As a 3rd generation nuclear engineer in her family, Katia Paramonova grew up with an appreciation for atoms and clean energy. While at MIT she worked with Professor Mike Short for her undergrad thesis in corrosion-resistant materials.
“NSE taught me to think in systems — (nuclear) technology, economics, and policy. But there was one challenge that I encountered time and again — the disconnects between stakeholders and how that hinders tech innovation development,” says Paramonova. Having worked in the U.S., Russia, Switzerland, India, and Ghana in nuclear, renewables, robotics, and higher education, Paramonova has seen how progress can be stunted because the right people haven’t met. She believes that more effective international collaboration, not a silver-bullet technology, is the key to mitigating climate change, and that the quality of stakeholder coordination can drastically change the outcomes.
With co-founder Yerzhan Karatayev, Paramonva has launched Centrly — a startup designed to connect cleantech startups with accelerators and potential funding sources. Centrly is on a mission to connect organizations to speed up innovation. They are building an open platform powered by graph algorithms that analyzes a vast dataset and recommends the best-matching partners for a company. They are working with accelerators to discover new startups for their programs and help those startups find the right companies for their pilot projects. They are also developing an enterprise-level open innovation platform to help corporate innovation teams manage their investment portfolio. Centrly’s first market is energywith the long-term goal of the platform being industry-agnostic.
Whle working with Professor Anne White as a graduate student, Rodriguez-Fernandez became intrigued by a fusion research mystery that had remained unsolved for 20 years: Why, under certain tokamak conditions, does cooling the edge of a fusion plasma result in the core becoming hotter?
His novel observations and subsequent modeling helped provide the answer, earning him the 2019 Del Favero Doctoral Thesis Prize, awarded annually to a PhD graduate in NSE whose thesis is judged to have made the most innovative advance in the field of nuclear science and engineering.
Currently, a postdoctoral associate at the MIT Plasma Science and Fusion Center (PSFC) Rodriguez-Fernandez spends half of his time working on SPARC, MIT’s fusion experiment, under the supervision of PSFC deputy director Martin Greenwald. He is involved in predicting the behavior and performance of SPARCplasmas using advanced computer simulation and optimization algorithms.
His research led him to become first author of an article published in a SPARC special issue of the Journal of Plasma Physics, September 2020: “Predictions of core plasma performance for the SPARC tokamak.”
Apart from this work on plasma performance, he is gaining experience on the topic of “whole-device modeling,” involving simulations that connect the engineering of the machine with the physics of the plasma, an effort that will aid in designing SPARC and supporting future experiments.
Through a collaboration with Oak Ridge National Laboratory, Rodriguez-Fernandez also devotes time to the JET tokamak in the UK, studying why plasmas that contain Deuterium (D) and Tritium (T) appear to have better energy confinement than plasmas that contain only Hydrogen. Understanding this is important to tokamaks like SPARC and ITER, the next-generation fusion device being built in France, as they will operate with mixtures of DT.
He contributes as well to the ASDEX Upgrade tokamak at the Max Planck Institute for Plasma Physics Institute of Plasma Physics in Germany, working with Prof. Anne White and her group. This collaboration, funded by the U.S. Department of Energy, focuses on the Correlation Electron Cyclotron Emission (CECE) diagnostic, which is able to measure tiny fluctuations in the plasma temperature that are related to the loss of energy and particles from the hot core of the plasma to the edge. Rodriguez-Fernandez trains the group in the use of modeling techniques to better understand the experimental results and study energy transport. He is also implementing machine learning algorithms to design experiments for the device, which are expected to be benchmarked in the coming year.
To date seven NSE alums have been recognized in the annual Forbes 30 under 30 lists.
Written by NSE and Paul Rivenberg/PSFC.