Curriculum Vitae / Publications

Institute of Mathematical Sciences
Claremont Graduate University
1237 N. Dartmouth Ave.
Claremont, CA 91711
Tel: 909-607-0744 / Fax: 909-607-8261
https://scholar.cgu.edu/allon-percus/

Research Interests:

• Discrete optimization
• Statistical physics of algorithms and networks
• Random combinatorial structures
• Computational complexity

Education:

• Université Paris-Sud, Orsay: PhD in theoretical physics
  Thesis topic: The Traveling Salesman and Related Stochastic Problems
  September 1997
• École Normale Supérieure, Paris: DEA (MS equivalent) in theoretical physics
  September 1994
• Harvard University: BA in physics
  June 1992

Regular Appointments:

• Joseph H. Pengilly Professor of Mathematics, Institute of Mathematical Sciences, Claremont Graduate University
  July 2023 – present
• Professor of Mathematics, Institute of Mathematical Sciences, Claremont Graduate University
  July 2016 – June 2023
• Associate Professor of Mathematics, Institute of Mathematical Sciences, Claremont Graduate University
  January 2009 – June 2016
• Member of the Technical Staff, Information Sciences Group, Los Alamos National Laboratory
  June 2000 – December 2008

Adjunct and Visiting Appointments:

• Adjunct Faculty, Computational Science Research Center, San Diego State University
  July 2009 – June 2018
• Visiting Associate Researcher, Department of Mathematics, University of California, Los Angeles
  July 2009 – August 2012
• Visiting Researcher, New Mexico Consortium
  September 2008 – August 2012
• Visiting Associate Professor, Department of Mathematics, University of California, Los Angeles
  July 2006 – October 2008
• Associate Director, Institute for Pure and Applied Mathematics, University of California, Los Angeles
  July 2003 – June 2006
• Associate Adjunct Professor, Department of Computer Science and Engineering, University of California, Riverside
  December 2002 – October 2005
• Postdoctoral Research Associate, Center for Nonlinear Studies and Computer Research & Applications Group, Los Alamos National Laboratory
  October 1997 – June 2000

Book:

• A.G. Percus, G. Istrate and C. Moore, eds., Computational Complexity and Statistical Physics (Oxford University Press, New York, 2006), including introductory chapter “Where statistical physics meets computation,” pp. 3-24.

Published Papers:

• 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).
• H. Pi, K. Burghardt, A.G. Percus and K. Lerman, “Clique densification in networks,” Physical Review E 107, L042301 (2023). Featured in Research Highlights: Z. Budrikis, “Networks get more cliquey as they grow,” Nature Reviews Physics 5, 442 (2023).
• K.A. Burghardt, Z. He, A.G. Percus and K. Lerman, “The emergence of heterogeneous scaling in research institutions,” Communications Physics 4, 189 (2021).
• S.-C. Ngo, A.G. Percus, K. Burghardt and K. Lerman, “The transsortative structure of networks,” Proceedings of the Royal Society A 476, 20190772 (2020).
• 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 and 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).
• J. Sunu, A.G. Percus and B. Hunter, “Unsupervised vehicle recognition using incremental reseeding of acoustic signatures,” Proceedings of the 24th International Symposium on Methodologies for Intelligent Systems (ISMIS 2018). Lecture Notes in Artificial Intelligence (Springer International, Cham, Switzerland, 2018), Vol. 11177, pp. 151-160.
• X.-Z. Wu, P.G. Fennell, A.G. Percus and K. Lerman, “Degree correlations amplify the growth of cascades in networks,” Physical Review E 98, 022321 (2018).
• J. Sunu and A.G. Percus, “Dimensionality reduction for acoustic vehicle classification with spectral embedding,” Proceedings of the 15th IEEE International Conference on Networking, Sensing and Control (ICNSC 2018), 129-133 (2018).
• 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).
• L.M. Smith, L. Zhu, K. Lerman and A.G. Percus, “Partitioning networks with node attributes by compressing information flow,” ACM Transactions on Knowledge Discovery from Data 11, 15 (2016).
• C. Garcia-Cardona, A. Flenner and A.G. Percus, “Multiclass semi-supervised learning on graphs using Ginzburg-Landau functional minimization,” Advances in Intelligent Systems and Computing 318, 119-135 (2015).
• A. Ma, A. Flenner, D. Needell and A.G. Percus, “Improving image clustering using sparse text and the wisdom of the crowds,” Proceedings of the 48th Annual Asilomar Conference on Signals, Systems, and Computers, 1555-1557 (2014).
• 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, 1600-1613 (2014).
• 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, 29-34 (2014).
• 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).
• C. Garcia-Cardona, A. Flenner and A.G. Percus, “Multiclass diffuse interface models for semi-supervised learning on graphs,” Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2013), 78-86 (2013).
• M. Keeter, D. Moore, R. Muller, E. Nieters, J. Flenner, S.E. Martonosi, A.L. Bertozzi, A.G. Percus and R. Levy, “Cooperative search with autonomous vehicles in a 3D aquatic testbed,” Proceedings of the 2012 American Control Conference (ACC 2012), 3154-3160 (2012).
• M. Bradonjic, T. Müller and A.G. Percus, “Coloring geographical threshold graphs” (full journal version), Discrete Mathematics and Theoretical Computer Science 12, 103-114 (2010).
• M. Bradonjic, A. Hagberg, N.W. Hengartner and A.G. Percus, “Component evolution in general random intersection graphs,” Proceedings of the 7th Workshop on Algorithms and Models for the Web-Graph (WAW2010). Lecture Notes in Computer Science (Springer-Verlag, Berlin, 2010), Vol. 6516, pp. 36-49.
• S. Eidenbenz, G. Ercal-Ozkaya, A. Meyerson, A. Percus and S. Varatharajan, “Incentive compatible and globally efficient position based routing for selfish reverse multicast in wireless sensor networks,” Algorithms 2, 1303-1326 (2009).
• G. Ercal-Ozkaya, S. Eidenbenz, A. Meyerson and A. Percus, “On a locally minimum cost forwarding game,” Proceedings of the Second ACM International Workshop on Foundations of Wireless Ad Hoc and Sensor Networking and Computing (FOWANC 2009), 29-36 (2009).
• C. Wang, J.D. Hyman, A.G. Percus and R. Caflisch, “Parallel tempering for the traveling salesman problem,” International Journal of Modern Physics C 20, 539-556 (2009).
• M. Bradonjic, T. Müller and A.G. Percus, “Coloring geographical threshold graphs” (extended abstract), Proceedings of the Fifth Workshop on Analytic Algorithmics and Combinatorics (ANALCO 09), 11-16 (2009).
• M. Bradonjic, A. Hagberg and A.G. Percus, “The structure of geographical threshold graphs,” Internet Mathematics 5, 113-139 (2008).
• 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). Required copyright notice: copyright (2008) American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The official version of the article may be found here.
• M. Bradonjic, A. Hagberg and A.G. Percus, “Giant component and connectivity in geographical threshold graphs,” Proceedings of the 5th Workshop on Algorithms and Models for the Web-Graph (WAW2007). Lecture Notes in Computer Science (Springer-Verlag, Berlin, 2007), Vol. 4863, pp. 209-216.
• V. Krishnamurthy, M. Faloutsos, M. Chrobak, J.-H. Cui, L. Lao and A.G. Percus, “Sampling large internet topologies for simulation purposes,” Computer Networks 51, 4284-4302 (2007).
• S. Boettcher and A. Percus, “Optimizing glasses with extremal dynamics,” Proceedings of the 17th Workshop on Computer Simulation Studies in Condensed-Matter Physics. Springer Proceedings in Physics (Springer, Berlin, 2006), Vol. 103, pp. 74-79.
• G. Istrate, S. Boettcher and A.G. Percus, “Spines of random constraint satisfaction problems: definition and connection with computational complexity,” Annals of Mathematics and Artificial Intelligence 44, 353-372 (2005).
• V. Krishnamurthy, M. Faloutsos, M. Chrobak, L. Lao, J.-H. Cui and A.G. Percus, “Reducing large internet topologies for faster simulations,” Proceedings of the 4th International IFIP-TC6 Networking Conference (NETWORKING 2005). Lecture Notes in Computer Science (Springer-Verlag, Berlin, 2005), Vol. 3462, pp. 328-341.
• S. Boettcher and A.G. Percus, “Extremal optimization at the phase transition of the 3-coloring problem,” Physical Review E 69, 066703 (2004).
• S. Boettcher, G. Istrate and A.G. Percus, “Spines of random constraint satisfaction problems: definition and impact on computational complexity,” Proceedings of the 8th International Symposium on Artificial Intelligence and Mathematics (AIMATH ’04) AI&M 2-2004.
• D. Aldous and A.G. Percus, “Scaling and universality in continuous length combinatorial optimization,” Proceedings of the National Academy of Sciences 100, 11211-11215 (2003).
• S. Boettcher and A.G. Percus, “Optimization with extremal dynamics,” Complexity 8, 57-62 (2003).
• S. Boettcher and A.G. Percus, “Extremal optimization: an evolutionary local-search algorithm,” in: H.K. Bhargava and N. Ye, eds., Computational Modeling and Problem Solving in the Networked World: Interfaces in Computer Science and Operations Research (Kluwer Academic Publishers, Dordrecht, Netherlands, 2003), pp. 61-77.
• C.M. Brislawn, B.E. Wohlberg and A.G. Percus, “Resolution scability for arbitrary wavelet transforms in the JPEG-2000 standard,” in: T. Ebrahimi and T. Sikora, eds., Visual Communications and Image Processing, Proceedings of SPIE 5150, 774-784 (2003).
• S. Boettcher and A.G. Percus, “Optimization with extremal dynamics,” Physical Review Letters 86, 5211-5214 (2001).
• S. Boettcher and A.G. Percus, “Extremal optimization for graph partitioning,” Physical Review E 64, 026114 (2001).
• S. Boettcher and A.G. Percus, “Nature’s way of optimizing,” Artificial Intelligence 119, 275-286 (2000).
• S. Boettcher and A.G. Percus, “Combining local search with co-evolution in a remarkably simple way,” Proceedings of the 2000 Congress on Evolutionary Computation, 1578-1584 (2000).
• S. Boettcher, A.G. Percus and M. Grigni, “Optimizing through co-evolutionary avalanches,” Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, 447-456 (2000).
• E. Czabarka, G. Konjevod, M.V. Marathe, A.G. Percus and D.C. Torney, “Algorithms for optimizing production DNA sequencing,” Proceedings of the 11th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA ’00), 399-408 (2000).
• S. Boettcher and A.G. Percus, “Extremal optimization: Methods derived from co-evolution,” Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO ’99), 825-832 (1999).
• A.G. Percus and O.C. Martin, “The stochastic traveling salesman problem: Finite size scaling and the cavity prediction,” Journal of Statistical Physics 94, 739-758 (1999).
• A.G. Percus and D.C. Torney, “Greedy algorithms for optimized DNA sequencing,” Proceedings of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA ’99), S955-S956 (1999).
• A.G. Percus and O.C. Martin, “Scaling universalities of kth-nearest neighbor distances on closed manifolds,” Advances in Applied Mathematics 21, 424-436 (1998).
• N.J. Cerf, J. Boutet de Monvel, O. Bohigas, O.C. Martin and A.G. Percus, “The random link approximation for the Euclidean traveling salesman problem,” Journal de Physique I 7, 117-136 (1997).
• A.G. Percus and O.C. Martin, “Finite size and dimensional dependence of the Euclidean traveling salesman problem,” Physical Review Letters 76, 1188-1191 (1996).
• H.M. Lacker and A. Percus, “How do ovarian follicles interact? A many-body problem with unusual symmetry and symmetry-breaking properties,” Journal of Statistical Physics 63, 1133-1161 (1991).

Book Review:

• A.G. Percus, review of: D.P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics (3rd ed.), The American Statistician 65:2, 137-138 (May 2011).

Preprints and Technical Reports:

• B. Nettasinghe, A.G. Percus and K. Lerman, “Dynamics of affective polarization: from consensus to partisan divides,” arXiv:2403.16940 (2024).
• Y. Shi, D.J. Berry, J. Kath, S. Lodhy, A. Ly, A.G. Percus, J.D. Hyman, K. Moran, J. Strait, M.R. Sweeney, H.S. Viswanathan and P.H. Stauffer, “Bayesian learning of gas transport in three-dimensional fracture networks,” arXiv:2306.03416 (2023).
• V. Lloyd, J. Pena, A. Percus, C. Wang, R. Zhao, T. Hardin and M. Wilson, “Using Machine Learning Approaches to Predict Atomic-Scale Glass Failure in Environmental Conditions,” Sandia National Laboratories publication SAND2021-3233C (2021).
• M. Bradonjic, A. Hagberg, N.W. Hengartner, N. Lemons and A.G. Percus, “The phase transition in inhomogeneous random intersection graphs,” arXiv:1301.7320 (2013).
• A.G. Percus, “The traveling salesman problem and kth-nearest neighbors,” Los Alamos report LA-UR 06-6896 (2006).
• A.G. Percus, G. Istrate, S. Kasiviswanathan, S. Boettcher, N. Hengartner and B. Goncalves, “Belief propagation for graph bisection,” Los Alamos report LA-UR 06-6868 (2006).

Funding:

• PI, Los Alamos National Laboratory subcontract Mathematics Clinic: Reduced-Order Multifidelity Models for Predicting Flow in Fracture Networks (total budget: $145K)
  June 2022 – September 2024
• PI, Sandia National Laboratories subcontract Mathematics Clinic: Graph Theoretic Machine Learning Approaches to Predict Atomic Scale Fracture in Silica-Based Glasses (total budget: $140K)
  September 2019 – May 2021
• PI, Los Alamos National Laboratory subcontract Mathematics Clinic: Machine Learning Algorithms for Graph-Based Representations of Fracture Networks (total budget: $120K)
  September 2016 – May 2018
• PI, Southern California Edison contract Mathematics Clinic: Predicting and Minimizing Volatility in Power Outages (total budget: $60K)
  September 2015 – May 2016
• Co-PI, Air Force Office of Scientific Research MURI award Inferring Structure and Forecasting Dynamics on Evolving Networks (total budget: $538K)
  October 2010 – September 2015
• PI, Southern California Edison contract Mathematics Clinic: Topological Optimization of Reliability Volatility in Power Distribution Networks (total budget: $60K)
  September 2014 – May 2015
• Co-PI, DOE/ASCR award Dynamics through Randomness: New Mathematical Approaches for Complex Networks (total budget: $936K)
  January 2010 – September 2013
• Co-PI, Office of Naval Research award Mathematics of Communication and Control for Dynamic Mobile Aquatic Sensors (total budget: $165K)
  April 2010 – September 2012
• Co-PI, NSF EMT award Harnessing Statistical Physics for Computing and Communications (total budget: $388K)
  September 2008 – August 2012
• PI, Southern California Edison contract Mathematics Clinic: Optimizing Transmission of Renewable Energy (total budget: $60K)
  September 2011 – May 2012
• PI, Computing Research Association award CI Fellows (total budget: $193K)
  January 2011 – May 2012
• PI, Los Alamos National Laboratory subcontract Mathematics Clinic: Optimizing Smart Power Grids (total budget: $90K)
  September 2009 – May 2011
• Senior Personnel, NSF award Institute for Pure and Applied Mathematics renewal (total budget: $17M)
  July 2005 – June 2010
• Co-PI, DOE Laboratory-Directed Research and Development (LDRD) Directed Research project Physics of Algorithms (total budget: $4.9M)
  October 2006 – September 2009
• PI, DOE Weapons Supported Research (WSR) Computer Science Research Foundation project New Approaches to Fault Tolerance (total budget: $232K)
  October 2005 – September 2006
• Co-PI, DOE Laboratory-Directed Research and Development (LDRD) Directed Research project Statistical Physics of Infrastructure Networks (total budget: $4.5M)
  October 2003 – September 2006
• PI, DOE Laboratory-Directed Research and Development (LDRD) Exploratory Research project Improving Local Search (total budget: $840K)
  October 2002 – September 2005
• Co-PI, NSF ACT award Intelligent Extraction of Information from Graphs and High-Dimensional Data (total budget: $200K)
  July 2005
• Co-PI, DOE Laboratory-Directed Research and Development (LDRD) Exploratory Research project Extremal Optimization (total budget: $320K)
  October 1999 – September 2002
• Co-PI, DOE Laboratory-Directed Research and Development (LDRD) Exploratory Research project Combinatorial Optimization in Biology (total budget: $320K)
  October 1999 – September 2002

Teaching:

• Math 164/264: Scientific Computing
  Harvey Mudd College/Claremont Graduate University, Spring 2024
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2023 – Spring 2024
• Math 387: Discrete Mathematical Modeling
  Claremont Graduate University, Fall 2023
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2022 – Spring 2023
• Math 164/264: Scientific Computing
  Harvey Mudd College/Claremont Graduate University, Spring 2022
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2021 – Spring 2022
• Math 387: Discrete Mathematical Modeling
  Claremont Graduate University, Fall 2021
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2020 – Spring 2021
• Math 451: Statistical Mechanics and Lattice Models
  Claremont Graduate University, Spring 2020
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2019 – Spring 2020
• Math 387: Discrete Mathematical Modeling
  Claremont Graduate University, Fall 2019
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2018 – Spring 2019
• Math 164/264: Scientific Computing
  Harvey Mudd College/Claremont Graduate University, Spring 2018
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2017 – Spring 2018
• Math 387: Discrete Mathematical Modeling
  Claremont Graduate University, Fall 2017
• Math 164/264: Scientific Computing
  Harvey Mudd College/Claremont Graduate University, Spring 2017
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2016 – Spring 2017
• Math 387: Discrete Mathematical Modeling
  Claremont Graduate University, Fall 2016
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2015 – Spring 2016
• Math 293-393: Mathematics Clinic
  Claremont Graduate University, Fall 2014 – Spring 2015
• Math 451: Statistical Mechanics and Lattice Models
  Claremont Graduate University, Spring 2014
• Math 392: Mathematics Clinic
  Claremont Graduate University, Fall 2013
• Math 389: Discrete Mathematical Modeling
  Claremont Graduate University, Spring 2013
• Math 389: Discrete Mathematical Modeling
  Claremont Graduate University, Spring 2012
• Math 392-393: Mathematics Clinic
  Claremont Graduate University, Fall 2011 – Spring 2012
• Math 251: Probability
  Claremont Graduate University, Fall 2011
• Math 164/264: Scientific Computing
  Harvey Mudd College/Claremont Graduate University, Spring 2011
• Math 392-393: Mathematics Clinic
  Claremont Graduate University, Fall 2010 – Spring 2011
• Math 451: Statistical Mechanics
  Claremont Graduate University, Fall 2010
• Math 164/264: Scientific Computing
  Harvey Mudd College/Claremont Graduate University, Spring 2010
• Math 392-393: Mathematics Clinic
  Claremont Graduate University, Fall 2009 – Spring 2010
• Math 389: Discrete Mathematical Modeling
  Claremont Graduate University, Fall 2009
• Math 251: Probability
  Claremont Graduate University, Fall 2009
• Math 473: Combinatorial Optimization and Discrete Algorithms
  Claremont Graduate University, Spring 2009
• Math 164/264: Scientific Computing
  Harvey Mudd College/Claremont Graduate University, Spring 2009
• Math 290J: Discrete Algorithms and Phase Transitions
  UCLA, Fall 2006 – Spring 2007
• CS 260: Monte Carlo Algorithms
  UC Riverside, Spring 2003
• CS 260: Heuristic Methods in Optimization
  UC Riverside, Winter 2003
• Industry Mentor for RIPS undergraduate research project at IPAM: Improving the Performance of Local Search Heuristics
  UCLA, Summer 2002

PhD Committees:

• Justin Sunu
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2023 (committee chair)
• Maxwell Forst
  CGU Mathematics, PhD 2023
• David Kogan
  CGU Mathematics, PhD 2022
• Kevin Cotton
  CGU Mathematics, PhD 2021 (committee chair)
• Manuel Valera
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2021
• Sarun Seepun
  CGU Mathematics, PhD 2021 (committee chair)
• Adrian Cantu
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2020
• Siddhi Tavildar
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2020
• Afrooz Jahedi
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2020
• Mohsen Babaeian
  CGU/CSULB Joint Doctoral Program in Engineering and Computational Mathematics, PhD 2020
• Son Doan
  CGU/CSULB Joint Doctoral Program in Engineering and Computational Mathematics, PhD 2020
• Kristy Tran
  CGU/CSULB Joint Doctoral Program in Engineering and Computational Mathematics, PhD 2019
• Moein Parsinia
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2019
• Priscilla Kelly
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2019
• Uyen Hoang
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2019
• Tim VanderBeek
  CGU/CSULB Joint Doctoral Program in Engineering and Computational Mathematics, PhD 2019
• Shuan He
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2019
• Christina Duron
  CGU Mathematics, PhD 2019
• Nan Rao
  CGU Mathematics, PhD 2019 (committee co-chair)
• Huu Nguyen
  CGU Mathematics, PhD 2018
• Nasima Bhuiyan
  CGU/CSULB Joint Doctoral Program in Engineering and Industrial Applied Mathematics, PhD 2018
• Collins Allan
  CGU/CSULB Joint Doctoral Program in Engineering and Industrial Applied Mathematics, PhD 2018
• Anna Ma
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2018
• John Waynelovich
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2017
• William Spinella
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2017
• Jennifer Flenner
  CGU Mathematics, PhD 2017
• Deng Zhou
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2016
• Shaher Abdallah
  CGU/CSULB Joint Doctoral Program in Engineering and Industrial Applied Mathematics, PhD 2016
• Martin Kandes
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Mariangel Garcia
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Daniel Herrlin
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Peng Zhao
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Omair Zubairi
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Micah Schuster
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Mark Wilson
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Gene Ko
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Melodie Hallett
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• Xun Sun
  CGU Mathematics, PhD 2015
• Wei Wang
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2015
• David Heckman
  CGU Mathematics, PhD 2015
• Mohammad Abouali
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2014
• Nicolas Chaumont
  CGU/KGI Joint Doctoral Program in Computational and Systems Biology, PhD 2014
• Mary Thomas
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2014
• Cristina Garcia-Cardona
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2013 (committee chair)
• Jonathan Wilson
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2013
• Dany De Cecchis
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2012
• Sara Zarei
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2012
• Rafael Navarro
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2012
• Ron Caplan
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2012
• Joris Billen
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2012
• Sammuel Jalali
  CGU/CSULB Joint Doctoral Program in Engineering and Industrial Applied Mathematics, PhD 2012
• Justin Ku
  CGU Information Systems and Technology, PhD 2012
• Dwayne Chambers
  CGU Mathematics, PhD 2011
• Michael Vodhanel
  CGU Mathematics, PhD 2011
• Todd Coburn
  CGU/CSULB Joint Doctoral Program in Engineering and Industrial Applied Mathematics, PhD 2010
• Hai Ah Nam
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2010
• Rodrigo Negreiros
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2009
• Kun Marhadi
  CGU/SDSU Joint Doctoral Program in Computational Science, PhD 2009
• Milan Bradonjic
  UCLA Electrical Engineering, PhD 2008 (committee chair)
• Gunes Ercal
  UCLA Computer Science, PhD 2008

Professional Service and Distinctions:

• Director, CGU Engineering and Computational Mathematics Clinic
  July 2010 – present
• CGU Faculty Executive Committee
  August 2009 – December 2009, July 2018 – June 2021, July 2023 – present
• Chair, Executive Committee, Claremont Center for the Mathematical Sciences
  July 2021 – June 2024
• Avery Fellow, Claremont Graduate University and Harvey Mudd College
  January 2009 – June 2011, January 2017 – June 2018, January 2022 – June 2022, January 2024 – June 2024
• Task Committee for Faculty Review of President, Claremont Graduate University
  January 2022 – May 2022
• Director, Institute of Mathematical Sciences, Claremont Graduate University
  July 2013 – June 2015, July 2020 – June 2021
• Executive Committee, Claremont Center for the Mathematical Sciences
  July 2017 – June 2021
• Commencement Advisory Committee, Claremont Graduate University
  July 2017 – June 2020
• Board of Trustees Faculty Advisory Committee, Claremont Graduate University
  July 2016 – June 2018
• Faculty Advisory Committee on Indirect Cost Policies, Claremont Graduate University
  July 2017 – June 2018
• Society for Industrial and Applied Mathematics (SIAM) Committee on Gene Golub SIAM Summer School
  October 2013 – September 2017
• Task Committee for Faculty Review of Provost, Claremont Graduate University
  January 2014 – May 2014
• Program Committee, 2013 Workshop on Algorithms and Models for the Web Graph
• Program Committee, 2012 Workshop on Algorithms and Models for the Web Graph
• Organizing Committee, Algorithmic Game Theory workshop, Institute for Pure and Applied Mathematics, University of California, Los Angeles
  January 2011
• Organizing Committee, Physics of Algorithms workshop, Santa Fe, New Mexico
  September 2009
• Review Panel, DOE Office of Nonproliferation Research and Development
  March 2008
• Organizing Committee, Algorithms, Inference, and Statistical Physics workshop, Santa Fe, New Mexico
  May 2007
• Advisory Committee, NetSci07 – International Conference on Network Science, New York
  May 2007
• Board of Trustees (member ex-officio), Institute for Pure and Applied Mathematics, University of California, Los Angeles
  July 2003 – June 2006
• Science Advisory Board (member ex-officio), Institute for Pure and Applied Mathematics, University of California, Los Angeles
  July 2003 – June 2006
• Review Panel, DOE NNSA Office of Research and Engineering
  April 2005
• Review Panel, DOE Laboratory-Directed Research and Development (LDRD) Directed Research program
  June 2004
• Organizer, Phase Transitions in Computer Science symposium, Annual Meeting of the American Association for the Advancement of Science, Seattle
  February 2004
• Advisory Board, Los Alamos National Laboratory Research Library
  December 1998 – June 2003
• Program Committee, 2003 Congress on Evolutionary Computation
• Program Committee, 2002 Congress on Evolutionary Computation
• Organizing Committee chair, Phase Transitions and Algorithmic Complexity workshop, Institute for Pure and Applied Mathematics, University of California, Los Angeles
  June 2002
• Organizing Committee chair, Computational Complexity and Statistical Physics workshop, Santa Fe, New Mexico
  September 2001
• Co-organizer, Frontiers in Combinatorics workshop, Los Alamos, New Mexico
  July-August 1998