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Dr. Hammond’s research focuses on the design and control of adaptive robotic manipulation (ARM) systems. This class of devices exemplified by kinematic structures, actuation topologies, and sensing and control strategies that make them particularly well-suited to operating in unstructured, dynamically varying environments - specifically those involving cooperative interactions with humans. The ARM device design process uses an amalgamation of bioinspiration, computational modeling and optimization, and advanced rapid prototyping techniques to generate manipulation solutions which are functionally robust and versatile, but which may take completely non-biomorphic (xenomorphic) forms. This design process removes human intuition from the design loop and, instead, leverages computational methods to map salient characteristics of biological manipulation and perception onto a vast robotics design space. Areas of interest for ARM research include kinematically redundant industrial manipulation, wearable robotic devices for human augmentation, haptic-enabled teleoperative robotic microsurgery, and autonomous soft robotic platforms.
Dr. Hesketh's research interests are in Sensors and Micro/Nano-electro-mechanical Systems (MEMS/NEMS).
Systems biophotonics: single-molecule biophotonics, super-resolution and advanced optical microscopy, imaging instrumentation and devices.
Director of Translational Clinical Informatics
Dr. Lindsey is interested in developing new imaging technologies for understanding biological processes and for clinical use. In the Ultrasonic Imaging and Instrumentation lab, we develop transducers, contrast agents, and systems for ultrasound imaging and image-guidance of therapy and drug delivery. Our aim is to develop quantitative, functional imaging techniques to better understand the physiological processes underlying diseases, particularly cardiovascular diseases and tumor progression.
The MNM Biotech Lab uses engineering expertise to assist life scientists in the study, diagnosis, and treatment of human disease. By developing better models of the body, we help advance drug discovery, increase understanding of the mechanisms of disease, and develop clinical treatments.
Dr. Yeo’s research in the field of biomedical science and bioengineering focuses on the fundamental and applied aspects of biomolecular interactions, soft materials, and nano-microfabrication for the development of nano-biosensors and soft bioelectronics.
Dr. Young's research is focused on developing control systems to improve prosthetic and orthotic systems. His research is aimed at developing clinically translatable research that can be deployed on research and commercial systems in the near future. Some of the interesting research questions are how to successfully extract user intent from human subjects and how to use these signals to allow for accurate intent identification. Once the user intent is identified, smart control systems are needed to maximally enable individuals to accomplish useful tasks. For lower limb devices, these tasks might include standing from a seated position, walking, or climbing a stair. We hope to improve clinically relevant measures with powered mechatronic devices, including reducing metabolic cost, improving biomechanics and decreasing the time required to perform daily tasks of living.
Rehabilitation engineering, systems-level medical device design, instrumented orthoses, locomotion mechanics, wearable devices
PhD, MBA, Managing Innovation and Technology Commercialization
Medical Device Innovation / Clinically Translational Technology
Connectomics, process optimization, electron microscopy, serial sectioning, microfluidics, capillary interactions, tomography
Biomedical signal and image processing, Computational Neuroscience, Medical robotics
My research interests involve rehabilition and assistive technologies for the augmentation and restoration of human mobility.
Ph.D. Student + R&D Engineer
Neural Engineering + Biomaterials
Wearable physiological monitoring, stretchable/flexible hybrid electronics, MEMS, advanced diagnostics, signal processing and machine learning