Allon Percus is the Joseph H. Pengilly Professor of Mathematics at Claremont Graduate University. He joined the university as an associate professor in January 2009.
Percus is an applied mathematician whose diverse background has put him at the forefront of a field of mathematics with applications in the computational and physical sciences. His research combines discrete optimization and statistical physics, exploiting physical models and techniques to study the performance of algorithms on NP-hard problems. Together with Stefan Boettcher, he developed the method of Extremal Optimization that has since been applied to problems ranging from social networks to protein folding.
Other research interests include the phase structure of combinatorial problems over ensembles of random instances, using this phase structure to motivate better algorithms and extending the picture to random network models closely reflecting real-world data. Percus has led several interdisciplinary project teams at Los Alamos National Laboratory and has organized numerous conferences and workshops exploring the overlap between combinatorics, phase transitions and computational complexity. His research has received funding from the Air Force Office of Scientific Research, the National Science Foundation, the Department of Energy, and Southern California Edison, among others.
From 2003 to 2006, Percus was associate director of the Institute for Pure and Applied Mathematics (IPAM) at UCLA, a national institute established by the National Science Foundation to spark interactions between mathematicians and scientists from a broad range of fields. He was responsible for scientific oversight of many of IPAM’s activities, working in close collaboration with organizing committees across disciplines to create and run programs spreading the impact of mathematics throughout the sciences. He has been an adjunct faculty member in computational science at San Diego State University, a visiting researcher at the New Mexico Consortium, and a visiting associate professor at UCLA.
Percus received his BA in Physics from Harvard in 1992 and his PhD from the Université Paris-Sud, Orsay, in 1997, after which he spent the first part of his scientific career as a technical staff member of Los Alamos National Laboratory’s Information Sciences Group.
J.M. Henderson, J. Kath, J.K. Golden, A.G. Percus and D. O’Malley, “Addressing quantum’s ‘fine print’ with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks,” Scientific Reports 14, 3592 (2024).
M. Schwarzer, B. Rogan, Y. Ruan, Z. Song, D.Y. Lee, A.G. Percus, V.T. Chau, B.A. Moore, E. Rougier, H.S. Viswanathan, G. Srinivasan, “Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks,” Computational Materials Science 162, 322-332 (2019).
M. Valera, Z. Guo, P. Kelly, S. Matz, A. Cantu, A.G. Percus, J.D. Hyman, G. Srinivasan and H.S. Viswanathan, “Machine learning for graph-based representations of three-dimensional discrete fracture networks,” Computational Geosciences 22, 695-710 (2018).
X.-Z. Wu, A.G. Percus and K. Lerman, “Neighbor-neighbor correlations explain measurement bias in networks,” Scientific Reports 7, 5576 (2017).
C. Garcia-Cardona, E. Merkurjev, A.L. Bertozzi, A. Flenner, and A.G. Percus.“Multiclass Data Segmentation Using Diffuse Interface Methods on Graphs.” IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2014): 1600–13.
E. Merkurjev, C. Garcia-Cardona, A.L. Bertozzi, A. Flenner, and A.G. Percus. “Diffuse Interface Methods for Multiclass Segmentation of High-Dimensional Data,” Applied Mathematics Letters 33 (2014): 29–34.
L.M. Smith, K. Lerman, C. Garcia-Cardona, A.G. Percus, and R. Ghosh. “Spectral Clustering With Epidemic Diffusion.” Physical Review E 88, 042813 (2013).
A.G. Percus, G. Istrate, B. Goncalves, R.Z. Sumi and S. Boettcher. “The Peculiar Phase Structure of Random Graph Bisection.” Journal of Mathematical Physics 49, 125219 (2008).
D. Aldous and A.G. Percus. “Scaling and Universality in Continuous Length Combinatorial Optimization.” Proceedings of the National Academy of Sciences 100, 11211–15 (2003).
A.G. Percus, G. Istrate and C. Moore, eds. Computational Complexity and Statistical Physics. (Oxford University Press, New York, 2006).
S. Boettcher and A.G. Percus. “Optimization With Extremal Dynamics.” Physical Review Letters 86, 5211–14 (2001).
Math 164/264: Scientific Computing
Math 387: Discrete Mathematical Modeling
Math 451: Statistical Mechanics & Lattice Models
Math 251: Probability
Math 293/393: Mathematics Clinic