork which we will combine with biomarker data previously gathered by ELEMENT. Our focus will be on the infrastructures and social structures that move water in and out of neighborhoods, households, and bodies.
I am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.