Srijan Sen, MD, PhD, is the Frances and Kenneth Eisenberg Professor of Depression and Neurosciences. Dr. Sen’s research focuses on the interactions between genes and the environment and their effect on stress, anxiety, and depression. He also has a particular interest in medical education, and leads a large multi-institution study that uses medical internship as a model of stress.
Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.
Satish Narayanasamy, Ph.D., is Associate Professor in the Electrical Engineering and Computer Science department in the College of Engineering at the University of Michigan, Ann Arbor. Satish’s interests are working at the intersection of computer architecture, software systems and program analysis. His current interests include concurrency, security, customized architectures and tools for mobile and web applications, machine learning assisted program analysis, and tools for teaching at scale.
Omid Dehzangi, PhD, is Assistant Professor of Computer and Information Science, College of Engineering and Computer Science, at the University of Michigan, Dearborn.
Wearable health technology is drawing significant attention for good reasons. The pervasive nature of such systems providing ubiquitous access to the continuous personalized data will transform the way people interact with each other and their environment. The resulting information extracted from these systems will enable emerging applications in healthcare, wellness, emergency response, fitness monitoring, elderly care support, long-term preventive chronic care, assistive care, smart environments, sports, gaming, and entertainment which create many new research opportunities and transform researches from various disciplines into data science which is the methodological terminology for data collection, data management, data analysis, and data visualization. Despite the ground-breaking potentials, there are a number of interesting challenges in order to design and develop wearable medical embedded systems. Due to limited available resources in wearable processing architectures, power-efficiency is demanded to allow unobtrusive and long-term operation of the hardware. Also, the data-intensive nature of continuous health monitoring requires efficient signal processing and data analytic algorithms for real-time, scalable, reliable, accurate, and secure extraction of relevant information from an overwhelmingly large amount of data. Therefore, extensive research in their design, development, and assessment is necessary. Embedded Processing Platform Design The majority of my work concentrates on designing wearable embedded processing platforms in order to shift the conventional paradigms from hospital-centric healthcare with episodic and reactive focus on diseases to patient-centric and home-based healthcare as an alternative segment which demands outstanding specialized design in terms of hardware design, software development, signal processing and uncertainty reduction, data analysis, predictive modeling and information extraction. The objective is to reduce the costs and improve the effectiveness of healthcare by proactive early monitoring, diagnosis, and treatment of diseases (i.e. preventive) as shown in Figure 1.
Luis Ortiz, PhD, is Assistant Professor of Computer and Information Science, College of Engineering and Computer Science, The University of Michigan, Dearborn
The study of large complex systems of structured strategic interaction, such as economic, social, biological, financial, or large computer networks, provides substantial opportunities for fundamental computational and scientific contributions. Luis’ research focuses on problems emerging from the study of systems involving the interaction of a large number of “entities,” which is my way of abstractly and generally capturing individuals, institutions, corporations, biological organisms, or even the individual chemical components of which they are made (e.g., proteins and DNA). Current technology has facilitated the collection and public availability of vasts amounts of data, particularly capturing system behavior at fine levels of granularity. In Luis’ group, they study behavioral data of strategic nature at big data levels. One of their main objectives is to develop computational tools for data science, and in particular learning large-population models from such big sources of behavioral data that we can later use to study, analyze, predict and alter future system behavior at a variety of scales, and thus improve the overall efficiency of real-world complex systems (e.g., the smart grid, social and political networks, independent security and defense systems, and microfinance markets, to name a few).
Muzammil M. Hussain is an Assistant Professor of Communication Studies, and Faculty Associate in the Institute for Social Research at the University of Michigan. Dr. Hussain’s interdisciplinary research is at the intersections of global communication, comparative politics, and complexity studies. At Michigan, Professor Hussain teaches courses on research methods, digital politics, and global innovation. His published books include “Democracy’s Fourth Wave? Digital Media and the Arab Spring” (Oxford University Press, 2013), a cross-national comparative study of how digital media and information technologies have supported the opening-up of closed societies in the MENA, and “State Power 2.0: Authoritarian Entrenchment and Political Engagement Worldwide” (Ashgate Publishing, 2013), an international collection detailing how governments, both democracies and dictatorships, are working to close-down digital systems and environments around the world. He has authored numerous research articles, book chapters, and industry reports examining global ICT politics, innovation, and policy, including pieces in The Journal of Democracy, The Journal of International Affairs, The Brookings Institutions™ Issues in Technology and Innovation, The InterMedia Institute™s Development Research Series, International Studies Review, International Journal of Middle East Affairs, The Communication Review, Policy and Internet, and Journalism: Theory, Practice, and Criticism. His website is mmhussain.net, and he tweets from @m_m_hussain
My interest is in using econometrics, especially Bayesian econometrics, and machine learning methods to infer causality. I tend to work with mostly parametric models of firm and consumer behavior to assess the effectiveness of firm actions. My work spans a variety of industries such as pharmaceuticals, e-commerce, gaming and hi-technology.
Kevin Ward, MD, is Professor of Emergency Medicine in the department of Emergency Medicine in the University of Michigan Medical School.
Dr. Ward is the director of the Michigan Center for Integrative Research in Critical Care (MCIRCC) and a new Medical School-wide innovation program, Fast Forward Medical Innovation. He has successfully developed monitors for measuring tissue oxygenation, volume status, redox potential, coagulation monitoring, image and physiologic signal analysis, and other physiologic parameters leading teams of engineers, basic scientists, and clinicians, bridging the translation gap.
My research group is engaged in fundamental research in the following areas: Statistical learning theory: We are developing theory and algorithms for predictions problems (e.g., learning to rank and multilabel learning) with complex label spaces and where the available human supervision is often weak. Sequential prediction in a game theoretic framework: We are trying to understand the power and limitations of sequential predictions algorithms when no probabilistic assumptions are placed on the data generating mechanism. High dimensional and network data analysis: We are developing scalable algorithms with provable performance guarantees for learning from high dimensional and network data. Optimization algorithms: We are creating incremental, distributed and parallel algorithms for machine learning problems arising in today’s data rich world. Reinforcement learning: We are synthesizing concepts and techniques from artificial intelligence, control theory and operations research for pushing the frontier in sequential decision making with a focus on delivering personalized health interventions via mobile devices. My research group is pursuing and continues to actively search for challenging machine learning problems that arise across disciplines including behavioral sciences, computational biology, computational chemistry, learning sciences, and network science.