Stefanus Jasin

By |

My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.

 

Brendan Kochunas

By |

Dr. Kochunas’s research focus is on the next generation of numerical methods and parallel algorithms for high fidelity computational reactor physics and how to leverage these capabilities to develop digital twins. His group’s areas of expertise include neutron transport, nuclide transmutation, multi-physics, parallel programming, and HPC architectures. The Nuclear Reactor Analysis and Methods (NURAM) group is also developing techniques that integrate data-driven methods with conventional approaches in numerical analysis to produce “hybrid models” for accurate, real-time modeling applications. This is embodied by his recent efforts to combine high-fidelity simulation results simulation models in virtual reality through the Virtual Ford Nuclear Reactor.

Relationship of concepts for the Digital Model, Digital Shadow, Digital Twin, and the Physical Asset using images and models of the Ford Nuclear Reactor as an example. Large arrows represent automated information exchange and small arrows represent manual data exchange.

Lubomir Hadjiyski

By |

Dr. Hadjiyski research interests include computer-aided diagnosis, artificial intelligence (AI), machine learning, predictive models, image processing and analysis, medical imaging, and control systems. His current research involves design of decision support systems for detection and diagnosis of cancer in different organs and quantitative analysis of integrated multimodality radiomics, histopathology and molecular biomarkers for treatment response monitoring using AI and machine learning techniques. He also studies the effect of the decision support systems on the physicians’ clinical performance.

Yasser Aboelkassem

By |

In this project, we use multi-scale models coupled with machine learning algorithms to study cardiac electromechanic coupling. The approach spans out the molecular, Brownian, and Langevin dynamics of the contractile (sarcomeric proteins) mechanism of cardiac cells and up-to-the finite element analysis of the tissue and organ levels. In this work, a novel surrogate machine learning algorithm for the sarcomere contraction is developed. The model is trained and established using in-silico data-driven dynamic sampling procedures implemented using our previously derived myofilament mathematical models.

Multi-scale Machine Learning Modeling of Cardiac Electromechanics Coupling

Multi-scale Machine Learning Modeling of Cardiac Electromechanics Coupling

Elle O’Brien

By |

My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.

Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency

The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.

View MIDAS Faculty Research Pitch, Fall 2021

 

Ayumi Fujisaki-Manome

By |

Fujisaki-Manome’s research program aims to improve predictability of hazardous weather, ice, and lake/ocean events in cold regions in order to support preparedness and resilience in coastal communities, as well as improve the usability of their forecast products by working with stakeholders. The main question Fujisaki-Manome’s research aims to address is: what are the impacts of interactions between ice and oceans / ice and lakes on larger scale phenomena, such as climate, weather, storm surges, and sea/lake ice melting? Fujisaki-Manome primarily uses numerical geophysical modeling and machine learning to address the research question; and scientific findings from the research feed back into the models and improve their predictability. Her work has focused on applications to the Great Lakes, the Alaska’s coasts, Arctic Ocean, and the Sea of Okhotsk.

View MIDAS Faculty Research Pitch, Fall 2021

Areal fraction of ice cover in the Great Lakes in January 2018 modeled by the unstructured grid ice-hydrodynamic numerical model.

Kevin Bakker

By |

Kevin’s research is focused on to identifying and interpreting the mechanisms responsible for the complex dynamics we observe in ecological and epidemiological systems using data science and modeling approaches. He is primarily interested in emerging and endemic pathogens, such as SARS-CoV-2, influenza, vampire bat rabies, and a host of childhood infectious diseases such as chickenpox. He uses statistical and mechanistic models to fit, forecast, and occasionally back-cast expected disease dynamics under a host of conditions, such as vaccination or other control mechanisms.

Niko Kaciroti

By |

Niko Kaciroti is a Research Scientist at the Departments of Pediatrics and Biostatistics. He received his PhD in Biostatistics from the University of Michigan. Since then he has collaborated in multidisciplinary research at the University of Michigan and with researchers from other universities in the United States and internationally. Dr. Kaciroti is a faculty member at the Center for Computational Medicine and Bioinformatics. His main research interest is in using Bayesian models for analyzing longitudinal data from clinical trials with missing values, as well as using Bayesian methods for nonlinear and dynamic models. Dr. Kaciroti is an elected member of the International Statistical Institute and serves as statistical editor for the American Journal of Preventive Medicine and the International Journal of Behavior Nutrition and Physical Activity.

Andrew Brouwer

By |

Andrew uses mathematical and statistical modeling to address public health problems. As a mathematical epidemiologist, he works on a wide range of topics (mostly related to infectious diseases and cancer prevention and survival) using an array of computational and statistical tools, including mechanistic differential equations and multistate stochastic processes. Rigorous consideration of parameter identifiability, parameter estimation, and uncertainty quantification are underlying themes in Andrew’s work.

Christiane Jablonowski

By |

Machine learning approaches and new data science algorithms are an emerging frontier for the atmospheric sciences. We explore whether newly developed physics-guided machine learning algorithms trained with atmospheric model data or observations can serve as emulators for physical processes in weather and climate models, such as the time-consuming solar radiation code, precipitation mechanisms, or the shallow or deep convection cloud schemes. A second, less aggressive approach is to utilize machine learning approaches for the estimation of uncertain parameters in the subgrid-scale physical parameterizations of atmospheric models. We use idealized weather and climate model configurations to intercompare the pros and cons of various machine learning algorithms, such as linear regression, random forests, boosted forests, artificial neural networks and deep neural networks with and without convolutions. In addition, we are interested in machine learning approaches to understand and foster the predictability of the climate system over subseasonal-to seasonal (weeks-to-months) time scales.