Markos Katsoulakis

Professor; Director, Applied Mathematics & Computation

Director, Applied Math MS Graduate Admissions

Office:

LGRT 1423G

Phone:

413-545-1331

Email:

Current Courses

Mathematical Modeling
MATH 456.2

TuTh 2:30 pm - 3:45 pm

Education

Ph.D. Brown University, 1993
B.A. National and Kapodistrian University, 1987

Mailing Address

Department of Mathematics and Statistics
Lederle Graduate Research Tower
University of Massachusetts
710 N. Pleasant Street
Amherst, MA 01003-9305

Research Interests

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

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)

Selected Publications

B. J. Zhang, M. A. Katsoulakis, A mean-field games laboratory for generative modeling, preprint
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
J. Birrell, P. Dupuis, M. A. Katsoulakis, Y. Pantazis, L. Rey-Bellet, Function-space regularized Rényi divergences, International Conference on Learning Representations (ICLR) 2023
J. Birrell, P. Dupuis, M. A. Katsoulakis, Y. Pantazis, L. Rey-Bellet, (f, Γ) -Divergences: Interpolating between f-Divergences and Integral Probability Metrics, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 2022
J. Birrell, M. A. Katsoulakis, L. Rey-Bellet, W. Zhu, Structure-preserving GANs, 39th International Conference on Machine Learning (ICML) 2022
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)
Y. Pantazis, D. Paul, M. Fasoulakis, Y. Stylianou, M. A. Katsoulakis, Cumulant GAN, IEEE Transactions on Neural Networks and Learning Systems, (2022)
J. Birrell, M. A. Katsoulakis, Y. Pantazis, Optimizing variational representations of divergences and accelerating their statistical estimation, IEEE Transactions on Information Theory, (2022)
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).
P. Birmpa, M. A. Katsoulakis, Uncertainty Quantification for Markov Random Fields, SIAM/ASA J. Uncertainty Quantification, 9(4), 1457–1498, (2021).
Eric J. Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky, GINNs: Graph Informed Neural Networks for multiscale physics, J. Comp. Phys. Volume 433, 110192 (2021).
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
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).
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.
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.
P. Vilanova, M. A. Katsoulakis, Data-driven, variational model reduction of high-dimensional reaction networks, J. Comp. Phys. Volume 401, 108997 (2020)
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).
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).
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).
G. Arampatzis, M. A. Katsoulakis and P. Plechac, Parallelization, processor communication and error analysis in lattice kinetic Monte Carlo, SIAM Num. Analysis, 52, no. 3, 11561182, (2014).
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).
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).
J. Feng , M. A. Katsoulakis, A Hamilton Jacobi theory for controlled gradient flows in infinite dimensions, Arch. Rat. Mech. Analysis 192, 2 , 275-310 (2009).
A. Sopasakis & M. A. Katsoulakis, Stochastic modeling and simulation of traffic flow: ASEP with Arrhenius look-ahead dynamics, SIAM J. Appl. Math., 66, 921-944, (2006).
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 Crystals, Nature Materials, 5, 400, (2006)
A. Chaterjee, M. A. Katsoulakis & D. G. Vlachos, Binomial distribution based τ -leap accelerated stochastic simulation, J. Chem. Phys. 122, 024112 (2005)
M. A. Katsoulakis, A. J. Majda, D. G. Vlachos. Coarse-grained stochastic processes for lattice systems, Proc. Natl. Acad. Sci. USA 100, 782-787, (2003).
M. A. Katsoulakis & A.E. Tzavaras, Contractive Relaxation Systems and the Scalar Multidimensional Conservation Law, Comm. P.D.E. 22, (1997), 195-233.
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 dynamics, Comm. Math. Phys., 169, (1995), 61-97.

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

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Current Courses

Education

Mailing Address

Department of Mathematics and Statistics