Research topics

• Reinoso J, Paggi M, Linder C (2017). Phase field modeling of brittle fracture for enhanced assumed strain shells at large deformations: formulation and finite element implementation. COMPUTATIONAL MECHANICS, vol. 59, p. 981-1001, doi: 10.1007/s00466-017-1386-3
• Lenarda P, Paggi M, Ruiz Baier R (2017). Partitioned coupling of advection–diffusion–reaction systems and Brinkman flows. JOURNAL OF COMPUTATIONAL PHYSICS, vol. 344, p. 281-302, doi: 10.1016/j.jcp.2017.05.011
• Lenarda P, Gizzi A, Paggi M (2018). A modeling framework for electro-mechanical interaction between excitable deformable cells. EUROPEAN JOURNAL OF MECHANICS. A, SOLIDS, vol. 72, p. 374-392, doi: 10.1016/j.euromechsol.2018.06.001

Reference Faculty
Marco Paggi (MUSAM) and Mirco Tribastone (SYSMA)

• Reinoso J, Paggi M (2014). A consistent interface element formulation for geometrical and material nonlinearities. COMPUTATIONAL MECHANICS, vol. 54, p. 1569-1581, doi: 10.1007/s00466-014-1077-2
• Paggi M, Reinoso J (2017). Revisiting the problem of a crack impinging on an interface: A modeling framework for the interaction between the phase field approach for brittle fracture and the interface cohesive zone model. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, vol. 321, p. 145-172, doi: 10.1016/j.cma.2017.04.004
• Mariggiò G, Reinoso J, Paggi M, Corrado M (2018). Peeling of thick adhesive interfaces: The role of dynamics and geometrical nonlinearity. MECHANICS RESEARCH COMMUNICATIONS, vol. 94, p. 21-27, doi: 10.1016/j.mechrescom.2018.08.018

• Paggi M, Barber JR (2011). Contact conductance of rough surfaces composed of modified RMD patches. INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, vol. 54, p. 4664-4672, doi:10.1016/j.ijheatmasstransfer.2011.06.011
• Vakis AI, Yastrebov VA, Scheibert J, Nicola L, Dini D, Minfray C, Almqvist A, Paggi M, Lee S, Limbert G, Molinari JF, Anciaux G, Aghababaei R, Echeverri Restrepo S, Papangelo A, Cammarata A, Nicolini P, Putignano C, Carbone G, Stupkiewicz S, Lengiewicz J, Costagliola G, Bosia F, Guarino R, Pugno NM, Müser MH, Ciavarella M (2018). Modeling and simulation in tribology across scales: An overview. TRIBOLOGY INTERNATIONAL, vol. 125, p. 169-199, doi: 10.1016/j.triboint.2018.02.005
• Paggi M, Reinoso J (2018). A variational approach with embedded roughness for adhesive contact problems, MECHANICS OF ADVANCED MATERIALS AND STRUCTURES, in press, doi:10.1080/15376494.2018.1525454

Abstract
Dynamical systems are a fundamental mathematical model to describe predict the behavior of natural as well as engineered processes. Our capability to gain relevant insights details is however hindered by the large dimensionality of such models when describing systems characterized by a large degree of complexity. Coarse graining, model reduction, and model abstraction are among the several different keywords with which a wide range of disciplines refer to the topic of simplifying a given dynamical system into a smaller one that preserves key observables of interest to the modeler. The candidate will have the opportunity to work on the development of new coarse-graining methods and algorithms, with applications related to models in various disciplines including automation, computer science, statistical physics, and systems biology.
Keywords
coarse graining; model reduction; dynamical systems
Reference Faculty
Mirco Tribastone (SYSMA), Guido Caldarelli (NETWORKS), and Diego Garlaschelli (NETWORKS)
References

• S. Tognazzi, M. Tribastone, M. Tschaikowski, and A. Vandin, “Backward invariance for linear differential algebraic equations,” in 57th IEEE Conference on Decision and Control (CDC), 2018
• L. Cardelli, M. Tribastone, M. Tschaikowski, and A. Vandin, “Maximal aggregation of polynomial dynamical systems,” Proceedings of the National Academy of Sciences, vol. 114, no. 38, pp. 10 029–10 034, 2017.
• L. Cardelli, M. Tribastone, A. Vandin, and M. Tschaikowski, “ERODE: A tool for the evaluation and reduction of ordinary differential equations,” in Tools and Algorithms for the Construction and Analysis of Systems - 23rd International Conference, TACAS, 2017.

Abstract
The goal is to develop autonomous, self-reconfigurable, control systems that are able to learn how to achieve their objectives from data, adapting themselves to external stimuli such as changing environmental conditions and variations of the dynamic properties of the process. In particular, new approaches will be developed for synthesizing control systems from data that are optimal, robust, and can cope with operating constraints on input and output variables, addressing both model-based methods, where an open-loop model of the process is identified from data, and model-free methods, that directly synthesize the control law from data.
Keywords
Systems identification, machine learning, reinforcement learning, model predictive control
Reference Faculty
A. Bemporad
References

• R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. Second Edition. MIT press Cambridge, 2018
• F. L. Lewis, D. Vrabie, and K. G. Vamvoudakis, “Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers,” IEEE Control Systems, vol. 32, no. 6, pp. 76–105, 2012.
• B. Recht, "A Tour of Reinforcement Learning: The View from Continuous Control", 2018.
• D. Selvi, D. Piga, and A. Bemporad, “Towards direct data-driven control design of optimal controllers,” in Proc. European Control Conf., Limassol, Cyprus, 2018, pp. 2836–2841.
• V. Breschi, D. Piga, and A. Bemporad, “Piecewise affine regression via recursive multiple least squares and multicategory discrimination,” Automatica, vol. 73, pp. 155–162, Nov. 2016.
• D. Piga, S. Formentin, and A. Bemporad, “Direct data-driven control of constrained systems,” IEEE Transactions on Control Systems Technology, vol. 26, no. 4, pp. 1422–1429, Jul 2018.
• J. R. Salvador, D. Munoz de la Pena, T. Alamo, and A. Bemporad, “Data-based predictive control via direct weight optimization,” in 6th IFAC Conference on Nonlinear Model Predictive Control, pp. 437-442, Madison, WI, USA, 2018.
• D. Masti and A. Bemporad, “Learning nonlinear state-space models using deep autoencoders,” in Proc. 57th IEEE Conf. on Decision and Control. 2018./

Abstract
In recent years, robots have spread everywhere and are able to accomplish numerous tasks in various applications. While most success has been obtained in restricted environments, the current challenges include being able to operate safely (e.g. not harming people around the robots), coordinating multiple robots and adapting to changing environments.In this context, Model Predictive Control is a valuable tool for tackling constrained multiple input multiple output (possibly nonlinear) systems. While both theory and algorithms have been largely investigated in the literature, many questions still need to be answered before it will be possible to safely and effectively deploy robots to cooperate with human beings. Autonomous driving is currently perhaps the most thrilling field of research in this context.
Keywords
Robotics, autonomous driving, model predictive control
Reference Faculty
M. Zanon
References

• Craig, J. J. (2005). Introduction to robotics: mechanics and control (Vol. 3, pp. 48-70). Upper Saddle River, NJ, USA:: Pearson/Prentice Hall.
• Borrelli, F., Bemporad, A., & Morari, M. (2017). Predictive control for linear and hybrid systems. Cambridge University Press.- Rawlings, J. B., & Mayne, D. Q. (2009). Model predictive control: Theory and design.
• M. Graf Plessen, D. Bernardini, H. Esen, and A. Bemporad. Spatial-based predictive control and geometric corridor planning for adaptive cruise control coupled with obstacle avoidance. IEEE Trans. Contr. Systems Technology, vol. 26, no. 1, pp. 38–50, 2018.
• R. Hult, M. Zanon, S. Gros, and P. Falcone. Optimal Coordination of Automated Vehicles at Intersections: Theory and Experiments. IEEE Transactions on Control Systems Technology, (in press, available online)
• I. Batkovic, M. Zanon, N. Lubbe and P. Falcone. A Computationally Efficient Model for Pedestrian Motion Prediction. Proceedings of the European Control Conference (ECC), 2018

Abstract
Distributed Optimization has been mostly investigated in order to address problems which have an intrinsically distributed nature, including smart cities, autonomous driving, power grids, etc. By restricting the focus on convex problems, it is possible to apply a wide range of algorithms. Moreover, the assumption of lossless and instantaneous communication links makes it possible to deploy algorithms which require simple computations but rely on large amounts of communication. In order to extend the applicability of distributed optimization, investigating nonconvex optimization strategies over lossy communication channels becomes of paramount importance. While only few results are currently available for this setting, some encouraging results have recently been obtained, which pave the road for further investigations on the topic.  Notable examples include networked and cooperative control scenarios applied to, e.g., autonomous driving, formation flight, etc.
Keywords
Nonconvex optimization, distributed optimization, networked optimization
Reference Faculty
M. Zanon
References

• S. Boyd, L. Vandenberghe, "Convex optimization", Cambridge Univ. Press, 2004
• J. Nocedal, S.J. Wright, Numerical optimization 2nd ed, 2006
• R. Hult, M. Zanon, S. Gros and P. Falcone. Primal Decomposition of the Optimal Coordination of Vehicles at Traffic Intersections.Proceedings of the Conference on Decision and Control (CDC), 2016
• M. Zanon, S. Gros, P. Falcone and H. Wymeersch. An Asynchronous Algorithm for Optimal Vehicle Coordination at Traffic Intersections. Proceedings of the World Congress of the International Federation of Automatic Control, 2017

Abstract
Model Predictive Control (MPC) is one of the most successful techniques adopted in industry to control multivariable systems in an optimized way under constraints on input and output variables. In MPC, the manipulated inputs are computed in real time by solving a mathematical programming problem, most frequently a Quadratic Program (QP). Topics of research are available on how to formulate and solve MPC problems with higher throughput than is currently available, while maintaining the numerical optimization code simple, certifiable for the worst-case execution time, and robust with respect to limited machine precision.
Keywords
Convex optimization, numerical methods, model predictive control
Reference Faculty
A. Bemporad
References

• G. Cimini and A. Bemporad, "Exact complexity certification of active-set methods for quadratic programming," IEEE Trans. Automatic Control, vol. 62, no. 12, pp. 6094–6109, 2017.
• A. Bemporad, "A numerically stable solver for positive semi-definite quadratic programs based on nonnegative least squares," IEEE Trans. Automatic Control, vol. 63, no. 2, pp. 525–531, 2018.
• A. Bemporad and V.V. Naik, "A numerically robust mixed-integer quadratic programming solver for embedded hybrid model predictive control," in 6th IFAC Conf. on Nonlinear Model Predictive Control, Madison, WI, 2018, pp. 502–507.
• N. Saraf and A. Bemporad, "Fast model predictive control based on linear input/output models and bounded-variable least squares," in Proc. 56th IEEE Conf. on Decision and Control, Melbourne, Australia, 2017.
• A. Bemporad, M. Morari, V. Dua, and E.N. Pistikopoulos, "The explicit linear quadratic regulator for constrained systems," Automatica, vol. 38, no. 1, pp. 3–20, 2002.
• J. Nocedal, S.J. Wright, Numerical optimization 2nd ed, 2006
• R. Verschueren, M. Zanon, R. Quirynen and M. Diehl. Time-optimal Race Car Driving using an Online Exact Hessian based Nonlinear MPC Algorithm. Proceedings of the European Control Conference (ECC), 2016

Abstract
The Software Supply Chain (SSC) is a cornerstone of the industrial society on which many other Supply Chains (SCs) depend. The continuous demand/integration of the computing systems into SCs is pushing the development and distribution of software. To cope with this growing request many companies are including open source software (OSS) in their software products. OSS has many advantages, for example, it prevents that the software producer does not acquires a strong bargaining position on the consumer. However, the flip side is that the producer of a OSS has no obligation to maintain, improve or fix her software. All in all, the OSS ranges from small scale projects, with limited or even no security plan, to community projects that release periodic security updates. Such heterogeneity makes it difficult to understand the actual risks when one wants to integrate a OSS in his project. From a methodological point of view, the project aims at answering the following questions: (i) what are the conditions to make the formal verification a valuable asset in the SSC? (ii) can we design a mechanism based on economic rewards that push participant to find and fix bugs in OSS software? (iii) can the blockchain technology be used to implement a decentralized framework for the formal verification of security properties of OSS? From a practical point of view, the project aims at designing and implementing a blockchain service for the security analysis and patching of the OSS, where developers and security analysts cooperate efficiently.
Keywords
Formal methods, software verification, blockchain, DLT, contract-driven development, mobile code security
Reference Faculty
Letterio Galletta
Research units involved
SysMA, AXES

Abstract
Symbolic execution is a powerful technique to spot out corner cases, e.g., vulnerabilities, in the semantics of a program. As a matter of fact, it replaces the standard semantics (referring to specific values) with a symbolic one (manipulating abstract expressions). Unfortunately, symbolic execution does not scale on large programs. For this reason, hybrid techniques have been proposed (e.g. concolic testing and symbolic backward execution). The goal of this project is to combine the symbolic analysis of a program with a test execution environment driven by an evolutionary optimization strategy. The symbolic analysis is applied to obtain a compact (thus computable) specification of the conditions that a test must satisfy to trigger a certain vulnerability. Instead the evolutionary algorithm drives the refinement process that, starting from some random tests, leads to the convergence toward the desired ones. The convergence criteria is based on the optimization of a fitness function derived from the symbolic specification.
References
Formal methods, vulnerability analysis, security testing, evolutionary algorithms, white-box testing, binary analysis
Reference Faculty
Gabriele Costa
Research units involved
SysMA, AXES

• Eigenvector centrality for characterization of protein allosteric pathways
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• Complex Agent Based Models M. Gallegati Springer 2018
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• Thöni, Christian, and Simon Gächter. "Peer effects and social preferences in voluntary cooperation: A theoretical and experimental analysis." Journal of Economic Psychology 48 (2015): 72-88.
• Belloc, Marianna, Ennio Bilancini, Leonardo Boncinelli, and Simone D’Alessandro. "Intuition and Deliberation in the Stag Hunt Game" (2019), mimeo
• Bilancini, Ennio, and Leonardo Boncinelli. "The co-evolution of cooperation and defection under local interaction and endogenous network formation." Journal of Economic Behavior & Organization 70.1-2 (2009): 186-195.

• Bilancini, Ennio, Leonardo Boncinelli, and Jiabin Wu. "The interplay of cultural intolerance and action-assortativity for the emergence of cooperation and homophily." European Economic Review 102 (2018): 1-18.
• Bilancini, Ennio, and Leonardo Boncinelli. "Social coordination with locally observable types." Economic Theory 65.4 (2018): 975-1009.
• Bilancini, Ennio, and Leonardo Boncinelli. "Instrumental cardinal concerns for social status in two-sided matching with non-transferable utility." European Economic Review 67 (2014): 174-189.

• Alós-Ferrer, Carlos. "A Review Essay on Social Neuroscience: Can Research on the Social Brain and Economics Inform Each Other?." Journal of Economic Literature 56.1 (2018): 234-64.
• Griessinger, Thibaud, and Giorgio Coricelli. "The neuroeconomics of strategic interaction." Current Opinion in Behavioral Sciences 3 (2015): 73-79.
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• Bilancini, Ennio, Leonardo Boncinelli, and Alan Mattiassi. "Assessing Actual Strategic Behavior to Construct a Measure of Strategic Ability." Frontiers in Psychology (2019) forthcoming
• Gill, David, and Victoria Prowse. "Cognitive ability, character skills, and learning to play equilibrium: A level-k analysis." Journal of Political Economy 124.6 (2016): 1619-1676.
• Alaoui, Larbi, and Antonio Penta. "Endogenous depth of reasoning." Review of Economic Studies 83.4 (2015): 1297-1333.

• Rice, Thomas. "The behavioral economics of health and health care." Annual review of public health 34 (2013): 431-447.
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• Bartolini, Stefano, Ennio Bilancini, Luigino Bruni, and Pierluigi Porta, eds. Policies for happiness. Oxford University Press, 2016.

• Bakhshi, H., Downing, J., Osborne, M., & Schneider, P. (2017). The future of skills: employment in 2030. London: Nesta, Oxford-Martin, Pearson.
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• Andrews, D. Criscuolo C. and Gal P. (2016), “The Best versus the Rest: The Global Productivity Slowdown, Divergence across Firms and the Role of Public Policy”, OECD Productivity Working Papers, No. 5
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Abstract
Decision making process is a main part of managing activities across all kinds of firms. Every decision is apt to product consequences for the firm, also impacting its performances. In the last decades, management research is increasingly focusing on the behavioral approach: integrating psychological theories and methods to management science can increase the understanding of how cognitive and emotional processes work and shape individuals’ decisions and actions. This line of research shall study how cognitive and social psychology they apply to strategic management theory and practice (namely, behavioral strategy). The behavioral approach uses the cognitive psychology, which is a branch of psychology that seeks to understand the internal mental processes of thought. The main research theme is business organization, not only the man. The candidate will embrace topics in the existing core of behavioral strategy (such as decision biases and cognitive schema), while encouraging innovations and de-biasing actions in a managerial context.
Reference Faculty
Nicola Lattanzi, Emiliano Ricciardi
References

• Powell, T. C., Lovallo, D., & Fox, C. R. (2011). Behavioral strategy. Strategic Management Journal, 32(13), 1369-1386.
• Lattanzi, N. (2013). Management Science and Neuroscience Impact. Decision Making Process, Entrepreneurship and Business Strategy. McGraw-Hill.
• Ricciardi, E., Bonino, D., Gentili, C., Sani, L., Pietrini, P., & Vecchi, T. (2006). Neural correlates of spatial working memory in humans: a functional magnetic resonance imaging study comparing visual and tactile processes. Neuroscience, 139(1), 339-349.
• Intesa San Paolo Innovation Center & IMT School for Advanced Studies Lucca (2018). Innovation Trend Report: Neuroscience Impact. Brain and Business.

Abstract
Human beings manage all organizations; they are made of men and are imperfect systems. Firms’ long-term success greatly depends on how managers can select and govern human resources, also being able to understand the nature and complexities of human beings as multi-faceted individuals. Individuals also influence firm performances: employing the right type of person (with certain skills, behaviors and abilities) can help an organization increase productivity and maintain a competitive advantage. This line of research studies how personality influences job and firm performances.
Reference Faculty
Nicola Lattanzi, Massimo Riccaboni. Andrea Morescalchi
References

• Barrick, M. R., Stewart, G. L., & Piotrowski, M. (2002). Personality and job performance: test of the mediating effects of motivation among sales representatives. Journal of Applied Psychology, 87(1), 43.
• Seriki, O. K., Nath, P., Ingene, C. A., & Evans, K. R. (2018). How complexity impacts salesperson counterproductive behavior: The mediating role of moral disengagement. Journal of Business Research.
• Lattanzi, N., Menicagli, D., & Dal Maso, L. (2016). Neuroscience Evidence for Economic Humanism in Management Science: Organizational Implications and Strategy. Archives italiennes de biologie, 154(1), 25-36.

Abstract
Family firms are a key component of the European economy, both for their high number and contributes to GDP and occupation. Although family businesses are not an Italian peculiarity, they strongly characterize the Italian economy. Research has not currently reached a unique paradigm on the relationships between the involvement of the family in the ownership and management of a firm. This line of research investigates the relationship between family firms and industrial districts, which is currently underdeveloped in the academic literature. The candidate will work both on developing the theoretical models and will analyse data using quantitative (econometrics and social network analysis) and qualitative methods (case studies).
Reference Faculty
Nicola Lattanzi, Marco Paggi e Alberto Bemporad, Armando Rungi
References

• Cucculelli, M., Storai, D. (2015). Family Firms and Industrial Districts: Evidence from the Italian Manufacturing Industry. Journal of Family Business Strategy, 6(4), p. 234-246
• Lattanzi, N. (2017). Le aziende familiari: Generazioni Società Mercato. G Giappichelli Editore.

Abstract
Fintech (Financial Technology) includes a wide set of technologies and innovations that are revolutionizing traditional financial services. Blockchain's uses have recently evolved into many applications, such as banking, financial markets, insurance and leasing contracts. Today, blockchain has potential for application in various business fields, including accounting and in certifying financial statements. This line of research will follow blockchain’s fundamental concepts, providing perspectives on its challenges and opportunities in business, management and accounting practices, also using complex systems for management science.
Reference Faculty
Nicola Lattanzi, Diego Garlaschelli
References

• De Bruijn, H., & Ten Heuvelhof, E. (2018). Management in networks. Routledge.
• Dai, J., & Vasarhelyi, M. A. (2017). Toward blockchain-based accounting and assurance. Journal of Information Systems, 31(3), 5-21.
• Fanning, K., & Centers, D. P. (2016). Blockchain and its coming impact on financial services. Journal of Corporate Accounting & Finance, 27(5), 53-57.