Daniel Ling, Ph.D.
Laboratory for Research in Complex Systems
A.B. (with distinction), Applied Mathematics in Economics
What trend, breakthrough or discovery are you most excited about?
Complex systems are at the core of how the world behaves, both in a macroscopic and microscopic context. The trend in incorporating complex systems research in building moonshot technologies and solving some of the world's hardest problems, is incredibly exciting.
It's evident that the solutions for these wide-ranging large-scale problems that society faces will require approaches that draw on complex systems research, and I'm thrilled to be able to work on innovations that can better the world we live in.
Mathematics in complex systems is proving to be useful for research, not only in epidemiological contexts such as pandemic prevention and COVID-19 response, but also in economic contexts such as addressing war, economic inequality, and financial crises. Complex systems and applied math can be useful in describing phenomena at every scale and in every facet of the world we live in, from voting behavior and stock market bubbles, to biological systems and molecular dynamics.
My research interests within complex systems include mechanism design, game theory, information economics, market design, systems biology, condensed matter physics, and AI/ML.
Seeing the emergence of the COVID-19 pandemic motivated me to take an active role in combating this crisis that is affecting the lives of so many people and impacting society on such a grand scale. I did not want to just let this pandemic run its course and take a passive stance to the current public health crisis. I had to do something, which brought me to Dr. Date and the Laboratory for Research in Complex Systems.
Conducting research on the mathematics of pandemics, mathematical modeling of infectious diseases, and how it can inform COVID-19 response efforts and pandemic prevention. I am working on research and company-building with the aim of helping public and private organizations around the world in the fight to control the spread of infectious disease and improve health on a population level. I firmly believe that a complex systems approach can help inform public health interventions aimed at containing pandemics and reducing the spread of infectious disease, and subsequently improve the lives of millions if not billions of people.
D. Ling, X.S. Ling. "On the distribution of DNA translocation times in solid-state nanopores: an analysis using Schrödinger’s first-passage-time theory." August 2013. Journal of Physics: Condensed Matter. 25 (37), 375102.
D. Ling. “Composite Gold-Silver Alloy Nanoparticles for GFP Interactions." Working Paper.
As a Research Scientist at the NASA Quantum Artificial Intelligence Lab, John helped advance the field of quantum machine learning by contributing to successfully train, for the first time, a quantum annealing computer to generate, reconstruct, and classify images of hand-written digits. John earned a PhD in ICT-Physics at the Polytechnique University of Turin, Italy. His thesis work in Ricardo Zecchina's Microsoft Research theory group focused on the development of message-passing algorithms inspired on the physics of disordered systems for solving combinatorial socio-economic problems. Afterwards he worked as a posdoc with Alan McKane and Tobias Galla at the University of Manchester, combining tools of non-equilibrium statistical physics and agent based models to investigate socio-economic and ecological systems.