Brief bio: Dylan Hutchison is a student at Stevens Institute of Technology pursuing a B.E. in Computer Engineering, a M.S. in Computer Science and a M.S. in Applied Mathematics. Dylan's experience evolved over four internships, from analytics with distributed databases, to uncertainty in causal inference, and to model construction in probabilistic programming.
Why the three degrees, and why the breadth of study? To discover new models behind complex systems, we need an interdisciplinary approach combining each domain's techniques.
How do we disseminate the power of inference algorithms, if not by coupling machine learning with programming languages?
How do we leverage inference for analytics at scale, if not by randomized algorithms and HPC?
How do we ensure our analytics impact the world, if not by causal inference in computational science?
How do we formalize our findings into concrete statements, if not by the rigour of mathematics and computer science theory?
Philosophical aside: I've had many thoughts and discussions on the goals of scientists and engineers as modelers. Check out the ModelTalk for lively conclusions on where we as a society are heading in the name of progress.