Sahil Agarwal

CEO @ Enkrypt AI | Math PhD, Yale | Oxford | IIT | Yale EIR

Cambridge, Massachusetts, United States

About

Applied Mathematician by training, I am passionate about solving complex problems that have real day-to-day impact on every living being, with a focus on innovations in Applied Machine Learning, Human-Technology Optimizations and Clean Technologies. I use data-driven tools inspired from non-equilibrium statistical physics to analyze, model and understand nonlinear dynamics and stochastic processes. I have applied these to geo-physical applications such as climate change and discovery of exo-planets as well as computer science applications in distinguishing noise from chaos, natural language processing, computer vision and temporal data analysis in text, music and video. I have more than 10 publications in highly reputed international journals. My work has been featured in American Physical Society as well as many news articles at Yale, such as Yale Scientific Magazine, Yale Daily Newspaper and Yale News. My PhD work has also been featured in an article by the Yale Graduate School (link below).

Experience

  • Co-Founder & CEO at Enkrypt AI

    On a mission to ensure AI benefits humanity.

  • Member at American Society for AI

  • Entrepreneur in Residence at Yale Ventures

  • Director of AI Research at Accrete AI

  • Visiting Research Scholar at Yale University

    Climatological Evolution of Sea Ice

  • Chief AI Officer at Accrete AI

  • Graduate Student at Yale University

    I work on problems in Nonlinear Dynamics and Stochastic Processes with Astrophysical and Geophysical Applications. I have combined the concepts of noise (stochasticity), chaos theory (sensitivity to initial conditions) and fractal theory in a multi-scale framework to study the dynamics of Earth's climate based on observations from satellite and Earth-based sensors. These important findings have implications on the dynamics and prediction of Arctic Sea Ice Extent as well as on Global Climate on multi-decadal timescales. This framework has also led to the invention of a new method to detect exoplanets, i.e. planets outside our solar system. While traditional methods fit stellar evolution models to data, we have developed a novel data-based method which does not have any a-priori assumptions to it, leading to much more robust detection of these exoplanets. The Yale Graduate School of Arts and Sciences did a feature article on my research, which can be found in a link below. My research on exoplanets has also been featured in Yale Scientific Magazine, Yale Daily Newspaper and Yale News (links below).

  • Teaching Fellow at Yale University

    Spectral Graph Theory (August - December, 2015) Linear Algebra with Applications (January - May, 2015) Design and Analysis of Algorithms (January - May, 2013)