Cyber-security is a complex and multi-dimensional research field. My research style comprises an inter-disciplinary (primarily rooted in economics, econometrics, data science (AI/ML/Bayesian and Frequentist Statistics), game theory, and network science) investigation of major socially pressing issues impacting the quality of cyber-risk management in modern networked and distributed engineering systems such as IoT-driven critical infrastructures, cloud-based service networks, and app-based systems (e.g., mobile commerce, smart homes) to name a few. I take delight in proposing data-driven, rigorous, and interdisciplinary solutions to both, existing fundamental challenges that pose a practical bottleneck to (cost) effective cyber-risk management, and futuristic cyber-security and privacy issues that might plague modern (networked) engineering systems. I strongly strive for originality, practical significance, and mathematical rigor in my solutions. One of my primary end goals is to conceptually get arms around complex, multi-dimensional information security and privacy problems in a way that helps, informs, and empowers practitioners and policy makers to take the right steps in making the cyber-space more secure.
My research is focused on the human biometric data (such as motion) to guide the design and manufacturing of assistive and proactive devices. Embedded and external sensors generate ample data which require scientific approaches to analyze and create knowledge. I have worked closely with the University of Michigan Orthotics and Prosthetics Center in the design and manufacturing of custom assistive devices using 3D-printing and cyber-based design. The goal is to create a cyber-physical system that can acquire the data from scanning, sensors, human motion, user feedback, clinician diagnosis into quantitative health metrics and guidelines to improve the quality of care for people with needs.
Dr. Arpan Kusari has joined UMTRI as an Assistant Research Scientist, a position where he will bring his cutting-edge industry experience. Dr. Kusari has spent five years at Ford Motor Company researching exclusively on making autonomous vehicles safe and viable, working collaboratively with researchers from MIT and University of Michigan to advance the state-of-the-art knowledge in autonomous vehicles. His research interest spans through the spheres of sensing and perception; and decision-making and control, in the domain of autonomous vehicles. In the sensing and perception realm, his interests lie in uncertainty quantification and fault tolerance of a generic sensor suite. Dr. Kusari is also interested in utilizing noise reduction methods for designing cost-effective low SNR (signal-to-noise ratio) LiDARS. In decision making and control, he is focused on creating a robust framework capable of handling the uncertainty stemming from other road users’ behavior. In that regard, Dr. Kusari is pursuing development of methods for increasing the efficiency and robustness of probabilistic formalisms such as reinforcement learning and evolutionary algorithms to safely navigate the dynamic environment. His doctoral research was in LiDAR mapping in the areas of sensor calibration, precise estimation of earthquake displacement and uncertainty quantification in the point cloud.
Fred Conrad’s research concerns the development of new methods and data sources for conducting social research. His work is largely focused on survey methodology, but he also explores the use of social media content as a complement to survey data and as a source of large-scale qualitative insights. His focus is on data quality and reducing measurement error. For example, live video interviews promote more thoughtful responses, e.g., less straightlining – the tendency to give the same answer to a battery of survey questions, but they also promote less candor when answering questions on sensitive topics. Measurement error in social media include misclassification in the automated interpretation of content using methods such as sentiment analysis and topic modeling, as well as selective self-presentation (only posting flattering content). Equally challenging is not knowing the extent to which users differ from the population to which one might wish to generalize results.
Anthony Vanky develops and applies data science and computational methods to design, plan, evaluate cities, emphasizing their applications to urban planning and design. Broadly, his work focuses on the domains of transportation and human mobility; social behaviors and urban space; policy evaluation; quantitative social sciences; and the evaluation of urban form. Through this work, he has extensively collaborated with public and private partners. In addition, he considers creative approaches toward data visualization, public engagement and advocacy, and research methods.
Anthony Vanky’s Cityways project analyzed 2.2 million trips from 135,000 people over one year to understand the factors that influence outdoor pedestrian path choice. Factors considered included weather, urban morphology, businesses, topography, traffic, the presence of green spaces, among others.
My areas of interest are control, estimation, and optimization, with applications to energy systems in transportation, automotive, and marine domains. My group develops model-based and data-driven tools to explore underlying system dynamics and understand the operational environments. We develop computational frameworks and numerical algorithms to achieve real-time optimization and explore connectivity and data analytics to reduce uncertainties and improve performance through predictive control and planning.
My research focuses on understanding, designing, and evaluating learning technologies and environments that foster collaborative problem solving, spatial reasoning, engineering design thinking and agency. I am particularly interested in applying multimodal learning analytics in the context of co-located and/or virtually distributed teams in clinical simulations. I strive to utilize evidence in education science, simulation-based training and learning analytics to understand how people become expert health professionals, how they can better work in teams and how we can support these processes to foster health care delivery and health outcomes.
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.
Jeffrey Regier received a PhD in statistics from UC Berkeley (2016) and joined the University of Michigan as an assistant professor. His research interests include graphical models, Bayesian inference, high-performance computing, deep learning, astronomy, and genomics.