This project, which is a collaboration between groups at Stanford and at CNRS (France) investigates the use of one-dimensional plasma photonic crystals (which are periodic arrays of ionized gases that have a tunable refractive index in the microwave range of the electromagnetic spectrum) for the measurement of the electron density – the principal constituent of the ionized gas that affects the refractive index. The incident EM waves scatters off of the periodic structure much like x-rays scatter off of crystals.
How can we use computational tools to help clinicians in their daily practice? To develop the personalization of therapies, to aid the diagnosis? Are we sure that we can trust the results of the algorithms? These are core questions in personalized computational medicine. In this context, the goal of my research at Stanford is to create a generative statistical model of a given organ's shape for personalized computational medicine. Keeping in mind the potential clinical applications, special care will be given to the rigorous mathematical definition of its utilization's limits.
Successful fight against global warming cannot be achieved without the massive development of alternative carbon-free energies. Clean electricity can already be produced from fuel cells or wind energy, but in the transport industry liquid fuels have no real substitute yet. Our project is to address this issue by producing liquid fuels with cold plasmas. Plasmas are highly reactive media than can dissociate or create new species. We use them to dissociate CO2, the byproduct of liquid fuel combustion, instead of rejecting it into the environment.
Geoscientists and engineers use mineral and glass dissolution rates to quantify waterrock interactions and make predictions about groundwater chemistry, energy systems and environmentally contaminated sites. However, such rates are typically laboratory-derived and differ dramatically from observed rates in natural settings.
During the past decades, our environment has been critically polluted owing to the widening of the industrial processes and human actions. The result is the vast presence of chemical pollutants which can induce climate change and health problems. This situation leads to the development of novel waste abatement technologies. Among them, atmospheric non-thermal plasma (NTP) is a suitable technology to ensure pollutant dissociation since plasma chemical processes are quite energy efficient.
Your circadian rhythm is a “body clock” that controls 24-hour cycles and is crucial to obtaining regular and restful sleep. Over the course of evolution, input to this system was sunlight, but in modern society we are exposed to artificial light sources that differ in intensity, color, and timing compared to natural light. How circadian rhythms are affected by these aspects of artificial light is not fully understood, especially how different wavelengths (colors) of light can impact the timing of the clock.
The discovery of graphene and its astonishing properties has given birth to a new class of two-dimensional (2D) materials so named because, as with graphene, they can be thinned down to single layers only one to three atoms thick. This particular feature grants 2D materials unique physical and chemical properties such as transparency, flexibility, and extreme sensitivity to stimuli, making them very attractive for electronic and photonic applications such as high performance bendable electronics, optoelectronic and spintronic devices, sensors, electrodes and nanocomposites.
In the search for an alternative approach to chemotherapy against cancer, Photodynamic Therapy (PDT) has proven to be an effective treatment technique. PDT used a chemical compound called photosensitizer (PS), which is injected intravenously. When the PS reaches the tumor (generally after a few hours), a physician activates it with a non-harmful laser. The combination of PS and light instantly generates toxic molecular species that kill the tumor.
For my FSCIS fellowship, I worked at EcoleCentrale's Life Sciences Laboratory. Over the course of my six-week internship, I coded a program (written in C++ and Python) that utilizes image analysis algorithms and logistic regression to predict whether a cell will divide in the next X hours. It currently classifies cells as growing or not with 97% accuracy allowing for predictive analysis, which was not previously used in the laboratory. What drew me to the FSCIS fellowship is my love for the French culture.
DNA sequences from a sample of present day individuals is a record of the evolutionary history of the population. Availability of molecular sequence data from different organisms living today and from ancient DNA samples has enabled reconstruction of past population size trajectories of human populations over the past 150,000 years, the 2014 Ebola virus epidemic in Sierra Leone, and the Hepatitis C virus epidemic in Egypt.