Jean-Marie Bussat, PhD
Personal and Educational Background
I grew up in a small village in the French Alps. I have an engineer degree (MS. Equivalent) from the Ecole Superieure d'Ingenieurs en Genie Electrique (ESIGELEC) and a PhD from the University of Paris XI. Since my PhD, I have been working as an integrated circuit (IC) and system design engineer. I specialize in mixed analog-digital design and focus on system aspects. My expertise includes specifying, desigining and testing ICs, and developing system prototypes. I have worked on instruments for applications ranging from high energy physics to material science. I enjoy the pluridisciplinary nature of my work.
My thesis work was on integrated circuits design, applied to instruments for high-energy physics experiments. I developed an autoranging circuit for a detector that measures the energy of sub-atomic particles. This circuit was developed for the ATLAS experiment, installed at CERN, which sought to discover the Higgs boson, a particle that could explain why matter has mass.
Preparing my PhD also gave me the opportunity to teach a digital electronics class in the networking department of the Technical Institute of the University of Annecy. For two years, I was in charge of 50 students and designed and taught the lectures as well as the labs.
After my PhD, I continued working in high-energy physics at Princeton University, on another Higgs boson experiment (CMS).
I then worked for six years at the Lawrence Berkeley National Laboratory. There, I developed various instrumentation systems and prototypes, including a high-speed detector for material science, a camera for an electron microscope, a detector for a probe to be sent to Mercury and a fast X-ray camera. I also continued developing my teaching skills by mentoring several engineering students and by teaching a graduate class (Physics 290E: Introduction to Electronics for Physicists).
I moved to Stanford University in September 2007. Joining the Brains in Silicon group fulfilled an interest in neural networks and neuromorphic engineering that I had put on hold for many years. I am planning to engage in this research, although the current focus of my job is to provide support.
Being an engineer, I'm interested in exploiting the research done in the lab in real-world applications. My main interest lies in sensorimotor control and I'd like to build a neuromorphic system that mimics the vestibular ocular reflex and can track objects.
Neuromorphic engineering has great potential in robotics, where brute-force computational techniques fall short. A robot shouldn't need three Pentium class computers to stand and walk—biology achieves the same goal with a few thousand neurons. Robotic vision and object recognition display similar inefficiencies. I plan to explore neuromorphic solutions with Neurogrid, which will model a million neurons in real-time.
Personal home page at Stanford.