Research topics

Abstract
Much of what we observe in the adult brain reflects how neural circuitries have been sculpted by experience along the life cycle. A powerful way to investigate the impact of experience on the functional and structural organization of the brain is provided by sensory deprivation models. By perturbing the availability of a sensory input, as for instance adopting (1) permanent sensory deprivation (e.g. deafness and blindness), (2) sensory re-afferentation (e.g. individuals whom recover vision or audition after a period of deprivation) or (3) short-lasting deprivation in the adult, as models of investigation, we can understand the degree of plasticity of sensory systems and of their interactions. Developmental and multisensory perspectives are adopted.
Related topics:

• Degree of plasticity and functional recovery in case of temporary sensory deprivation and restoration
• Impact of visual deprivation on auditory development
• Impact of auditory deprivation on visual development     
• Neural entrainment to sensory signals in Cochlear implanted individuals (PRIN 2017 Bottari)
• Functional organization underpinning cross-modal responses in typical development

Methods of investigation include: EEG, Computational Neuroscience, fMRI and psychophysics
Keywords 
Experience dependence, sensory deprivation and restoration, neural plasticity, supramodality
References

• Bottari D. Kekunnaya R. Hense M. Troje N. Sourav S. Röder B. Motion processing after sight restoration: No competition between visual recovery and auditory compensation. NeuroImage 167, 284-296, 2018
• Bottari, D., Heimler, B., Caclin, A., Dalmolin, A., Giard, M. H., & Pavani, F. (2014). Visual change detection recruits auditory cortices in early deafness. Neuroimage, 94, 172-184.
• Berto, M., Ricciardi, E., Pietrini, P., & Bottari, D. (2019). Experience dependent plasticity of auditory statistics: a computational approach (https://iris.imtlucca.it/handle/20.500.11771/13497?mode=full.47)
• Sourav, S., Kekunnaya, R., Shareef, I., Banerjee, S., Bottari, D., & Röder, B. (2019). A protracted sensitive period regulates the development of cross-modal sound–shape associations in humans. Psychological science, 30(10), 1473-1482.

Reference persons
Emiliano Ricciardi, Davide Bottari and Pietro Pietrini - MoMiLab
Other involved research units
NA

Abstract
‘Neuroeconomics’ and ‘neuromarketing’ are emerging interdisciplinary fields promoting a dialogue between neuroscience, psychology, behavioral science, economics and marketing.
A new laboratory called Innovation Center Lab-Neuroscience has been created to foster this interdisciplinary research fields within a multidisciplinary team to:

• apply behavioral science and design thinking to optimize consumer behavior, businesses, and policy
• investigate behavioral and neural correlates of consumers’ response to marketing stimuli
• design solutions that make products and services more responsive to human behavior and drive behavior change

The research projects will engage the use of brain imaging methodologies, biometrics and other technologies in assessing how specific samples (e.g., potential consumers, branch managers, trafers, etc.) respond when presented with specific products and/or related stimuli. In particular, the comprehension of how information on a specific product/item is conveyed through different sensory modalities and media channels and influence decision-making processes represent a current challenge for neuroscientific research in the field of marketing. Original approaches in neuroimaging studies applied to social sciences, behavior, decision-making processes should, not simply, characterize the individual (social and personological) profile, behavioral and brain functional processes, but mainly look for potential predictive biomarkers of individual choices or social outcome and contribute to the design of behavioral-change interventions. 
Methods of investigation include: behavioral and psychophysical, brain imaging (e.g., fMRI and EEG)
Keywords
neuroeconomics, decision making, neuromarketing, social sciences
References

• Genevsky A, Yoon C, Knutson B.When Brain Beats Behavior: Neuroforecasting Crowdfunding Outcomes. J Neurosci. 2017 Sep 6;37(36):8625-8634
• Casarotto S, Ricciardi E, Romani S, Dalli D, Pietrini P. Covert brand recognition engages emotion-specific brain networks. Arch Ital Biol. 2012 Dec;150(4):259-73
• Pietrini P. Toward a biochemistry of mind? Am J Psychiatry. 2003 Nov;160(11):1907-8

Reference persons
Emiliano Ricciardi and Pietro Pietrini, MoMiLab
Other involved research units
Ennio Bilancini and Nicola Lattanzi, AXES

Abstract
Social cognition represents the cornerstone of successful human interactions. Any social contact requires the interaction of several abilities: to observe other people’s behavior, to predict their reaction and to respond adequately. Altogether, these processes give rise to the complexity of the social world. In our everyday life, it is fundamental to recognize someone as a different person from ourselves, understand their feelings, emotions, beliefs and desires, infer the reasons behind their behavior and give a socially appropriate response to it. Therefore, we can easily imagine the consequences of not being able to understand when someone is angry, or not feeling anything when others are suffering. The total lack or severe impairment of social cognition abilities characterize several disorders, ranging from psychiatric (e.g., psychopathy) to neurodegenerative conditions (e.g., behavioral variant of frontotemporal dementia). The brain and psychological mechanisms at the basis of normal and pathological social cognition processes are still unclear and, among several others, different topics are relevant in the social and affective neuroscience field:
The investigation of how emotions are processed and represented in the brain
How the brain is able to analyze complex social scenarios and decipher social relations from minimal visual information. 
Brain characteristics of psychopath subjects and their association with empathic processing
Methods of investigation include
structural and functional MRI, behavioral measurement and questionnaires, recording of autonomic activity (e.g., skin conductance, respiration and heart rate)
Keywords
social cognition, emotion, mentalizing, perspective taking, empathy
References

• Lettieri, G., Handjaras, G., Ricciardi, E., Leo, A., Papale, P., Betta, M., ... & Cecchetti, L. (2018). Emotionotopy: Gradients encode emotion dimensions in right temporo-parietal territories. bioRxiv, 463166.
• Pietrini P. Toward a biochemistry of mind? Am J Psychiatry. 2003 Nov;160(11):1907-8
• Gobbini MI, Gentili C, Ricciardi E, Bellucci C, Salvini P, Laschi C, Guazzelli M, Pietrini P.Distinct neural systems involved in agency and animacy detection. J Cogn Neurosci. 2011 Aug;23(8):1911-20

Reference persons
Luca Cecchetti and Pietro Pietrini, MoMiLab
Other involved research units
NA

Abstract
The study of the sensory-deprived brain provides a unique tool to understand to what extent a specific sensory modality is truly a mandatory prerequisite for the brain morphological and functional architecture to develop and function. The demonstration that congenitally blind individuals during non-visual object recognition show topographically-organized category-related patterns of neural response in the ventral “visual” pathway indicated that visual experience is not necessary for the brain to develop a certain functional organization. On the same line, it was also shown that the brain appears to be able to process specific types of information independently from the modality that carries the input (e.g. Pietrini et al., 2004). Research from multiple labs has then confirmed an overall preservation of the large-scale functional and structural organization of congenitally blind individual brains across several domains. This research field has always been of major interest at the MoMiLab and thus, multiple topics have been and are currently explored, such as:

• Conceptual representation and its sensory (in)dependence (Handjaras et al., 2016)
• Supramodal brain organization in sensory-deprived individuals
• Structural changes in the sensory-deprived brain (Cecchetti et al., 2016)
• Emotion processing and social interactions in blind individuals

Methods of investigation include: fMRI, EEG and psychophysics
Keywords
Supramodality, experience dependence, sensory deprivation
References

• Ricciardi, E., Bottari, D., Ptito, M., Röder, B., & Pietrini, P. (2019). The sensory-deprived brain as a unique tool to understand brain development and function. Neuroscience & Biobehavioral Reviews Volume 108, January 2020, Pages 78-82
• Ricciardi E. Pietrini P.  New light from the dark: what blindness can teach us about brain function. Current Opinion in Neurology, 24 (4), 357–363, 2011
• Handjaras G. Ricciardi E. Leo A. Lenci A. Cecchetti L. Cosottini M. Marotta G. Pietrini P. How concepts are encoded in the human brain: A modality independent, category-based cortical organization of semantic knowledge. Neuroimage, 135, 232-242, 2016
• Cecchetti, L., Ricciardi, E., Handjaras, G., Kupers, R., Ptito, M., & Pietrini, P. (2016). Congenital blindness affects diencephalic but not mesencephalic structures in the human brain. Brain Structure and Function, 221(3), 1465-1480.

Reference persons
Emiliano Ricciardi, Luca Cecchetti, Davide Bottari and Pietro Pietrini - MoMiLab
Other involved research units
NA

Abstract
Traditionally, sleep and wakefulness have been considered as two global, mutually exclusive states. However, this view has been challenged by the discovery that sleep and wakefulness are locally regulated and that islands of these two states may often coexist in the same individual. Importantly, the local regulation of sleep seems to be key for many of the known functions of this physiological state, including the maintenance of brain functional efficiency, the consolidation or stabilization of new memories and the modulation of mood and emotional reactivity. Local changes in brain activity during sleep may also explain the emergence of particular conscious experiences in the form of dreams, and may modulate the level of sensory disconnection that is essential for a restorative sleep. On the other hand, during wakefulness, the reiterated activation of specific brain areas seems to determine a state of functional fatigue, characterized by the appearance of local, sleep-like episodes. These events may have important consequences for behavior and cognition and may contribute to explain the known effects of sleep deprivation. Given these premises, alterations in the local regulation of sleep and wakefulness may represent the pathophysiological base for symptoms observed in many sleep disorders, but also in some psychiatric or neurological disorders.
Topics of interest related to this research field include:

• mechanisms and functions of local sleep regulation in humans;
• the role of sleep and dreams in experience-dependent brain plasticity (memory/learning) and in emotional regulation;
• local sleep during wakefulness and its implications for behavior and cognition;
• the effects of sleep deprivation/restriction on brain structure and function;
• alterations of local sleep regulation in pathological conditions.

Methods of investigation include
psychometric questionnaires, behavioral testing, recording of autonomic activity, high-density EEG recordings, functional/structural MRI. Additional research opportunities (e.g., for the study of patients with neurological, psychiatric or sleep disorders) and methodologies (e.g., combined fMRI-EEG, intracranial EEG recordings) may become available through established national and international collaborations.
Keywords
sleep, consciousness, dreams, learning, memory, emotion.
References

• Avvenuti G, Handjaras G, Betta M, Cataldi J, Imperatori LS, Lattanzi S, Riedner BA, Pietrini P, Ricciardi R, Tononi G, Siclari F, Polonara G, Fabri M, Silvestrini M, Bellesi M, Bernardi G. Integrity of corpus callosum is essential for the cross-hemispheric propagation of sleep slow waves: a high-density EEG study in split-brain patients. bioRxiv 2019, 756676.
• Siclari F, Baird B, Perogamvros L, Bernardi G, LaRocque JJ, Riedner B, Boly M, Postle BR, Tononi G. The neural correlates of dreaming. Nat Neurosci 2017;20: 872–878.
• Bernardi G, Siclari F, Yu X, Zennig C, Bellesi M, Ricciardi E, Cirelli C, Ghilardi MF, Pietrini P, Tononi G. Neural and behavioral correlates of extended training during sleep deprivation in humans: evidence for local, task-specific effects. J Neurosci 2015;35: 4487–4500.

Reference persons
Giulio Bernardi, Emiliano Ricciardi and Pietro Pietrini - MoMiLab
Other involved research units
NA

Abstract
 A key function in our brain is the coding of information related to movements. Many questions regarding how motor schemes and actions are represented in the brain, how we process sensory information relevant for planning movements or to understand others’ actions, how we do represent our and others’ space for action are still open and debated. In addition, the importance of exploiting our knowledge about the motor system and the action representation system to develop more efficient strategies for rehabilitation and restoration of movement in coordination developmental disorders or in patients who suffered brain damage is consistently growing. The study of this topic can be pursued through different methods, ranging from behavioral and psychophysiological studies to the usage of functional imaging to investigate the brain correlates of motor planning or perception, action observation or processing. In the last years, the MoMiLab has conducted much work on this field, leveraging also on collaborations with researchers even from the bioengineering and bionics field, for the investigation of multiple topics related to motor control, such as:

• Models for coding hand and upper limb movements. In the last years, the MoMiLab has conducted studies to demonstrate that the human motor system encodes low-dimensional models based on synergies. These models are particularly important for the design of prostheses, and the study of their direct coding in the brain is important for both research and application purposes.
• Representation of different classes of movements in the human action observation systems
• Representation of space for action, effectors, affordances, objects for motor planning and its interaction with sensory modalities

Methods of investigation include
fMRI, EEG, EMG, optical joint tracking and psychophysics
Keywords
motor representation, action observation system, synergies, object processing
References

• Leo, A., Handjaras, G., Bianchi, M., Marino, H., Gabiccini, M., Guidi, A., Scilingo, E.P., Pietrini, P., Bicchi, A., Santello, M., & Ricciardi, E. (2016). A synergy-based hand control is encoded in human motor cortical areas. Elife, 5, e13420Siclari F, Baird B, Perogamvros L, Bernardi G, LaRocque JJ, Riedner B, Boly M, Postle BR, Tononi G. The neural correlates of dreaming. Nat Neurosci 2017;20: 872–878.
• Handjaras, G., Bernardi, G., Benuzzi, F., Nichelli, P. F., Pietrini, P., & Ricciardi, E. (2015). A topographical organization for action representation in the human brain. Human brain mapping, 36(10), 3832-3844
• Ricciardi E, Menicagli D, Leo A, Costantini M, Pietrini P, Sinigaglia C. Peripersonal space representation develops independently from visual experience. Scientific Reports, 7(1):17673, 2017 doi: 10.1038/s41598-017-17896-9

Reference persons
Emiliano Ricciardi and Giacomo Handjaras - MoMiLab
Other involved research units
NA

Abstract
Recent research in cognitive and behavioral sciences is increasingly illuminating the basic mechanisms of human reasoning and cognition, as well as their limitations and systematic deviations from normative theories of rational inference and decision-making. This research line puts together theoretical and formal models with empirical approaches to the study of human reasoning and cognition. The aim is to better understand, and possibly improve, how people reason and make choices in different contexts, both in ordinary life and in science. Topics of interest include:

• Normative and descriptive models of reasoning, rational inference and decision-making: heuristics and biases, ecological rationality, nudge theory; expert judgment and reasoning (e.g., clinical reasoning, legal reasoning, etc.); reasoning under uncertainty and fallacies; reasoning in moral and social dilemmas.
• Neural correlates of reasoning, choice and strategic behavior: plasticity of strategic sophistication in interactive decision making; neural correlates of sophisticated and unsophisticated decision-making; relationships between intelligence and the ways social and individual information is utilized to make decisions; choice in two and multi-persons games.
• Methodological aspects of social, cognitive and behavioral sciences: models of inferences and fallacies in scientific reasoning; confirmation theory and Bayesian reasoning; game-theoretical foundations of economics and social sciences.

Methods of investigation include
Eye-tracking; fMRI and Mouse-tracking.
Keywords
rationality, decision-making, game theory, logic, critical thinking, scientific method
References 

• ​Festa, Roberto, and Gustavo Cevolani. 2017. “Unfolding the Grammar of Bayesian Confirmation: Likelihood and Antilikelihood Principles.” Philosophy of Science 84 (1): 56–81. https://doi.org/10.1086/688935
• Polonio, L., Di Guida, S., & Coricelli, G. (2015). Strategic sophistication and attention in games: an eye-tracking study. Games and Economic Behavior, 94, 80-96. https://doi.org/10.1016/j.geb.2015.09.003
• Zonca, J., Coricelli, G., & Polonio, L. (2020). Gaze data reveal individual differences in relational representation processes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(2), 257–279. https://doi.org/10.1037/xlm0000723

Reference persons
​Gustavo Cevolani, Luca Polonio, Emiliano Ricciardi, Pietro Pietrini ‒ MoMiLab
Other involved research units
AXES (Ennio Bilancini)

• 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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• 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.
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• 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.
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• 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.
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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
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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.