John Realpe-Gómez, Ph.D.
Laboratory for Research in Complex Systems
Polytechnic University of Turin, Italy
(Ph.D., Physics with Riccardo Zecchina)
NASA, Quantum Artificial Intelligence Lab, USA: Research Scientist
University of Manchester, Complex Systems and Statistical Physics Group, UK: Research Associate
University of Cartagena, Physics Department, Colombia: Associate Professor
Polytechnic University of Turin, Microsoft Research Theory, Italy: PhD Student
Abdus Salam ICTP, Condensed Matter Physics: Pre-PhD Diploma Student
University of Valle, Physics Department, Colombia: MSc & BSc Student
I am currently focused on understanding the origin of biological complexity, life as information, and the potential synergies between biology, AI, and physics. I am using analytical and computational tools to investigate the origin of biological complexity in protein interaction networks, which essentially underpin nearly all biophysical and biochemical aspects of known life. I am also interested in using the tools of modern cognitive science to understand life as information and investigate "scientific intelligence" by modeling scientists as biological/cognitive systems learning about the environment they inhabit.
Realpe-Gomez, J. (2020). Embodied observations from an intrinsic perspective can entail quantum dynamics. ArXiv preprint:
Realpe-Gómez J, et al. Balancing selfishness and norm conformity can explain human behavior in large-scale prisoner's dilemma games and can poise human groups near criticality. Phys. Rev. E. 2018 Apr;97(4-1):042321.
M Benedetti, J Realpe-Gómez, et al. Quantum-assisted learning of hardware-embedded probabilistic graphical models. Phys. Rev. X 7 (4), 041052
Realpe-Gómez J, et al. Demographic noise and piecewise deterministic Markov processes.
Galla T, McKane AJ. Phys. Rev. E. 2012 Jul;86(1 Pt 1):011137.
J. Realpe-Gómez, et al. Demographic noise and resilience in semi-arid ecosystems; Ecol. Complex. 15 97-108 (2013).
M. Benedetti, J. Realpe-Gómez, et al. Quantum-assisted Helmholtz machines: A hybrid quantum-classical deep learning framework for commercial datasets; Quantum Science and Technology 3, 034007 (2018).
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.