Feng Zhou

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For human-machine systems, I first collect data from human users, whether it’s an individual, a team, or even a society. Different kinds of methods can be used, including self-report, interview, focus groups, physiological and behavioral data, as well as user-generated data from the Internet.

Based on the data collected, I attempt to understand human contexts, including different aspects of the human users, such as emotion, cognition, needs, preferences, locations and activities. Such understanding can then be applied to different human-machine systems, including healthcare systems, automated driving systems, and product-service systems.

Based on the different design theory and methodology, from the perspective of the machine dimension, I apply knowledge of computing and communication as well as practical and theoretical knowledge of social and behavior to design various systems for human users. From the human dimension, I seek to understand human needs and decision making processes, and then build mathematical models and design tools that facilitate integration of subjective experiences, social contexts, and engineering principles into the design process of human-machine systems.

Zhixin (Jason) Liu

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My research focuses on quantitative modeling approaches that help business or nonprofit institutions make efficient operational decisions. My research addresses decisions that are made: 1) on either a single independent operation or multiple integrated operations, and 2) by either a single party or multiple parties, most likely different supply chain members. I am specifically interested in the allocation of resources over time and/or among different parties, which often involve scheduling, i.e., the allocation of resources over time to optimize certain objectives, capacity allocation, i.e., the allocation of production capacity from supplier to retailers in a supply chain setting, and pricing, i.e., the determination of selling price of certain products. When multiple parties are involved, decisions can be made either cooperatively or non-cooperatively. The methodologies used in my work include game theory, real analysis, optimization, approximation, simulation, and statistics.

Jaerock Kwon

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My research interests are in the areas of brain-inspired machine intelligence and its applications such as mobile robots and autonomous vehicles. To achieve true machine intelligence, I have taken two different approaches: bottom-up data-driven and top-down theory-driven approach. For the bottom-up data-driven approach, I have investigated the neuronal structure of the brain to understand its function. The development of a high-throughput and high-resolution 3D tissue scanner was a keystone of this approach. This tissue scanner has a 3D virtual microscope that allows us to investigate the neuronal structure of a whole mammalian brain in a high resolution. The top-down theory-driven approach is to study what true machine intelligence is and how it can be implemented. True intelligence cannot be investigated without embracing the theory-driven approach such as self-awareness, embodiment, consciousness, and computational modeling. I have studied the internal dynamics of a neural system to investigate the self-awareness of a machine and model neural signal delay compensation. These two meet in the middle where machine intelligence is implemented for mechanical systems such as mobile robots and autonomous vehicles. I have a strong desire to bridge the bottom-up and top-down approaches that lead me to conduct research focusing on mobile robotics and autonomous vehicles to combine the data-driven and theory-driven approaches.

9.9.2020 MIDAS Faculty Research Pitch Video.

High-Throughput and High-Resolution Tissue Scanner – NSF Funded

Niccolò Meneghetti

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Dr. Niccolò Meneghetti is an Assistant Professor of Computer and Information Science at the University of Michigan-Dearborn.
His major research interests are in the broad area of database systems, with primary focus on probabilistic databases, statistical relational learning and uncertain data management.

Lei Chen

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Lei Chen’s group focus on applying data science tools to advanced manufacturing. Chen’s research expertise and interests are to integrate the physics-based computational and experimental methods and data-driven approaches, to exploit the fundamental phenomena emerged in advanced manufacturing and to establish the design protocol for optimizing the materials and process parameters of as-fabricated parts for quality control. Current research can be summarized by:
1 One of significant challenges in additive manufacturing (AM) is the presence of heterogeneous sources of uncertainty involved in the complex layer-wise processes under non-equilibrium conditions that lead to variability in the microstructure and properties of as-built components. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production, and current practice usually reverts to trial-and-error techniques that are very time-consuming and costly. This research aims to develop an uncertainty quantification framework by bringing together physical modeling, machine-learning (ML), and experiments.
2 Computational microstructure optimization of piezocomposites involves iterative searches to achieve the desired combination of properties demanded by a selected application. Traditional analytical-based optimization methods suffer from the searching efficiency and result optimality due to high dimensionality of microstructure space, complicated electrical and mechanical coupling and non-uniqueness of solutions. Moreover, AM process inherently poses several manufacturing constraints e.g., the minimum feature size and the porosity in the piezoelectric ceramics as well as at the ceramics-polymer interface. It is challenging to include such manufacturing constraints since they are not explicitly available. This research aims to develop a novel data-driven framework for microstructure optimization of AM piezoelectric composites by leveraging extensive physics-based simulation data as well as limited amount of measurement data from AM process.
3 Lithium (Li) and other alkali metals (e.g., sodium and potassium) are very attractive electrode candidates for the next-generation rechargeable batteries that promise several times higher energy density at lower cost. However, Li-dendrite formation severely limits the commercialization of Li-metal batteries, either because dendrite pieces lose electrical contact with the rest of the Li-electrode or because growing dendrites can penetrate the separator and lead to short circuits. This research aims to develop a computational model to accelerate the design of dendrite-free Li-metal batteries.

9.9.2020 MIDAS Faculty Research Pitch Video.

Blueprint for the research: data-driven modelling of additive manufacturing. Stereolithography-based and laser melting-based additive manufacturing processes are used to fabricate the powder-based piezoelectric ceramics and metals respectively, with controllable complex microstructures and/or architectures to tune material properties. Physics-based numerical simulations are performed in an “in-house” multiscale computational framework, which includes macroscopic finite-element based manufacturing process modelling, mesoscopic phase-field modelling of microstructure evolution and design, and first principles/CALPHAD calculation of thermodynamics and kinetics. Data-driven approaches include machine learning and uncertainty quantification with surrogate models, such as polynomial chaos expansion, Gaussian process, radial basis functions, etc.

Zhen Hu

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I am an assistant professor in Department of Industrial and Manufacturing Systems Engineering (IMSE) at the University of Michigan-Dearborn. Prior to joining UM-Dearborn, I was a research assistant professor and postdoctoral research scholar at Vanderbilt University. My research areas of interest are uncertainty quantification, Bayesian data analytics, big data analytics, machine learning, optimization under uncertainty, and applications of data analytics and machine learning in aerospace, mechanical and manufacturing systems, and material science. The goal of my research is to develop novel computational methods to design sustainable and reliable engineering systems by leveraging the rich information contained in the high-fidelity computational simulation models, experimental data, and big operational data and historical data.

Birhanu Eshete

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I study cybercrime using data-driven methods to analyze, characterize, and measure the infrastructure and modus operandi used by criminal activities on the Internet. In particular, I focus on collection, analysis, and semantic characterization of cyber threat intelligence that comes in many shapes and forms (e.g., natural language, network traffic, system audit logs). The ultimate goal is to learn insights that will inform decisions on building robust defense against online criminal activities that involve threats such as ransomware, exploit kits, and botnets. To achieve these goals, I find graph theory and analytics, machine learning (deep learning), longitudinal analysis, and causality inference to be the natural methods. I also study the training and deployment of cyber threat classification/prediction systems in adversarial settings.

From behavioral fingerprinting and detection of cybercrime toolkits to analytics and detection of online cyber threats; from semantic characterization of cyber threat intelligence to detection and forensics of advanced cyber attacks, machine learning, graph theory and analytics, graph isomorphism, and causal inference serve as the core ingredients to build robust defense against cyber threats.

Jin Lu

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Dr. Jin Lu is an Assistant Professor of Computer and Information Science at the University of Michigan, Dearborn.
His major research interests include machine learning, data mining, optimization, matrix analysis, biomedical informatics, and health informatics. Two main directions are being pursued:
(1) Large-scale machine learning problems with data heterogeneity. Data heterogeneity is common across many high-impact application domains, ranging from recommendation system to Computer Vision, Bioinformatics and Health-informatics. Such heterogeneity can be present in a variety of forms, including (a) sample heterogeneity, where multiple resources of data samples are available as side information; (b) task heterogeneity, where multiple related learning tasks can be jointly learned to improve the overall performance; (c) view heterogeneity, where complementary information is available from various sources. My research interests focus on building efficient machine learning methods from such data heterogeneity, aiming to improve the learning model by making the best use of all data resources.
(2) Machine learning methods with provable guarantees. Machine learning has been substantially developed and has demonstrated great success in various domains. Despite its practical success, many of the applications involve solving NP-hard problems based on heuristics. It is challenging to analyze whether a heuristic scheme has any theoretical guarantee. My research interest is to employ granular data structure, e.g. sample clusters or features describing an aspect of the sample, to design new theoretically-sound models and algorithms for machine learning problems.

Fred Feng

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Dr. Feng’s research involves conducting and using naturalistic observational studies to better understand the interactions between motorists and other road users including bicyclists and pedestrians. The goal is to use an evidence-based, data-driven approach that improves bicycling and walking safety and ultimately makes them viable mobility options. A naturalistic study is a valuable and unique research method that provides continuous, high-time-resolution, rich, and objective data about how people drive/ride/walk for their everyday trips in the real world. It also faces challenges from the sheer volume of the data, and as with all observational studies, there are potential confounding factors compared to a randomized laboratory experiment. Data analytic methods can be developed to interpret the behavioral data, make meaningful inferences, and get actionable insights.

9.9.2020 MIDAS Faculty Research Pitch Video.

Using naturalistic driving data to examine the interactions between motorists and bicyclists

Shan Bao

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My research interests are to improve safety associated with motor-vehicle transportation by addressing both active safety (increased crash avoidance) and passive safety (increased crash protection) issues through the development and application of a wide range of research methodologies. These methodologies are targeted at developing a better understanding and modeling of driver behavior, including physical and cognitive attributes, driver decision-making processes and human intention prediction. I am currently interested in applying data science to study the following topics:
*Driver state detection and prediction;

*Improve user intersection with automated vehicle technologies;

*Communication and interaction between vehicle and vulnerable road users

*Driving style classification

*Human factors issues associated with connected and automated vehicle technologies

Where do drivers look when they are not paying attention to the road