About

I was born in Vietnam but came to the U.S. during high school through a cultural exchange program. Since then I have graduated from Massachusetts Institute of Technology with a B.Sc. Degree and Princeton University with a Ph.D. Degree (thesis “Swarm Intelligence in Natural and Synthetic Lives“).

I was a postdoc in the Department of Molecular, Cellular and Developmental Biology at Yale University. I am now a postdoc in the Department of Chemical and Biomolecular Engineering at Johns Hopkins University, and also affiliated with Johns Hopkins Medical Institute. My life is blessed with an amazing family, good friends, and great teachers.

Here is my CV.

Research Interests

I see myself as a physicist, but my research journey has also made me a biologist and a roboticist.

My main interest is exploring unconventional ideas (such as the emergence of intelligence at microbial level and the robo-biology of artificial lifeforms), but for credibility I also have worked on various conventional scientific topics (such as emergent swarm behavior in active matter, microbiological engineering, information theory, cell morphology & motility, evolutionary cancer biology, protein-folding, hydrodynamics, applied number theory in atomic physics, ideological polarization in politico-physics, M(atrix) theory in high energy physics, opto-thermo-electrical transport in condensed matter physics, and many more).

The 2024 Nobel laureate in physics, John Hopfield, published his groundbreaking 1982 paper on neural networks in PNAS because “no physics journal would accept it as physics.” As humanity advances, our understanding of physics continues to expand beyond contemporary textbook definitions. This year, the American Physical Society’s mission underscores the importance of diverse, interdisciplinary approaches to address global challenges. This aligns with my vision, as much of my work has been about developing tools to explore emergent behaviors and complex social dynamics — key issues in today’s evolving concept of physics.

You can read here for the broader impacts of my works.

Notable Media Coverages

Media coverage of our work on emergent bacterial swarm intelligence as they navigate through non-trivial topology mazes and invade lung-like fractal spaces: WIRED.

Media coverage of our work on emergent states of synthetic life on a programable interactive landscape. We call the robot bootstrapped locomotion due to spontaneous symmetry-breaking “field-drive,” similar to “warp-drive” in general relativity, as it is based on the back-reaction of “resource-space” warping around a robot: APS Physics, Epsiloon.

Media coverage of our work on how analog simulations can help optimizing cancer treatment. The robobiological experiments bring new insights into biological evolution and suggest a way to improve chemotherapy. Instead of periodic doses of a single drug, stochastic doses of multiple drugs could be more effective: Physics Today. This is a new and exciting field in biology: Let the robotic games begin!

Media coverage of our work on developing a tool for estimating the local entropy production rate based on time-reversal asymmetry, linking the local dynamics to global pattern formation and creating the visualization & quantification of how information is transferred in living systems: APS Physics. A figure from this paper is the cover picture of Physical Review Letter Volume 129 Issue 22.

Media coverage of our work on how social inter-bacterial communication can help colonies avoid dangers. Here we have created an information "black hole" on a microfluidic device where bacteria are irreversibly swept away by hydrodynamic flow upon entering, representing an existential threat: Discover.

Notable Professional Activities

I’m a referee for Proceedings of the National Academy of Sciences, Small and Biophysical Journal. In the summer, if time permits I teach enlisted veterans S.T.E.M. and lab skills for the Warrior-Scholar Project.

I was a Graduate Teaching Fellow at McGraw Center, Princeton University. During my undergrad, I developed some of the notes for high-energy physics courses at M.I.T. OCW and EdX such as 8.851 Effective Field Theory and 8.821 String Theory and Holographic Duality.

In my free time, I collaborate with many talented Vietnamese students on various exploratory topics. I also assist in coaching the Vietnamese team for Physics Olympiads at international level. I really wish to help my home country more and guide our next generation of physicists.

Notable Research Findings

Biophysics:

Phan, T. V., Morris, R., Black, M. E., Do, T. K., Lin, K. C., Nagy, K., Sturm, J. C., Bos, J., & Austin, R. H. (2020). “Bacterial Route Finding and Collective Escape in Mazes and Fractals.” Physical Review X, 10(3), 031017.

Natural complex topologies form challenging existential puzzles for bacteria. Billions of years of evolution have shaped the response of bacteria to these puzzles, whose solutions can be found in how bacteria such as the common E. coli respond collectively and individually to challenges. We pose challenges to bacteria in the forms of mazes and fractal spaces, showing the unexpected and clever (if we can say that word about bacteria) way in which they are able to efficiently explore nontrivial mazes in times much shorter than a no-memory walk would predict, and can collectively escape from a fractal topology.

Guo, B.*, Ro, S.*, Shih, A., Phan, T. V., Austin, R. H., Martiniani, S., Levine, D., & Chaikin, P. M. (2022). “Model-free measurement of local entropy production and extractable work in active matter.” Phys. Rev. Lett. 129, 220601. [*co-first authors].

Time-reversal symmetry breaking is a universal feature of active and living systems. Here we introduce a model-free local measure of entropy production as a metric of time-irreversibility and a numerical protocol to estimate it, then establish a connection to the extractability of work in a given region of the system. We validate our approach in theory, simulation, and experiments by considering systems of active Brownian particles and E.coli bacteria.

Phan, T. V.*, Li, S.*, Ferreris, D., Morris, R., Bos, J. A., Guo, B., Martiniani, S., Chaikin, P. M., Kevrekidis, Y. G., & Austin, R. H. (2024). “Social Physics of Bacteria: Avoidance of an Information Black Hole.” arXiv preprint arXiv:2401.16691. [*co-first authors].

Social physics explores responses to information exchange in a social network and can be mapped onto bacterial collective signaling. Here, we investigate how social inter-bacterial communication involves the coordination of responses to communication loss, as opposed to solitary foraging behavior, with collective responses emerging at the population level. We present a two-dimensional enclosed microfluidic environment featuring concentric rings of funnel ratchets, which direct motile E. coli bacteria towards a single exit, an information "black hole." Passage into the black hole irreversibly sweeps the bacteria away via hydrodynamic flow. We demonstrate that the spatiotemporal evolution of entropy production reveals how bacteria avoid crossing the hydrodynamic black hole's information horizon.

Robophysics:

Wang, G.*, Phan, T. V.*, Li, S., Wombacher, M., Qu, J., Peng, Y., Chen, G., Goldman, D. I., Levin, S., Austin, R. H., & Liu, L. (2021). “Emergent Field-Driven Robot Swarm States.” Phys. Rev. Lett. 126, 108002. [*co-first authors].

A swarm of small robots can imitate deer or bacteria searching for food. We release these electronic foragers on an LED screen whose light output was adjusted to mimic the amount of a consumable resource—such as grass for deer. Once the robots “ate” the resource in one place, they can sense that they are in a depleted region and will move in the direction of more food; we call this emergent symmetry-breaking motion “field drive” (which is analogous to the general relativistic “warp drive,” as it is based on the warping of the “resource space” around a robot). This programmed behavior led to collective patterns that resembled phases of matter including liquid, crystal, and glass.

Wang, G.*, Phan, T. V.*, Li, S., Wang, J., Peng, Y., Chen, G., Goldman, D. I., Levin, S. A., Pienta, K., Amend, S., Austin, R. H., & Liu, L. (2022). “Robots as Models of Evolving Systems.” Proceedings of the National Academy of Sciences, 119(12), e2120019119. [*co-first authors].

Perhaps someday synthetic life may ultimately co-exist with biological life, and it may learn from biology how to survive. To study a form of robobiology with embedded evolution dynamics which have been so successful for the dominance of biological life, we have developed in emulation of natural systems analog/digital interactive motile robots which move over multiple dynamic resource landscapes that they themselves change. We discover an interesting sector of evolution, in which the survivability of fast-mutating robots can be strongly suppressed under stochastic changes when they respond to resources that they neither consumed nor needed. In biology, the ability of a gene to influence two or more unrelated physical traits is known as pleiotropy, which has been implicated in cancer cells’ resistance to chemotherapy. This finding suggests a way to improve chemotherapy: instead of periodic doses of a single drug, stochastic doses of multiple drugs could be more effective.