Hamid Ghanbari

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My research focuses on using digital health solutions, signal processing, machine learning and ecological momentary assessment to understand the physiological and psychological determinants of symptoms in patients with atrial fibrillation. I am building a research framework for rich data collection using smartphone apps, medical records and wearable sensors. I believe that creating a multidimensional dataset to study atrial fibrillation will yield important insights and serve as model for studying all chronic medical conditions.

Zheng Song

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I received my second PhD in Computer Science (with a focus on distributed systems and software engineering) from Virginia Tech USA in 2020, and the first PhD (with a focus on wireless networking and mobile computing) from Beijing University of Posts and Telecommunications China in 2015. I received the Best Paper Award from IEEE International Conference on Edge Computing in 2019. My ongoing research projects include measuring the data quality of web services and using federated learning to improve indoor localization accuracy.

Susan Hautaniemi Leonard

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I am faculty at ICPSR, the largest social science data archive in the world. I manage an education research pre-registration site (sreereg.org) that is focused on transparency and replicability. I also manage a site for sharing work around record linkage, including code (linkagelibrary.org). I am involved in the LIFE-M project (life-m.org), recently classifying the mortality data. That project uses cutting-edge techniques for machine-reading handwritten forms.

Mortality rates for selected causes in the total population per 1,000, 1850–1912, Holyoke and Northampton, Massachusetts

Matias del Campo

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The goal of this project is the creation of a crucial building block of the research on AI and Architecture – a database of 3D models necessary to successfully run Artificial Neural Networks in 3D. This database is part of the first stepping-stones for the research at the AR2IL (Architecture and Artificial Intelligence Laboratory), an interdisciplinary Laboratory between Architecture (represented by Taubman College of Architecture of Urban Planning), Michigan Robotics, and the CS Department of the University of Michigan. A Laboratory dedicated to research specializing in the development of applications of Artificial Intelligence in the field of Architecture and Urban Planning. This area of inquiry has experienced an explosive growth in recent years (triggered in part by research conducted at UoM), as evidenced for example by the growth in papers dedicated to AI applications in architecture, as well as in the investment of the industry in this area. The research funded by this proposal would secure the leading position of Taubman College and the University of Michigan in the field of AI and Architecture. This proposal would also address the current lack of 3D databases that are specifically designed for Architecture applications.

The project “Generali Center’ presents itself as an experiment in the combination of Machine Learning processes capable of learning the salient features of a specific architecture style – in this case, Brutalism- in order to generatively perform interpolations between the data points of the provided dataset. These images serve as the basis of a pixel projection approach that results in a 3D model.

Stefanus Jasin

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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.

 

Cong Shi

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Cong Shi is an associate professor in the Department of Industrial and Operations Engineering at the University of Michigan College of Engineering. His primary research interest lies in developing efficient and provably-good data-driven algorithms for operations management models, including supply chain management, revenue management, service operations, and human-robot interactions. He received his Ph.D. in Operations Research at MIT in 2012, and his B.S. in Mathematics from the National University of Singapore in 2007.

Negar Farzaneh

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Dr. Farzaneh’s research interest centers on the application of computer science, in particular, machine learning, signal processing, and computer vision, to develop clinical decision support systems and solve medical problems.

Brian Lin

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Dr. Brian Lin has 12 years of experience in automotive research at UMTRI after his Ph.D. His current research is focused on mining naturalistic driving data, evaluating driver assistance systems, modeling driver performance and behavior, and estimating driver distraction and workload, using statistical methods, classification, clustering, and survival analysis. His most recent work includes classifying human driver’s decision for a discretionary lane change and traversal at unsignalized intersections, driver’s response to lead vehicle’s movement, and subjective acceptance on automated lane change feature. Dr. Lin also has much experience applying data analytic methods to evaluate automotive system prototypes, including auto-braking, lane departure, driver-state monitoring, electronic head units, car-following and curve-assist systems on level-2 automation, and lane-change and intersection assist on L3 automation on public roads, test tracks, or driving simulators. He is also familiar with the human factors methods to investigate driver distraction, workload, and human-machine interaction with in-vehicle technologies and safety features. He serves as a peer reviewer for Applied Ergonomics, Behavior Research Methods, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles and Transportation Research Part F.

Brendan Kochunas

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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.