As a professor in bioengineering at UCSD, my research is multi-disciplinary at its core. I attribute the ability to make connections between biology and engineering to my training in Course VI at MIT under the guidance of Prof. Muriel Médard, as well as my postdoctoral training in neuroscience at MIT under the guidance of Prof. Emery Brown. Many of the topics I learned or TA’d while at MIT, and I think that the fundamental perspective that an MIT Course VI training exemplifies enables a researcher to comfortably move from one societally relevant research topic to another.
My research group, the Neural Interaction Laboratory at UCSD, is developing multi-functional, flexible bioelectronics that can be applied with a temporary tattoo, sense the body’s physiological signals, and wirelessly transmit them. The applications of this capability range from transforming the delivery of medicine to sports performance monitoring to immersive gaming experiences. What I find fascinating about this area is its transdisciplinary nature, where the design of one device must incorporate knowledge from electro-neurophysiology, medical instrumentation, mechanics, statistical signal processing, circuits, antenna design, and communications theory.
Many of these subjects I learned while at MIT. I was strongly influenced to evolve my research in this arena because of my experiences with MIT friends doing combined MD/PhDs or PhDs in fields that had a strong biomedical component. MIT’s close connection to hospitals in Boston, along with MIT’s strong traditional emphasis on understanding fundamentals, creates a ‘unique petri dish’ to create inter-disciplinary scientists.
Another research endeavor of my group involves developing a quantitative approach to understand or control, the interaction between biotic and abiotic systems. A couple of examples of this that we are pursuing include:
•Understanding the dynamical relationship betweenmany recorded neural processes in a brain: By treating a single brain as a stochastic system of interacting parts, and observing many neural signals simultaneously, we attempt to understand these dynamics and how they vary with brain function. Along with neuroscience, ideas from control theory, feedback information theory, and machine learning – all of which I learned in course VI – integrate holistically to paint this picture.
One consequence of our insights – and our systems engineering approach – is the demonstration of a robust wave-like phenomenon taking place in the spiking of neurons in the motor cortex that underlies the basis of motor coordination. This could have implications in treating humans with movement disorders.
• Engineering efficient brain-machine interfaces: Here, we construct brain-machine interfaces, which are systems that elicit direct communication pathways between a brain and an external device, and engineer them to be as efficient as possible. Due to my training in Course VI both in systems engineering, control theory, and statistics, I was able to identify a key issue that was not being resolved in the neuroscience community – the appropriate use of feedback in such systems.