1976 — 1981 |
Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Research Initiation - the Application of Harmonic Analysis Bilinear Approximations to Nonlinear Estimation Theory @ University of Texas At Austin |
0.954 |
1978 — 1979 |
Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Public Health and Nuisance Aspects of Community Wastewater Sludge Management @ Energy Resources Company Inc |
0.915 |
1981 — 1984 |
Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Lie Algebraic Methods in Nonlinear Estimation @ University of Texas At Austin |
0.954 |
1984 — 1991 |
Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Stochastic Adaptive Estimation and Control @ University of Texas At Austin
This research concerns several basic problems in the area of stochastic adaptive estimation and control. The adaptive control problem (with estimation incorporated) is that of designing a feedback controller (algorithm) which minimizes a given performance criterion or tracks some given reference signal when the process model contains unknown (due to modeling difficulties) or changing (due to wear, temperature variations, etc.) parameters. The adaptive control algorithm in the feedback loop attempts to actively reduce the parameter uncertainty by performing on line parameter estimation and then using these updated estimates to continuously reconfigure or retune the controller. The proposed research represents innovative approaches toward the adaptive estimation and control of systems which are nonlinear and/or involve incomplete state observations. The problems being considered are more difficult than problems solved to date. In particular, this research should result in a deep understanding of the adaptive estimation and control of Markov chains with incomplete observations; such problems arise in the control of computer communication networks, as well as in quality control, maintenance, replacement, and repair of industrial processes. A thorough investigation of the adaptive control of stochastic bilinear systems is also proposed; such dynamic systems contain terms which are bilinear in the state and the control. Professor Marcus is well known for his work on stochastic control. The University of Texas at Austin provides a fine research environment for this project. This award renews research previously supported under NSF Grant ECS-8412100.
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0.954 |
1988 — 1996 |
Baras, John (co-PI) [⬀] Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Engineering Research Center For Systems Research @ University of Maryland College Park
The Systems Research Center, an Engineering research Center at the University of Maryland and Harvard University is pursuing theoretical and experimental studies and educational programs in systems aspects of manufacturing, communications and signal processing, chemical process systems, intelligent servomechanisms, and expert systems and parallel computer architectures. New approaches to optimization-based design of engineering systems have been developed in the intelligent servomechanisms area, and these have been found useful in several of the other thrust areas. In the coming period, the Center will build on its significant accomplishments in each of these areas, and will continue the process of integration of project efforts across all of the research areas. This action is a five-year renewal.
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0.954 |
1988 — 1992 |
Busch-Vishniac, Ilene Marcus, Steven Masada, Glenn (co-PI) [⬀] Beaman, Joseph (co-PI) [⬀] Buckman, A. Bruce |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Magnetically Levitated Micro-Robots @ University of Texas At Austin
This research will entail the design of magnetically levitated micro- robots which are capable of submicron-level six-degree-of-freedom motion over large ranges. Micro-robots are microelectromechanical devices capable of repeatable, precise automated, micron-level motions. They potentially have great application in such technologies as the construction of hybrid devices with associated semiconductor devices. The magnetic levitation approach was selected because magnetically-levitated systems can function in harsh environments and minimize friction effects and the associated problems of fine particle matter generation. Additionally, such systems can be designed to provide movement to absolutely defined locations so that motion errors do not compound. The research will entail four major paths of study: development of analytical tools which may be used for design of magnetic levitation microbiotic systems, development of a sensor system suitable for a levitation system at least four degrees of freedom, design of a macro/micro-robot pair system which permits high precision motion over a wide range, and demonstration of the system performing a variety of tasks.
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0.954 |
1994 — 1997 |
Shayman, Mark [⬀] Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Supervisory Control Design of Nondeterministic Systems @ University of Maryland College Park
9312587 Shayman The proposed research project deals with supervisory control of nondeterministic discrete event systems. Discrete event systems are systems which involve quantities which take on a discrete set of values and which are constant except at discrete times when events occur in the system. Examples include communication networks, intelligent vehicle highway systems, manufacturing systems and computer programs. Supervisory control theory was developed to provide a mathematical framework for the design of controllers for such systems in order to meet various qualitative constraints. A research program on the supervisory control of nondeterministic systems will be undertaken. Supervisory control of both untimed and timed systems will be studied under complete as well as partial observation. Centralized as well as decentralized, hierarchical and modular control techniques will be developed. Efficient computational techniques to verify the existence of supervisors, to synthesize them when they exist and to synthesize minimally restrictive supervisors will be obtained. Both off-line and on-line computational techniques will be considered. The results will be applied to the problem of integrated management of communication networks. ***
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0.954 |
1994 — 1998 |
Marcus, Steven Rubloff, Gary (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Engineering Research Center For Institute For Systems Research @ University of Maryland College Park
9402384 MARCUS This award supports the Engineering Research Center at the University of Maryland and the Harvard University to focus on the integration and control of complex engineering system for the next five years. The Center will use and expand its well-established industrial collaboration programs to facilitate the rapid transfer of developed system methodologies. This will be achieved through three cross-disciplinary thrust areas: 1) intelligent control systems which involves the design of robust control systems with many sensors and many feedback loops using algorithm and tools for optimization-based design, 2) intelligent signal processing and communication system which involves the modelling, design, and control of wireless and high- speed communication networks, 3) system integration methodology which involves model complexity, architectures for control, and communication systems. In addition, there are three demonstration projects: (1) electromechanical motion control prototyping project which will develop basic research and engineering innovation needed to build high-performance, low-cost, motion control system, (2) wire- less multimedia shop communication project which will develop the research and technology for designing multimedia communication subsystems for manufacturing, (3) virtual factories for electro- mechanical devices manufacturing project which will develop an integrated tool kit for design, planning, and manufacturing of electromechanical devices. This award provides support for the ERC for three years. ***
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0.954 |
1997 — 2001 |
Krishnaprasad, P. [⬀] Carr, Catherine (co-PI) [⬀] Marcus, Steven Shamma, Shihab (co-PI) [⬀] Takahashi, Terry (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Learning and Intelligent Systems: Learning Binaurally-Directed Movement @ University of Maryland College Park
9720334 Krishnaprasad The goal of this research project is to investigate time coding in the central nervous system, specifically the auditory system of the barn owl, the early development of such codes, the learning of associated maps, and the exploitation of such sound codes and maps in source localization and sound separation. The approach consists of electrophysiological and anatomical study, coupled with mathematical modeling of neural circuitry, the rigorous investigation of the structure and performance of relevant learning algorithms and the creation of an experimental robotic testbed. This testbed, a binaural head, is intended to be capable of orienting itself to sound sources in complex acoustic environments through pure auditory servoing, by utilizing the development of control architectures capable of learning maps of the auditory space of the robot, and drawing upon an evolving understanding of barn owl auditory system. The results of this research will provide insights into the design of novel roles for auditory sensing, interpretation and discrimination in autonomous robotic systems. This research could lead to applications in hands-free human-machine communications in acoustically cluttered environments and in monitoring complex environments such as highly automated manufacturing plants.
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0.954 |
2000 — 2004 |
Marcus, Steven Fu, Michael |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
New Simulation-Based Approaches to Solving Markov Decision Processes @ University of Maryland College Park
The research to be performed will develop simulation-based algorithms for numerical solution of Markov Decision Processes (MDPs), which can be used to model complex systems in manufacturing, telecommunications, and finance. Two new approaches that offer potential benefits not found in currently available methods will be explored. The first approach will use ordinal optimization (OO) for choosing actions in the backwards induction step for finite horizon problems, or in the policy iteration or value iteration step for infinite horizon problems. The second approach will use simultaneous perturbation stochastic approximation (SPSA) for optimizing high-dimensional parameterized MDPs.
If successful, the results of the research will lead to dramatically more efficient algorithms for solving MDPs of practical interest in a number of application areas, from financial engineering to production systems. The impact of successfully applying high-dimensional solution methodologies to these problems would represent a major advance in developing computationally tractable methods for solving complex problems of sequential decision making under uncertainty. Furthermore, theoretical results are envisioned that would rigorously establish faster rates of convergence for the new algorithms over convergence rates from usual Monte Carlo simulation.
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0.954 |
2002 — 2004 |
Goldsman, Neil [⬀] Marcus, Steven Orloff, Jon (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
A Multidisciplinary Integrated Capstone Design Curriculum For Electrical and Computer Engineering @ University of Maryland College Park
PROPOSAL NO.: 0230628 PRINCIPAL INVESTIGATOR: Goldsman, Neil INSTITUTION NAME: University of Maryland College Park TITLE: A Multidisciplinary Integrated Capstone Design Curriculum for Electrical and Computer Engineering
Abstract
The Department of Electrical and Computer Engineering (ECE) at the University of Maryland at College Park (UMCP) is in the process of planning a major curriculum revision for its undergraduate program. This planning grant will address the Capstone design program, which will be greatly revised to provide a multidisciplinary design experience for senior students and to integrate juniors and sophomores into the Capstone experience. The team will assess the outcomes of the Capstone experience to generate data to guide the revision of our curriculum in general.
A major objective of the Capstone design courses is for the students to work on a design project at a professional level. The project makes use of most of the knowledge that students have acquired in the previous two to three years of the ECE curriculum. Therefore, the outcome of the Capstone design experience should be an excellent indicator of the how well the education process in the ECE Department is preparing students for their professional career. A year-to-year assessment of the outcomes of the Capstone design courses will enable us to make continuous improvements in the sophomore, junior, and also the senior year curricula. This being the case, the revision of the Capstone program will serve as the engine to drive curriculum revision for the entire ECE program.
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0.954 |
2002 — 2008 |
Marcus, Steven Liu, K. J. Ray |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Reu Site: Research Internships in Telecommunications Engineering @ University of Maryland College Park
This award provides funding to the University of Maryland-College Park for the support of a five-year, REU Site " Research Internships in Telecommunications Engineering-RITE Site," under the direction of Dr. Steven I. Marcus. This eleven-week summer program will involve twenty-two students annually in a diverse research experience on a wide range of topics in the field of Telecommunications including communications systems and theory, networking, signal processing, multimedia technology, information security, and neuromorphic engineering. Each student will work in small groups (2-3 students) with faculty members and graduate students. In addition to the fundamental aspects of engineering disciplines, aspects of existing programs that combine engineering and business perspectives will be incorporated into the RITE students' experiences. The students will participate in industrial site visits and interactions and will work closely with graduate research assistant mentors throughout their summer research experience.
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0.954 |
2003 — 2007 |
Marcus, Steven C |
K01Activity Code Description: For support of a scientist, committed to research, in need of both advanced research training and additional experience. |
Understanding Medical Errors in Psychiatry @ University of Pennsylvania
DESCRIPTION (provided by applicant): The proposed K01 Mentored Research Scientist Award outlines a program of training and research focusing on the epidemiology and prevention of medical errors in the treatment of patients with serious mental illness. The candidate proposes to gain the skills and knowledge necessary to become an independent mental health services researcher with expertise in medical errors in behavioral health settings. The research agenda will consist of three related research projects. The first project will be an intensive qualitative assessment of the factors that underlie medical errors for patients with mental illnesses. The second project will be conducted in conjunction with the Center of Excellence for Patient Safety Research and Practice at the University of Pennsylvania. It will examine hospital workplace stressors and physician reactions to those stressors to begin to understand factors associated with medication errors that occur for inpatients with mental illness. The third project will develop and test a medical chart abstraction form to measure and describe medical errors in the psychiatric inpatient setting. Together, these research studies will provide empirical data to develop a conceptual model of the common types and causes of medical errors in patients with mental illness. The educational component of the program will provide advanced training in four general areas: 1) clinical treatment of patients with mental illness; 2) qualitative methods; 3) system design; and 4) organizational behavior. This training will be obtained via mentoring by Drs. Phyllis Solomon and J. Sanford Schwartz, formal didactic coursework, and off-site seminars and conferences. The goal of the educational program is to increase the methodological rigor of the proposed research and place the findings in a broader theoretical context.
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0.911 |
2004 — 2008 |
Marcus, Steven Fu, Michael |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
New Computational Approaches For Markov Decision Processes @ University of Maryland College Park
Developing practical computational solution methods for large-scale Markov Decision Processes (MDPs), also known as stochastic dynamic programming problems, remains an important and challenging research area. The complexity of many modern systems that can in principle be modeled using MDPs have resulted in models for which it is not possible to explicitly enumerate the transition probabilities, but for which sample paths can be easily generated, e.g., via a stochastic simulation model. The project research addresses two other distinct but crucial issues that arise: how best to allocate a computational budget that is used to generate sample paths, and how to produce a robust set of good policies directly (rather than indirectly via value function approximations). In particular, the main thrusts of our proposed approaches center on two distinct paradigms: effective sampling-based methodologies using multi-armed bandit models and induced correlation for value function estimation; and population-based approaches for finding improving policies, in contrast to the traditional policy iteration method, which iterates on a single policy. The latter thrust will focus on infinite horizon problems, where there is assumed an optimal stationary policy, whereas the former approaches are intended for finite horizon problems, where backwards induction dynamic programming must be employed. Algorithms will be developed and then analyzed in terms of their properties such as convergence rate and theoretical bounds on performance, followed by testing on specific application areas to investigate their practical utility. Specific problem domains include the pricing of American-style financial derivatives; capacity planning and preventive maintenance in manufacturing systems; and communication networks.
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0.954 |
2009 — 2013 |
Marcus, Steven Fu, Michael |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Particle Filtering For Stochastic Control and Global Optimization @ University of Maryland College Park
Proposal Number: 0901543
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Objective
The objective of this program is to provide new breakthroughs in the areas of stochastic control and global optimization through insights gained from particle filtering and from additional recent results in nonlinear filtering. With a focus on applying the particle filtering methodology, the proposed research will result in (i) new computationally efficient algorithms for continuous-state partially observable Markov decision processes and global optimization, and (ii) rigorous analysis of the algorithms through the development of bounds and convergence proofs. In particular, for global optimization problems, the particle filtering framework can prove transformative by providing a firm analytical basis for understanding why algorithms work well, when algorithms break down, how to compare algorithms, which algorithm works better than the others for a specific problem, and how to develop new algorithms that should work well for particular problems.
Intellectual merit
Partially observable stochastic control and global optimization are areas with many theoretical challenges and many potential applications. To attack difficult problems of a size that are found in most applications will require significant new methodologies. The proposed approach based on particle filtering will provide new algorithms and rigorous analytical justification beyond that available with other methods.
Broader impacts
Stochastic control and optimization can be applied to many problems of critical concern in US industry, so the resulting algorithms will have broad and transformative applicability. In the project, they will be tested on problems in industries from telecommunications to manufacturing to finance. The project will closely integrate the training of PhD students.
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0.954 |
2009 — 2015 |
Marcus, Steven Cleaveland, W. Rance Wu, Tong Tong (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Next-Generation Model Checking and Abstract Interpretation With a Focus On Embedded Control and Systems Biology @ University of Maryland College Park
"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."
Summary: Formal Analysis of Complex Systems
A Collaborative Proposal Involving CMU, CUNY, NYU, Stony Brook, UMD, Cornell, JPL
This Expedition, under the directorship of Lead PI Edmund M. Clarke, will develop new computational tools to help scientists and engineers analyze and understand the behavior of the complex models they develop for application domains ranging from systems biology to embedded control. Building on the success of model checking and abstract interpretation (MCAI), two well-established methods for automatically verifying properties of digital circuit designs and embedded software, this research project will extend the MCAI paradigm to systems with complex continuous dynamics and probabilistic behaviors. Challenge problems providing technology drivers and testbeds for the research include: understanding the precursors and course of pancreatic cancer; predicting the onset of atrial fibrillation; and obtaining deep design-time insights into the behavior of automotive and aerospace control systems. Ultimately, this Expedition is expected to provide vital tools that will enable health-care researchers to discover better treatments for disease and will allow engineers to build safer aircraft and other complex systems.
The world-class team of scientists and engineers assembled for this Expedition includes two Turing Award winners, a recipient of the National Medal of Science, and awardees of other prestigious research prizes. Outreach consists of the development of a new, highly ambitious and highly cross-discipline educational program called Complex Systems Science Engineering, an annual Minority-Focused Intersession Workshop for Undergraduates on Understanding and Analyzing Complex Embedded and Biological Systems to be hosted at member institution Lehman College, CUNY; substantial financial support for undergraduate research; student involvement in the NASA JPL Research Affiliates Program; and other research opportunities for undergraduate and graduate students and postdoctoral trainees.
More information: http://www.mcai2.org/
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0.954 |
2009 — 2015 |
Fu, Michael Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Combining Gradient and Adaptive Search in Simulation Optimization @ University of Maryland College Park
"Combining Gradient and Adaptive Search in Simulation Optimization"
This research project aims to make significant theoretical and practical advances in simulation optimization. Specifically, we plan on doing the following: (i) develop new simulation optimization algorithms based on different sequences of the so-called ``reference distributions" in a recently developed approach called model reference adaptive search, and new hybrid global-local search algorithms integrating local gradient search and problem structure; and (ii) conduct rigorous theoretical analysis of the resulting algorithms, both finite-time behavior using an adaptive search framework and asymptotic behavior using a novel connection to stochastic approximation methods. We will also develop efficient computational selection methods for implementing these algorithms in simulation optimization, where the objective function requires multiple simulation replications, which are computationally expensive, in order to estimate system performance. A wide variety of applications from supply chain management to financial engineering will be tested for the purposes of investigating specific gradient search algorithms and problem structure, and evaluating the effectiveness in terms of empirical behavior.
Simulation is used throughout the US industry, so if successful, the resulting optimization algorithms will have broad practical applicability. To attack difficult problems arising from large, complex stochastic discrete-event simulation models will require significant new methodologies, leading to research advances in both algorithmic development and convergence analysis. In terms of theory, the rigorous analysis will explore connections to a rich body of results in stochastic approximation and stochastic adaptive research that have never been employed in this manner before, yielding new insights into both finite-time performance and asymptotic rates of convergence. In terms of practice, this line of research fills an important part of the "analytics" computational tool kit that has led to increased competitiveness for US businesses from manufacturers and retailers with global supply chains to financial services managing complex risk factors.
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0.954 |
2010 — 2013 |
Marcus, Steven C |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Patient Safety in Inpatient Psychiatry @ University of Pennsylvania
DESCRIPTION (provided by applicant): Reducing errors and adverse events have become a central focus of the health care system over the last two decades. However, patients with mental disorders have been systematically excluded from this research. As a result, the epidemiology of patient safety events (adverse events and errors) in hospital based mental health services remains unknown. The current application seeks to assess the incidence, nature and preventability of patient safety events via a record review of 11,000 Medicaid patient medical charts in a random sample of 38 inpatient psychiatric units of general hospitals in Pennsylvania. We will supplement this information with detailed surveys of unit leadership to define the patient, provider, and psychiatric unit/hospital factors that influence, contribute to, and/or protect against the commission of adverse events and/or errors. In-depth qualitative interviews with key informants from ten hospitals will be used to help interpret these quantitative findings and more deeply understand the mechanisms by which patient, provider and unit factors interact and contribute to cause harm and error with an eye towards intervention development. PUBLIC HEALTH RELEVANCE: Advancing an epidemiological approach to patient safety for inpatient mental health care is integral to a public health agenda that uses empirical evidence for improving hospital- based care. Our findings will have significant policy and practice implications with regard to targeting opportunities and strategies to prevent medical errors and adverse events. Such interventions have the potential to impact over a million patients discharged from inpatient psychiatric units each year and enhance the overall safety and quality of care for this vulnerable population.
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0.911 |
2014 — 2017 |
Cleaveland, W. Rance Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cps: Breakthrough: Compositional Modeling of Cyberphysical Systems @ University of Maryland College Park
Title: CPS: Breakthrough: Compositional Modeling of Cyberphysical Systems
This project is devoted to the discovery of new mathematical modeling techniques for Cyber-Physical Systems. In particular, the research involves devising novel conceptual methods for assembling systems from subsystems, and for reasoning about the behavior of systems in terms of the behavior of their subsystems, which may be computational or physical. The results enable scientists and engineers to develop more realistic models of the systems they are designing, and to obtain greater insights into their eventual behavior, without having to build costly prototypes. The intellectual merits are the new notions of system behavior being developed that unify the computational and the physical, and the mathematical operators and laws governing the relationships between systems and subsystems. The project's broader significance and importance lie in the increased pace of innovation within Cyber-Physical System design that the new modeling techniques make possible, and the curricular enhancements that the novel conceptual frameworks under development support.
The specific research program of this project involves the development of a novel modeling paradigm, Generalized Synchronization Trees (GSTs), into a rich framework for both describing Cyber-Physical Systems (CPSs) and studying their behavior under interconnection. GSTs are inspired by Milner's use of Synchronization Trees (STs) to model interconnected computing processes, but GSTs generalize the mathematical structure of their forebears in such a way as to encompass systems with discrete ("Cyber") as well as continuous ("Physical") dynamics. As Milner did with STs, the PIs are developing an algebraic theory of composition for GSTs. Such theories have a particular advantage over non-algebraic ones: because the composition of two (or more) objects results in an object of the same type, composition operators can be nested to build large structures out of smaller ones. Thus, the theory of GSTs is inherently compositional. The development of the theory involves five distinct but complementary endeavors. Standard models for cyber-physical systems are being encoded as GSTs in a semantically robust way; meaningful notions of composition and congruence for CPSs are being described and studied algebraically; the interplay between behavioral equivalence and the preservation of system properties is being investigated; a notion of real-time (or clock time) is under consideration for GSTs; and GSTs are being assessed as modeling tools for practical design scenarios.
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0.954 |
2014 — 2017 |
Fu, Michael Marcus, Steven |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
A New Approach to Nonconvex Risk-Sensitive Stochastic Optimization @ University of Maryland College Park
The research objective of this award is to develop a new framework for incorporating risk into sequential decision making under uncertainty. The two pillars of the approach are cumulative prospect theory and dynamic risk measures. The framework builds on both of these research streams to formulate a single theory that integrates subjective preferences in human behavior with normative decision-making objectives. Existing utility-based dynamic models cannot handle the nonconvexity implied by the behavioral models of prospect theory, whereas the framework allows the probability weighting found in cumulative prospect theory to be combined with the usual outcome weighting of traditional expected utility formulations in a sequential decision-making model that incorporates both types of risk sensitivity. The framework will be used to develop efficient dynamic programming sampling and simulation-based methods for risk-sensitive optimization and control problems, and to investigate how the new modeling of risk-sensitivity affects the behavior of decision makers.
If successful, the results of this research will provide an alternative framework for decision making under risk to currently existing approaches. The framework unifies the predominantly descriptive research stream of prospect theory coming primarily from psychology and behavioral economics with the normative approaches generally associated with the microeconomics and operations research communities. From this new approach arise a host of challenges, both theoretical and computational. Algorithms will be developed that can be used to address practical operational and tactical decision-making problems arising in a wide variety of application areas, from manufacturing and supply chain management to service systems, including health care, transportation, and financial engineering.
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0.954 |
2016 — 2020 |
Marcus, Steven C Olfson, Mark |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Improving the Emergency Department Management of Deliberate Self-Harm @ University of Pennsylvania
? DESCRIPTION (provided by applicant): Preventing suicide is one of the great public health challenges facing the US health care system. People who seek emergency care in general hospitals for deliberate self-harm are at exceptionally high short-term risk of repeated self-harm and suicide. Yet only about one-half of these patients receive emergency mental health evaluations. Among self-harm patients who are discharged to the community, only one-half receive follow-up outpatient mental health care in the following month. The proposed study will inform efforts to address deficiencies in the emergency management of deliberate self-harm by testing whether access to each of the following five emergency services influences inpatient admission, timely outpatient mental health care, short- term risk of repeated self-harm and suicide: 1) suicide risk assessment and triage procedures, 2) routine use of safety plans, 3) an on-site discharge planner, 4) an on-site or on-call mental health specialist, and 5) availability o crisis mental health services. The specific aims of the study are 1) to identify patient, hospital and service environment characteristics that influence access to these five emergency mental health services; 2) to determine whether access to each of these emergency mental health services influences inpatient admission, increases the likelihood of timely outpatient mental health follow-up care and reduces the short-term risk of deliberate self-harm and suicide, and 3) to understand qualitatively how these emergency mental health services operate in community practice. We will address these aims by extracting a sample of over ten thousand privately and Medicaid insured deliberate self-harm patients from approximately 500 emergency departments. At each of the treating hospitals, we will survey emergency medical directors to determine the presence or absence of the five emergency department services. Additional information about the emergency departments will be available from the Statewide Emergency Department Databases, hospital information will be available from the American Hospital Association Annual Survey, and regional mental health service information will be available from the Substance Abuse and Mental Health Services Administration surveys. Repeated deliberate self-harm will be assessed with administrative claims records and suicide will be determined by matching individual patients to the National Death Index. We will then use this patient, emergency department, hospital, and service environment information in propensity score adjusted models of the effects of the key emergency services on inpatient admission, follow-up outpatient mental health care, early repeated deliberate self-harm, and suicide. These issues will be examined in greater detail through qualitative interviews with key front line staff at selected emergency departments to probe how these services operate and what impedes their implementation in community practice. This new information will guide improvements in the emergency management of deliberate self-harm.
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0.911 |
2017 — 2019 |
Marcus, Steven C |
P50Activity Code Description: To support any part of the full range of research and development from very basic to clinical; may involve ancillary supportive activities such as protracted patient care necessary to the primary research or R&D effort. The spectrum of activities comprises a multidisciplinary attack on a specific disease entity or biomedical problem area. These grants differ from program project grants in that they are usually developed in response to an announcement of the programmatic needs of an Institute or Division and subsequently receive continuous attention from its staff. Centers may also serve as regional or national resources for special research purposes. |
Incentivizing Evidence Based Antidepressant Medication Treatment of Major Depressive Disorder @ University of Pennsylvania
PROJECT SUMMARY Improving the management of major depressive disorder is one of the great challenges confronting our health care system. Although continuous antidepressant treatment reduces relapse and symptom recurrence, early treatment discontinuation is common and slows recovery. There is a pressing need to develop new, acceptable, and easily implemented strategies to optimize treatment continuity and improve the clinical course of adults with major depressive disorder. In several medical contexts, direct patient financial incentives increase beneficial health behaviors, including medication adherence. Yet uncertainty exists over the durability of behavior change that is effected by financial incentives and the optimal design of such incentives. In the care of major depressive disorder, it may only be necessary to provide incentives early in treatment until the patients' mood begins to lift and treatment adherence becomes self-reinforcing. This pilot study tests two theory different incentive schedules for daily antidepressant adherence. It will compare usual care with either modest time-limited escalating or de-escalating financial incentives with respect to their effects on daily antidepressant adherence and depressive symptoms among non-elderly depressed adults initiating antidepressant treatment. The primary aims are 1) to compare short-term (6 weeks) effects of financial incentives on adherence to antidepressant treatment, depressive symptoms, and quality of life; 2) to determine whether the incentives, which end at 6 weeks, continue at 12 weeks to influence these patient outcomes; and 3) to assess potential negative effects of the incentives on perceived coercion, regret over study participation, trust in the treating psychiatrist, and patient participation in depression care. We will also explore whether present bias and risk aversion moderate the effectiveness of the two incentive schedules. Adult mental health outpatients ( N -= 120) with major depressive disorder who have been prescribed antidepressants will be randomly assigned to: 1) usual care, 2) usual care and escalating daily rewards for daily antidepressant treatment ($2, $3, $4, $5, $6, and $7 in weeks 1 through 6) or 3) usual care and de-escalating incentives from $7/day to $2/day for each day of antidepressant adherence with weekly decrements. Daily antidepressant adherence will be measured with a wireless electronic pill bottle, patient self-report, and physician orders. Baseline, six week, and twelve week assessments will be performed of depressive symptoms, quality of life, perceived coercion, regret over study participation, trust in the treating psychiatrist, and participation in depression care. The results of this pilot study will yield important new information on cognitive and motivational barriers to antidepressant adherence in major depressive disorder.
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0.911 |
2017 |
Marcus, Steven C Olfson, Mark |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Emergency Department Recognition of Mental Disorders and Short-Term Outcome of Deliberate Self-Harm in Older Adults @ University of Pennsylvania
? DESCRIPTION (provided by applicant): Preventing suicide is one of the great public health challenges facing the US health care system. People who seek emergency care in general hospitals for deliberate self-harm are at exceptionally high short-term risk of repeated self-harm and suicide. Yet only about one-half of these patients receive emergency mental health evaluations. Among self-harm patients who are discharged to the community, only one-half receive follow-up outpatient mental health care in the following month. The proposed study will inform efforts to address deficiencies in the emergency management of deliberate self-harm by testing whether access to each of the following five emergency services influences inpatient admission, timely outpatient mental health care, short- term risk of repeated self-harm and suicide: 1) suicide risk assessment and triage procedures, 2) routine use of safety plans, 3) an on-site discharge planner, 4) an on-site or on-call mental health specialist, and 5) availability o crisis mental health services. The specific aims of the study are 1) to identify patient, hospital and service environment characteristics that influence access to these five emergency mental health services; 2) to determine whether access to each of these emergency mental health services influences inpatient admission, increases the likelihood of timely outpatient mental health follow-up care and reduces the short-term risk of deliberate self-harm and suicide, and 3) to understand qualitatively how these emergency mental health services operate in community practice. We will address these aims by extracting a sample of over ten thousand privately and Medicaid insured deliberate self-harm patients from approximately 500 emergency departments. At each of the treating hospitals, we will survey emergency medical directors to determine the presence or absence of the five emergency department services. Additional information about the emergency departments will be available from the Statewide Emergency Department Databases, hospital information will be available from the American Hospital Association Annual Survey, and regional mental health service information will be available from the Substance Abuse and Mental Health Services Administration surveys. Repeated deliberate self-harm will be assessed with administrative claims records and suicide will be determined by matching individual patients to the National Death Index. We will then use this patient, emergency department, hospital, and service environment information in propensity score adjusted models of the effects of the key emergency services on inpatient admission, follow-up outpatient mental health care, early repeated deliberate self-harm, and suicide. These issues will be examined in greater detail through qualitative interviews with key front line staff at selected emergency departments to probe how these services operate and what impedes their implementation in community practice. This new information will guide improvements in the emergency management of deliberate self-harm.
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0.911 |
2021 |
Marcus, Steven C Olfson, Mark |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Development and Clinical Interpretation of Machine Learning Emergency Department Suicide Prediction Algorithms Using Electronic Health Records and Claims @ University of Pennsylvania
Project Summary Preventing suicide is one of the great public health challenges facing the US health care system. People who seek emergency care for mental health complaints are at high short-term risk of non-fatal suicide events and suicide. Yet identifying high-risk patients is challenging as risk fluctuates in a poorly understood manner. It is especially difficult to evaluate risk in emergency settings, where access to the patient's mental health history is often limited. The proposed project seeks to address this critical knowledge gap by pairing data mining and machine learning methods with rich data sources in order to develop short-term prediction models of non-fatal suicidal events and suicide for patients presenting to EDs with mental health problems. The specific aims of this study are to 1) apply advanced machine learning data analytic techniques to electronic health record (EHR) data to develop a clinically rich description of ED mental health patient characteristics that predict suicide and non-fatal suicidal events over a 90- day follow-up period; 2) use longitudinal and temporal features of EHR and claims data from the 180 days preceding the ED mental health visit to generate clinically interpretable suicide and suicidal event risk scores; and 3) convene ED physicians to enhance model development, clinical interpretability, and utility of a suicide risk assessment clinical decision support tool. We will achieve these aims by leveraging several different sophisticated machine learning analytic methods of existing longitudinal clinical and service use information. We seek to develop point-in-time, short-term risk scores for suicidal symptoms and suicide death and the clinical features that drive that risk that may be used to inform clinical risk assessment and management of patients who present to EDs with mental health complaints. Risk algorithms will be developed and validated using health information from a large combined EHR and claims dataset with over 24 million commercially insured patients, which is linked to the National Death Index. Findings will yield new insights regarding patient-specific risk factors and potential targets for intervention. By drawing on data sources common to most health care systems and using efficient computer algorithms this approach has the potential to develop clinically interpretable suicide risk scores at the point of ED evaluation and following disposition. This will help front- line clinicians focus their efforts on high risk patients during high risk periods to inform intervention decisions about suicide risk.
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0.911 |