Chrysanthos E. Gounaris
Assistant Professor of Chemical Engineering
Bio
Professor Chrysanthos Gounaris received a Dipl. in Chemical Engineering (2002) and an M.Sc. in Automation Systems (2003) from the National Technical University of Athens. He then attended Princeton University, where he earned an M.A. (2005) and a Ph.D. in Chemical Engineering (2008). His doctoral thesis, pursued under the supervision of Professor Chris A. Floudas, explored the use of nonlinear modeling and global optimization techniques to study porous materials. After graduation, Professor Gounaris joined McKinsey & Co. as an Associate, where he provided consultation to petrochemical, pharmaceutical and consumer packagedgoods companies on a variety of projects of operational and strategic nature (20082010). He returned to academia to pursue postdoctoral research at Princeton University (20102013), after which he joined the Department of Chemical Engineering at Carnegie Mellon University as an Assistant Professor (2013).Education
2008, Princeton University, Ph.D. in Chemical Engineering
2005, Princeton University, M.A. in Chemical Engineering
2003, National Technical University of Athens, M.Sc. in Automation Systems
2002, National Technical University of Athens, Dipl. in Chemical Engineering
Research
Research in the Gounaris group contemplates the development of theory, quantitative methodologies and software implementations to address problems of industrial and scientific interest that involve complex decisionmaking. The research pertains to a wide spectrum of application areas, ranging from how process industries conduct their operations in a lean and efficient manner to how advanced materials are synthesized for optimal performance to how utility networks are developed in ways that minimize overall energy consumption.
Process Operations
Many decision problems arise in the context of managing a process industrial enterprise. Typically, these problems involve the selection of the best investments (e.g., selecting the site to build a new plant) or the reduction of variable costs (e.g., minimizing energy consumption during production, optimizing supplychain operations). The group develops mathematical models and associated optimization algorithms to address questions of this nature. Example areas of focus include production scheduling and the distribution of finished goods, interim products and raw materials. From a methodological perspective, the focus is on both exact and metaheuristic algorithms, including innovative hybrid schemes that exploit the strengths of both approaches, while significant effort is spent on addressing the effect of operational uncertainty so as to enable rigorous risk management in these contexts.
Materials Design
The technological explosion in Materials Science has set the stage with a number of interesting decisionmaking problems. The continuous discovery of new materials as well as the compilation of large computergenerated databases of hypothetical structures has created the need for computational tools that can quickly screen the various options and match materials to processes. At the same time, advances in our ability to fabricate materials with finelytuned microstructures has created the need for rigorous design tools to select the specific microstructures that maximize material performance. The group focuses on developing accurate correlations between the structure of materials and their functionality and stability in an application setting, as well as on how to effectively use these correlations in designing optimal structures and the processes to fabricate them. Material classes of interest include microporous materials, which are used abundantly in the process industry as adsorbents and membranes, as well as metallic and metaloxide surfaces and nanoparticles for use in various catalysis, chemical looping, semiconductor and optics applications.
Networks Optimization
Large and complex networks arise in a multitude of fields; from cell metabolism and epidemiology to traffic management and the Internet, to name but a few. Understanding network behavior via detailed mathematical modeling and simulation has traditionally helped us elucidate how a network's structure affects its performance. However, little work has been done in identifying the very network topologies that shall enable the network to behave in a most desirable manner. To further compound the complexity of the task, at the time a network is designed there usually exists significant uncertainty about how the network shall be operated in practice. In this context, the group develops rigorous quantitative techniques to design networks that exhibit satisfactory performance across the envelope of expected operational conditions. Applications of interest include the design of optimal pipeline networks that retain their economic viability and environmental sustainability over a wide range of potential demand profiles and over their operational lifetime, as well as the generation of small "insilico" model networks that mimic the behavior of large existing networks (e.g., the US electricity grid) in order to aid us in studying the latter.
Research Websites
Center for Advanced Process DecisionMaking
Process Systems Engineering
Wilton E. Scott Institute for Energy Innovation
Highlights
 AIChE Programming Coordinator, Area 10C, 2018

Associate Editor, Journal of Global Optimization, 2014present

Associate Editor, Optimization Letters, 2012present

Member of the Organizing Committee, ODYSSEUS2012, 5th International Workshop on Freight Transportation and Logistics, 2012
Select Awards and Honors
 GloverKlingman Prize, 2016
 Gordon Wu Prize for Excellence, 2008
 Excellence in Teaching Award, Princeton University, 2005
 Stanley J. Seeger Fellowship, 20032008
Publications
Recent Publications
Selected Publications
Full Publications
Recent Publications
N.H. Lappas and C.E. Gounaris (2017). Robust Optimization for Decisionmaking under Endogenous Uncertainty. Under Review. Eprint available at: http://www.optimizationonline.orgA. Subramanyam, C.E. Gounaris and W. Wiesemann (2017). KAdaptability in TwoStage MixedInteger Robust Optimization. Under Review. Eprint available at: http://www.optimizationonline.org
A. Subramanyam, F. Mufalli, J. Pinto and C.E. Gounaris (2017). Robust MultiPeriod Vehicle Routing Under Customer Order Uncertainty. Under Review. Eprint available at: http://www.optimizationonline.org
Selected Publications
A. Subramanyam and C.E. Gounaris (2017). A Decomposition Algorithm for the Consistent Traveling Salesman Problem with Waiting Times. Transportation Science, Articles In Advance. DOI 10.1287/trsc.2017.0741C.E. Gounaris, K. Rajendran, I.G. Kevrekidis and C.A. Floudas (2016). Designing Networks: A MixedInteger Linear Optimization Approach. Networks, 68(6):283301. Winner of the 2016 GloverKlingman Prize. DOI 10.1002/net.21699
C.L. Hanselman and C.E. Gounaris (2016). A Mathematical Optimization Framework for the Design of Nanopatterned Surfaces. AIChE Journal, 62(9):32503263. DOI 10.1002/aic.15359
N.H. Lappas and C.E. Gounaris (2016). Multistage Adjustable Robust Optimization for Process Scheduling Under Uncertainty. AIChE Journal, 62(5):16461667. Selected as Editor’s Choice Paper. DOI 10.1002/aic.15183
Full Publications
30. N.H. Lappas and C.E. Gounaris (2017). Robust Optimization for Decisionmaking under Endogenous Uncertainty. Under Review. Eprint available at: http://www.optimizationonline.org29. A. Subramanyam, C.E. Gounaris and W. Wiesemann (2017). KAdaptability in TwoStage MixedInteger Robust Optimization. Under Review. Eprint available at: http://www.optimizationonline.org
28. A. Subramanyam, F. Mufalli, J. Pinto and C.E. Gounaris (2017). Robust MultiPeriod Vehicle Routing Under Customer Order Uncertainty. Under Review. Eprint available at: http://www.optimizationonline.org
27. A. Subramanyam and C.E. Gounaris (2017). A Decomposition Algorithm for the Consistent Traveling Salesman Problem with Waiting Times. Transportation Science, Articles In Advance. DOI 10.1287/trsc.2017.0741
26. C.E. Gounaris, K. Rajendran, I.G. Kevrekidis and C.A. Floudas (2016). Designing Networks: A MixedInteger Linear Optimization Approach. Networks, 68(6):283301. Winner of the 2016 GloverKlingman Prize. DOI 10.1002/net.21699
25. C.E. Gounaris, P.P. Repoussis, C.D. Tarantilis, W. Wiesemann and C.A. Floudas (2016). An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem. Transportation Science, 50(4):12391260. DOI 10.1287/trsc.2014.0559
24. C.L. Hanselman and C.E. Gounaris (2016). A Mathematical Optimization Framework for the Design of Nanopatterned Surfaces. AIChE Journal, 62(9):32503263. DOI 10.1002/aic.15359
23. N.H. Lappas and C.E. Gounaris (2016). Multistage Adjustable Robust Optimization for Process Scheduling Under Uncertainty. AIChE Journal, 62(5):16461667. Selected as Editor’s Choice Paper. DOI 10.1002/aic.15183
22. A. Subramanyam and C.E. Gounaris (2016). A BranchandCut Framework for the Consistent Traveling Salesman Problem. European Journal of Operational Research, 248(2):384395. DOI 10.1016/j.ejor.2015.07.030
21. M.G. Taylor, N. Austin, C.E. Gounaris and G. Mpourmpakis (2015). Catalyst Design Based on Morphology and EnvironmentDependent Adsorption on Metal Nanoparticles. ACS Catalysis, 5(11):62966301. DOI 10.1021/acscatal.5b01696
20. C.E. Gounaris, E.L. First and C.A. Floudas (2013). Estimation of Diffusion Anisotropy in Microporous Crystalline Materials and Optimization of Crystal Orientation in Membranes. The Journal of Chemical Physics, 139(12):124703. DOI 10.1063/1.4821583
19. C.E. Gounaris, W. Wiesemann and C.A. Floudas (2013). The Robust Capacitated Vehicle Routing Problem Under Demand Uncertainty. Operations Research, 61(3):677693. DOI 10.1287/opre.1120.1136
18. E.L. First, C.E. Gounaris and C.A. Floudas (2013). Predictive Framework for ShapeSelective Separations in ThreeDimensional Zeolites and MetalOrganic Frameworks. Langmuir, 29(18):55995608. DOI 10.1021/la400547a
17. E.L. First, C.E. Gounaris and C.A. Floudas (2012). Stereochemicallyconsistent Reaction Mapping and Identification of Multiple Reaction Mechanisms through Integer Linear Optimization. Journal of Chemical Information and Modeling, 52(1):8492. DOI 10.1021/ci200351b
16. E.L. First, C.E. Gounaris, J. Wei and C.A. Floudas (2011). Computational Characterization of Zeolite Porous Networks: An Automated Approach. Physical ChemistryChemical Physics, 13(38):1733917358. DOI 10.1039/c1cp21731c
15. C.E. Gounaris, K. Rajendran, I.G. Kevrekidis and C.A. Floudas (2011). Generation of Networks with Prescribed DegreeDependent Clustering. Optimization Letters, 5(3):435451. DOI 10.1007/s115900110319x
14. R. Misener, C.E. Gounaris and C.A. Floudas (2010). Mathematical Modeling and Global Optimization of LargeScale Extended Pooling Problems with the (EPA) Complex Emissions Constraints. Computers & Chemical Engineering, 34(9):14321456. DOI 10.1016/j.compchemeng.2010.02.014
13. C.E. Gounaris, J. Wei, C.A. Floudas, R. Ranjan and M. Tsapatsis (2010). Rational Design of Shape Selective Separation and Catalysis: Lattice Relaxation and Effective Aperture Size. AIChE Journal, 56(3):611632. DOI 10.1002/aic.12016
12. H.C. Lu, H.L. Li, C.E. Gounaris and C.A. Floudas (2010). Convex Relaxation for Solving Posynomial Programs. Journal of Global Optimization, 46(1):147154. DOI 10.1007/s1089800994142
11. J. Wei, C.A. Floudas and C.E. Gounaris (2009). Search Engines for Shape Selectivity. Catalysis Letters, 133(12):234241. DOI 10.1007/s1056200901550
10. C.A. Floudas and C.E. Gounaris (2009). A Review of Recent Advances in Global Optimization. Journal of Global Optimization, 45(1):338. DOI 10.1007/s1089800893328
9. R. Ranjan, S. Thust, C.E. Gounaris, M. Woo, C.A. Floudas, M.v. Keitz, K.J. Valentas, J. Wei and M. Tsapatsis (2009). Adsorption of Fermentation Inhibitors from Lignocellulosic Biomass Hydrolyzates for Improved Ethanol Yield and ValueAdded Product Recovery. Microporous and Mesoporous Materials, 122(13):143148. DOI 10.1016/j.micromeso.2009.02.025
8. R. Misener, C.E. Gounaris and C.A. Floudas (2009). Global Optimization of Gas Lifting Operations: A Comparative Study of Piecewise Linear Formulations. Industrial & Engineering Chemistry Research, 48(13):60986104. DOI 10.1021/ie8012117
7. C.E. Gounaris, R. Misener and C.A. Floudas (2009). Computational Comparison of PiecewiseLinear Relaxations for Pooling Problems. Industrial & Engineering Chemistry Research, 48(12):57425766. DOI 10.1021/ie8016048
6. C.E. Gounaris and C.A. Floudas (2008). Convexity of Products of Univariate Functions and Convexification Transformations in Geometric Programming. Journal of Optimization Theory and Applications, 138(3):407427. DOI 10.1007/s1095700894026
5. C.E. Gounaris and C.A. Floudas (2008). Tight Convex Underestimators for C2Continuous Problems: II. Multivariate Functions. Journal of Global Optimization, 42(1):6989. DOI 10.1007/s1089800892888
4. C.E. Gounaris and C.A. Floudas (2008). Tight Convex Underestimators for C2Continuous Problems: I. Univariate Functions. Journal of Global Optimization, 42(1):5167. DOI 10.1007/s1089800892879
3. C.E. Gounaris, J. Wei and C.A. Floudas (2006). Rational Design of Shape Selective Separation and Catalysis: II. Mathematical Model and Computational Studies. Chemical Engineering Science, 61(24):79497962. DOI 10.1016/j.ces.2006.09.011
2. C.E. Gounaris, C.A. Floudas and J. Wei (2006). Rational Design of Shape Selective Separation and Catalysis: I. Concepts and Analysis. Chemical Engineering Science, 61(24):79337948. DOI 10.1016/j.ces.2006.09.012
1. G.D. Bellos, L.E. Kallinikos, C.E. Gounaris and N.G. Papayannakos (2005). Modelling of the Performance of Industrial HDS Reactors using a Hybrid Neural Network Approach. Chemical Engineering and Processing, 44(5):505515. DOI 10.1016/j.cep.2004.06.008