English

The course introduces the study of genetics and focuses on understanding phenotypic variation and the mechanisms underlying inheritance. The processes that participate in converting an individual’s genotype into the phenotypes displayed by that individual are of particular interest and importance. In this course students explore the fundamental properties of genes and the various approaches to genetic analysis, as it is performed in several different model organisms. When applicable, the course examines current examples of genetics issues that
arise in the literature or in the media and link these examples to topics covered in class.

Prerequisites: General Biology

The course explores corporate power and how it influences law-making and law- enforcement. Students study the key drivers of corporate crime/harm, along with their social impacts. Students also examine the basis of corporations’ structural and agency power, and corporate influence on the production and dissemination of science. The examination of the causes of corporate crime/harm will focus on organizational and structural factors, while investigation of the social impacts of corporate crime/harm focuses on the negative effects on human health, wellbeing, and the environment. Finally, students examine the social responses to corporate harm, including public shaming and corporate sentencing.

This course examines how to manage the complexities of contemporary organizational communication. It focuses primarily on internal organizational communication and examines communication processes at various levels: interpersonal (dyadic), group and organization.

In this course and through the DAMS Lab group (FG Big Data Engineering), students learn how to conduct research in areas of data engineering, data management, and machine learning systems. Students review scientific literature in these areas as well as how to design, implement, and evaluate prototypes. The lab group offers this project on large-scale data engineering. The course includes tasks in a wide range of components of data management and machine learning systems. Students will have the opportunity to make meaningful contributions to free open-source projects.

This survey course is an overview of American history from its origins to the present, with particular emphasis on the place of American history in modern history. This course helps students understand the outline and key concepts in the history and culture of the United States of America. 

The class offers an introduction into the modern approaches to causal identification in quantitative political science research. Traditional view implying that quantitative work can establish only correlations, and no causal links, has been challenged recently by new research designs allowing scholars to identify causal effects using quantitative data. This class reviews these methods (such as appropriate strategies of selecting control variables in regressions, matching, instrumental variables, experiments and regression discontinuity design), as well as discuss their application to the practical problems of political science research. It uses specific examples to train students' ability to develop effective research designs.

The course explores how drama, theatre, and performance reflect and effect social change. Students think about the relationship of the individual and the community in relation to wider social or institutional structures. The course brings together historical perspectives about drama, theatre, and performance and urgent issues in the present. Key skills students gain include working with theatre texts, historical understanding, and critical analysis about social and cultural change.

The ability of the natural environment to dispose the waste of anthropogenic activities (keeping unchanged degree of regeneration) is not infinite (downstream “scarcity”). This kind of “scarcity” justifies using the tools of economic theory to face environmental problems. In this regard, two circumstances should be adequately emphasized. Firstly, today the most important (at least perceived) environmental problem is climate change due to the anthropogenic emissions of carbon dioxide. Secondly, more than 80% of carbon emissions are due to energy production and consumption. As a consequence, fighting climate change requires to adequately study the interaction between energy and environmental issues with the aim to understand how to mitigate global warming through (but not only) the decarbonization of energy supply. The analysis of this interaction is the principal mission of this course. The course is divided into three parts: 1) The first part focuses on how economic theory can help policymakers to design environmental policies. The main environmental tools are described and analyzed (environmental taxation, emissions trading, standards, voluntary agreements, etc.); 2) the second part introduces the main energy markets (crude oil, natural gas, coal, nuclear power and renewable energy). Great attention is dedicated to the perspectives of deployment of Renewable Energy Sources (RES), their competitiveness and the possible supporting mechanisms (quantity, pricing and hybrid supporting mechanisms); 3) The third part focuses on the policies aimed to achieve the net zero emission target, especially at the EU level. 

The course gradually moves from the foundational principles of Behavioral Science, to the practical applications based on those principles through three interlinked blocks. Students are introduced to dual-processing models of human behavior to highlight the role of the environment in shaping decisions. They consider the Dual System approach, heuristics and biases, and the influence of time, risk, and social preferences. The course delves into the science of happiness by introducing the main accounts of subjective wellbeing, how it is conceptualized and measured and its implications for policy and other contexts. Finally, the course introduces the Mindspace framework by teaching how various techniques from Behavioral Economics (incentives, commitments, defaults), Social Psychology (ego, messenger and social norms), and Cognitive Psychology (priming, affect) can be used to shape behavior. 

This course emphasizes the pivotal role of data and innovation in transforming society, with a deep dive into the implications of data utilization and technological advances. It highlights the transformative power of AI in fostering innovation and societal change. Starting with an overview of innovation in service systems, with a particular focus on public services, the course offers a comprehensive curriculum that explores the foundational principles of AI technologies such as machine learning and natural language processing. Students will investigate how these innovations drive significant changes across various public sectors, including healthcare, urban development, education, arts and culture, and the operations of international organizations. Enhancing the learning experience, the course will feature guest speakers from diverse fields, providing firsthand insights into the challenges and successes of AI implementation. Additionally, case studies will be integrated throughout the course to offer real-world contexts for the concepts discussed, and on-site visits to organizations actively utilizing AI will give students practical exposure to the dynamics of AI in action. The course aims to equip students with the knowledge, analytical frameworks, and practical skills to: •Develop strategies that enhance service efficiency and value, particularly in the public context; •Create innovative solutions to societal issues using AI and Machine Learning.