In the Biomedical and Healthcare Engineering group, we improve health through advancements in bioengineering and biomechanical design, and through operational and human factors-based improvements to the way healthcare is delivered.
Research topics for the Control in Biomedical Systems Lab include:
- Math models of the human thyroid.
- Optimal dosing of radioactive iodine in Graves’ disease.
- Pharmacokinetic/pharmacodynamics model of erythropoiesis.
- Design of anemia management protocols in end-stage kidney disease.
Diseases prediction and prevention analysis is a challenging mathematical problem because it is the outcome of a complex dynamical system that consists of interactions between multiple factors related to epidemiological, social, economical, environmental, population mobility, demographics, and individual behavioral and lifestyle. Disease prevention and intervention decisions, and subsequently resource allocation, at the national and global levels thus need to be based on evaluations of the impact of alternative decisions under this complex dynamical context. Our lab works on development of new methodologies and computational models for simulating the dynamics of disease incidence and spread for purposes of disease prediction, prevention, and control.
We use eye-tracking approaches to analyze how physicians and nurses interact with health information technology. We are able to see what information they pay attention to, and ignore, as they make clinical decisions or complete processes. These findings can be used to guide the redesign of electronic health record systems and related technologies. Read More...
The Human Robot Systems (HRS) Laboratory is part of the Department of Mechanical and Industrial Engineering at the University of Massachusetts Amherst.
Our mission is to advance how humans and robots learn to guide the physical interactive behavior of one another. To achieve this, our research aims to:
(1) develop new methods of describing human motor behavior that are compatible for robot control,
(2) understand and improve how humans learn models of robot behavior, and
(2) develop robot controllers that are compatible for human-robot physical collaboration.
This highly interdisciplinary research lies at the intersection of robotics, dynamics, controls, human neuroscience, and biomechanics.
The start of Intelligent Sensing Lab includes three key areas:
1. Machine design (flexible electronics printer and medical device);
2. Control (Sensing, metrology, pattern analysis, feedback control)
3. Machine intelligence (Machine vision, image processing, deep learning)
Interdisciplinary interface engineering, such as：
Our laboratory studies the interaction between fluid flow and biology, by integrating fluid dynamic engineering, cellular and molecular biology. Body fluids or biofluids, such as blood, lymph, and cerebrospinal fluid continuously interact with cells in the body eliciting biochemical and physical responses. Our research seeks to elucidate the fluid flow characteristics and fluid flow-dependent biomolecular pathways relevant in medicine.
Our research applies and integrates fundamental engineering principles, such as manufacturing, biomechanics, materials science, and micro/nanoengineering, to understand and harness the mechanobiology of stem cells for modeling currently incurable human diseases and for applications in regenerative medicine. Current research interests include:
- Stem cell bioengineering
- System mechanobiology
- Active biomaterials
- Tissue biomechanics
In the MRRL our research focuses on developing human-centered robotic technologies for augmenting human gait and balance and exploring physical human-machine interfaces. The 1000 sq. ft. of lab space is dedicated to the fabrication and evaluation of physically interactive mechatronic systems.
Disease — a threat that is common to all human beings across the globe and across generations. Prediction of diseases is a tough problem because it is the outcome of a complex dynamical system that consists of interactions between multiple factors related to epidemiological, social, economical, environmental, population mobility, demographical, and individual behavioral and lifestyle. Disease prevention and intervention decisions, and subsequently resource allocation, at the national and global levels thus need to be based on evaluations of the impact of alternative decisions under this complex dynamical context. Our lab works on development of new methodologies and computational models for simulating the dynamics of disease incidence and spread for purposes of disease prediction, prevention, and control.
Our primary research interests are broadly in operations research applied to healthcare delivery. Some examples: I've worked on planning and scheduling of surgical suites; designing primary care physician panels to maximize timeliness and patient-physician continuity; and optimization of prostate cancer screening decisions. We have also recently begun looking at improving emergency room operations. In addressing these problems, we collaborate with a diverse set of...Read More
Our group studies the interaction of non-ionizing energy and biology at multiscale resolution, from cells through organ systems. Computer simulations are used to model biological response evoked upon exposure to energy and to design energy parameters that improve the specificity of the desired treatment effect. We develop novel medical devices for energy delivery in vitro and in vivo, allowing identification of signaling pathways and cellular activity that is altered or upregulated upon energy-based treatment. Our techniques allow the targeted modulation of barrier function in the tissue microenvironment (cell membrane, stroma and blood vessels), creating new platforms for the study of cancer and other diseases. The knowledge gained from our experiments has applications in tumor ablation, drug delivery, immunotherapy and tissue engineering. We emphasize rapid translation of our findings to the clinic through collaboration and involving physicians and other key stakeholders at all stages of our research.