LGRT 1423G

Office Hours:
M 4-5:30PM, Th 2:30-4PM in LGRT 1423G or by appointment
  • Markos Katsoulakis is working at the interface of Applied & Computational Mathematics and Data Science, focusing on  machine learning,  uncertainty quantification and applications in  materials and catalysis.
  • He is the Director for the Center of Applied Mathematics at the University of Massachusetts and  a member of the Executive Committee of the  UMASS TRIPODS Institute for Theoretical Foundations of Data Science.  
  • He currently serves in the Editorial Board of the SIAM/ASA Journal of Uncertainty Quantification and the Editorial Board of the SIAM Mathematical Modeling and Computation book series. He was an Associate Editor of SIAM Journal of Mathematical Analysis between 2002-2014.
  • Brief CV (2023)


Ph.D. Brown University, 1993

B.A. National and Kapodistrian University, 1987


Generative Modeling, Scientific Machine Learning, Information Theory, Uncertainty Quantification, Multi-scale Methods

Selected Publications

  1. B. J. Zhang, M. A. Katsoulakis, A mean-field games laboratory for generative modeling, preprint
  2. Z. Chen, M. A. Katsoulakis, L. Rey-Bellet, W.  Zhu, Sample Complexity of Probability Divergences under Group Symmetry)International Conference on Machine Learning (ICML) 2023 
  3. J. Birrell, P. Dupuis, M. A. Katsoulakis, Y. Pantazis, L. Rey-Bellet, Function-space regularized Rényi divergencesInternational Conference on Learning Representations (ICLR) 2023
  4. J. Birrell, P. Dupuis, M. A. Katsoulakis, Y. Pantazis, L. Rey-Bellet, (f, Γ) -Divergences: Interpolating between f-Divergences and Integral Probability MetricsThirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 2022 
  5. J. Birrell,  M. A. Katsoulakis, L. Rey-Bellet, W. Zhu, Structure-preserving GANs39th International Conference on Machine Learning (ICML) 2022  
  6. P. Birmpa, J. Feng, M. A. Katsoulakis, L. Rey-Bellet, Model Uncertainty and Correctability for Directed Graphical Models, SIAM/ASA Journal on Uncertainty Quantification 10 (4),1461-1512, (2022)
  7. Y. Pantazis, D. Paul, M. Fasoulakis, Y. Stylianou, M. A. Katsoulakis, Cumulant GANIEEE Transactions on Neural Networks and Learning Systems, (2022) 
  8. J. Birrell, M. A. Katsoulakis, Y. Pantazis, Optimizing variational representations of divergences and accelerating their statistical estimation, IEEE Transactions on Information Theory, (2022) 
  9. J. Birrell, P. Dupuis, M. A. Katsoulakis, Y. Pantazis, L. Rey-Bellet, (f, Γ) -Divergences: Interpolating between f-Divergences and Integral Probability Metrics,  Journal of Machine Learning Research, 23 (39), 1-70, (2022).  
  10. P. Birmpa, M. A. Katsoulakis, Uncertainty Quantification for Markov Random Fields SIAM/ASA J. Uncertainty Quantification, 9(4), 1457–1498, (2021).
  11. Eric J. Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky, GINNs: Graph Informed Neural Networks for multiscale physicsJ. Comp. Phys. Volume 433, 110192 (2021).
  12. Feng J, Lansford J, Katsoulakis M, Vlachos D. Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences, Science Advances. 2020 October 14; 6(42):eabc3204
  13. K. Um, E. J. Hall, M. A. Katsoulakis, D. M. Tartakovsky. Causality and Bayesian Network PDEs for multiscale representations of porous media, J. Comp. Phys. 394:658-678 (2019).
  14. P. Dupuis, M. A. Katsoulakis, Y. Pantazis, L. Rey-Bellet Sensitivity Analysis for Rare Events based on Renyi Divergence, Annals Applied Prob., 2020 August; 30(4):1507-1533.
  15. Gourgoulias K, Katsoulakis M, Rey-Bellet L, Wang J. How Biased Is Your Model? Concentration Inequalities, Information and Model Bias, IEEE Transactions on Information Theory. 2020; 66(5):3079-3097.
  16. P. Vilanova, M. A. Katsoulakis, Data-driven, variational model reduction of high-dimensional reaction networksJ. Comp. Phys. Volume 401, 108997 (2020)
  17. V. Harmandaris, E. Kalligiannaki, M. A. Katsoulakis, P. Plechac. Path-space Variational Inference for non-equilibrium coarse-grained systems, J. Comp. Phys. 314,355-383 (2016).
  18. P. Dupuis, M. A. Katsoulakis, Y. Pantazis, P. Plechac. Path-Space Information Bounds for Uncertainty Quantification and Sensitivity Analysis of Stochastic Dynamics, SIAM/ASA J. Uncertainty Quantification, 4(1), 80-111, (2016).
  19. J. E. Sutton, W. Guo, M. A. Katsoulakis & D. G. Vlachos. Effects of correlated parameters and uncertainty in electronic-structure-based chemical kinetic modelling, Nature Chemistry 8, 331–337 (2016).
  20. G. Arampatzis, M. A. Katsoulakis and P. Plechac,  Parallelization, processor communication and error analysis in lattice kinetic Monte CarloSIAM Num. Analysis, 52, no. 3, 11561182, (2014).
  21. Y. Pantazis, D.G. Vlachos and M. A. Katsoulakis, Parametric Sensitivity Analysis for Biochemical Reaction Networks based on Pathwise Information Theory, BMC Bionformatics 14:311, (2013).
  22. G. Arampatzis, M. A. Katsoulakis, P. Plechác, M. Taufer, L. Xu. Hierarchical fractional step approximations and parallel kinetic Monte Carlo algorithms. J. Comp. Phys. 231, 7795-7814, (2012).
  23. J. Feng ,  M. A. Katsoulakis, A Hamilton Jacobi theory for controlled gradient flows in infinite dimensionsArch. Rat. Mech. Analysis 192, 2 , 275-310 (2009).
  24. A. Sopasakis & M. A. Katsoulakis, Stochastic modeling and simulation of traffic flow: ASEP with Arrhenius look-ahead dynamicsSIAM J. Appl. Math., 66, 921-944, (2006).
  25. T. M. Davis, T. O.Drews, H. Ramanan, C. He, J. Dong, H. Schnablegger, M. A. Katsoulakis, E. Kokkoli, A. V. McCormick, R. L.Penn, and M. Tsapatsis. Mechanistic Principles of Nanoparticle Evolution to Zeolite CrystalsNature Materials, 5, 400, (2006)
  26. A. Chaterjee, M. A. Katsoulakis & D. G. Vlachos, Binomial distribution based τ -leap accelerated stochastic simulationJ. Chem. Phys. 122, 024112 (2005)
  27. M. A. Katsoulakis, A. J. Majda, D. G. Vlachos. Coarse-grained stochastic processes for lattice systemsProc. Natl. Acad. Sci. USA 100, 782-787, (2003).
  28. M. A. Katsoulakis & A.E. Tzavaras, Contractive Relaxation Systems and the Scalar Multidimensional Conservation LawComm. P.D.E. 22, (1997), 195-233.
  29. M. A. Katsoulakis & P. E. Souganidis, Generalized motion by mean curvature as a macroscopic limit of stochastic Ising models with long range interactions and Glauber dynamicsComm. Math. Phys., 169, (1995), 61-97.

For a complete list see:  Google Scholar page & Researchgate profile