Hong Qin joined the UW-Madison Department of Nuclear Engineering and Engineering Physics as an associate professor in August 2026. Qin is a theoretical and computational plasma physicist whose work spans fusion energy, plasma physics, accelerator physics, artificial intelligence and machine learning, and quantum computing.
He was born and raised in Zhengzhou, China, and earned his BS and MS degrees from Peking University in Beijing, then earned his PhD in astrophysical sciences from Princeton University in 1998, where he specialized in plasma physics and fusion energy. Prior to joining UW-Madison, Qin spent more than two decades at Princeton University, including as a principal research physicist at the Princeton Plasma Physics Laboratory and a lecturer with the rank of professor.
In this Q&A, Qin talks about his research and what brought him to UW-Madison.
What is the focus of your research?
I am a theoretical and computational plasma physicist. Plasma is often called the fourth state of matter. It consists of electrically charged particles and is found in the sun and stars, in lightning and in the extremely hot fuel used in fusion energy experiments.
My research focuses on understanding the fundamental mathematical structures of plasma dynamics and using that understanding to develop more reliable computational methods and improve our ability to predict plasma behavior. I am particularly interested in geometric and topological structures and advanced simulation algorithms, and their applications to fusion energy, as well as their connections to quantum physics and quantum technologies.
What are the main goals of your current research program?
My current research program has three closely connected goals. The first is to develop highly accurate computer simulation algorithms for plasma dynamics. Fusion plasmas are extremely complex, and simulations may need to follow billions of particles over long periods. These simulations are expensive, and even small numerical errors can accumulate and eventually produce physically incorrect results. My group develops algorithms that preserve the fundamental laws and mathematical structures of the physical system, making long-term simulations much more reliable. This research is also increasingly connected with AI and quantum computing.
The second goal is to develop topological plasma physics. Topology studies properties that remain unchanged under continuous deformation. In plasma physics, these ideas can lead to waves that are unusually robust against imperfections, scattering and other disturbances. Such topological waves could provide more reliable ways to heat, control and diagnose fusion plasmas. Similar topological ideas also apply to equatorial ocean waves that influence El Niño.
The third goal is phase-space engineering: using electromagnetic waves to control how particles move and how their energy is distributed. This could help maintain the special particle distributions required by advanced fusion fuels and allow energy produced by fusion reactions to be transferred more efficiently.
These goals are important because practical fusion energy requires not only better materials and experiments but also better ways to understand, predict and control the behavior of extremely hot plasmas.
What are some key applications of your research?
The main application is fusion energy. Reliable simulations can help scientists design fusion devices, interpret experimental results and predict plasma behavior before an experiment is performed.
Topological plasma waves may provide robust ways to heat fusion plasmas and drive electrical current, even when the plasma edge is turbulent or irregular. Phase-space engineering could help maintain nonthermal particle distributions, transfer energy from fusion products back to the fuel, and potentially enable more efficient direct conversion of fusion energy into electricity.
The computational methods also have applications in plasma accelerators, high-intensity particle beams, space and astrophysical plasmas, and scientific machine learning. At UW-Madison, I hope to use them to help build high-fidelity digital twins of fusion experiments by combining experimental measurements with structure-preserving numerical models.
What attracted you to the UW-Madison Department of Nuclear Engineering and Engineering Physics?
UW-Madison is becoming what I like to call the “center of the universe for fusion energy.” It has an exceptional combination of plasma theory, computation, engineering and major fusion experiments. That combination is unusual and especially attractive to me because I want my theoretical and computational work to be closely connected with experiments.
The department and the broader university have major plasma and fusion programs such as Pegasus-III, WHAM, MST, HSX, MAP and the Center for Plasma Theory and Computation.
I am also excited by the College of Engineering’s new Engineering Moonshots initiative under Grainger Dean Devesh Ranjan. One of the six Moonshots is “Limitless distributed power,” centered on fusion-driven energy. This is closely aligned with my own research, and I hope my work in plasma theory, advanced computation, topological plasma physics and phase-space engineering can contribute directly to this larger effort.
Fusion energy is a challenge large enough that no single research group or discipline can solve it. What attracts me to UW-Madison is the opportunity to work across theory, computation, experiment and engineering toward that common goal. I am excited to join the faculty and help strengthen UW-Madison’s position as an international center for fusion energy research.
What achievements are you most proud of in your career?
Scientifically, I am most proud of helping establish two new research areas in plasma physics.
The first is structure-preserving geometric algorithms. My group developed a new class of plasma simulation methods that preserve the underlying mathematical structures and physical laws of the system. These methods have enabled whole-device six-dimensional kinetic simulations of tokamak plasmas at extremely large scales.
The second is topological plasma physics. My collaborators and I showed how topology can arise in the phase space of a plasma and discovered new types of robust waves, known as topological plasma waves.
I am most proud, however, of my students and postdoctoral researchers. Several have received major national fellowships and awards, and former students now hold faculty and scientific leadership positions at universities and fusion companies. Helping students become independent scientists has been one of the most rewarding parts of my career.
What courses will you be teaching?
This fall, I’m teaching NE/ECE/Physics 525: Introduction to Plasmas. I am also interested in developing new graduate courses, possibly including geometric and topological methods for engineering physics. At Princeton, I taught courses in general plasma physics, computational plasma physics, plasma transport and confinement, gyrokinetic theory, and nonneutral plasmas. I look forward to bringing that experience to UW-Madison and adapting it to the needs and interests of the students here.
What’s one thing you hope students who take a class with you will come away with?
I hope students will develop a passion for the subject and a desire to take ownership of it. Learning physics should not mean memorizing a collection of formulas. Students should understand where the equations come from, what assumptions they depend on, and how to judge whether a result is physically reasonable. Once students build that foundation from first principles, they can enjoy applying what they know to new problems that may look very different from anything they encountered in the classroom. I also want students to experience that learning, like research, is a creative process. Difficult problems often require persistence, imagination and a willingness to question standard approaches.
I think this approach to learning works especially well in the age of generative AI. AI can be a powerful tool, but students still need curiosity, judgment and a real desire to understand the subject. Once they develop that passion and sense of ownership, AI can greatly expand what they are able to explore and accomplish.
What are your hobbies and other interests?
Outside research and teaching, I enjoy reading about archaeology and art. I would not claim much knowledge of either, but I have managed not to let that interfere with my interest. I also enjoy dry and self-deprecating humor.
Photo of Hong Qin by Joel Hallberg