Natalia Lamberova

Natalia Lamberova

Assistant Professor at the University of Texas at Dallas, PhD (Government), MSc (Statistics)

CURRENT TEACHING

University of Texas at Dallas, School of Economic, Political and Policy Sciences (Fall 2023)

  • Innovation Economy

“Innovation Economy” is a dynamic course designed to equip students with the essential skills and knowledge to understand and navigate the complex journey of transforming innovative ideas into market-ready products. The course will delve into the interplay between patents, technology development, and commercialization strategies. It is ideal for aspiring entrepreneurs, inventors, and those interested in innovation management.

Course Components:

  • Patent Fundamentals: Delve into the economic mechanisms behind patents and their implications for technological advancements.
  • The Patent-Market Interface: Understand the pathways through which patents can be translated into profitable ventures.
  • Hands-On Patent Search: Engage in practical exercises to perform patent searches, focusing on real-world inventions generated within UTD.
  • Market Analysis Techniques: Learn the methodologies and tools for assessing market demand, competition, and potential revenue streams for patented technologies.
  • Commercialization Strategies: Examine the strategies, challenges, and best practices in bringing patented technologies to market.
  • Policy and Strategy Analysis: Explore how innovation policies at national and corporate levels shape the patent landscape and commercialization efforts.

Students get free access to short courses on DataCamp to prepare themselves for the coding part of this course. Link to DataCamp for UTD students

University of Texas at Dallas, School of Economic, Political and Policy Sciences (Spring 2023)

  • Research Design II

Often the goal of social science research is to figure out the causal effect of one variable on another. This task is generally straightforward if one can perform an experiment, randomizing the “treatment” variable of interest. However, in social sciences such experimentation is often impossible - we cannot randomize treatments of interest, such as which countries get democracy, what policies are adopted by a state, or who is exposed to violence. This course covers both experimentation in social sciences, and clarification of the conditions under which estimates made using non-experimental data can be given a causal interpretation. We will cover a variety of designs and methods, including experiments, matching, regression, panel methods, difference-in-differences, synthetic control methods, instrumental variable estimation, regression discontinuity designs, and sensitivity analyses. The toolkit you build during this course will apply to any discipline in which investigators seek to make causal statements even if full randomization of the treatment is not possible.

Students get free access to short courses on DataCamp to prepare themselves for the coding part of this course. Link to DataCamp for UTD students

University of Texas at Dallas, School of Economic, Political and Policy Sciences (Fall 2022)

  • Topics in Science, Technology and Institutions: from Writing to Cryptocurrency

The aim of the course is to provide an introduction to the political economy of innovation that summarizes the state of the literature in this area, and highlights key open policy-relevant questions. Through the lens of different technologies - from writing to cryptocurrency - we will learn what drives the creation of new technology and how technology changes the world. We will start with exploring the links between innovation and economic growth.

We will learn from the economic history of innovation the key aspects of intellectual property rights protection and the trade-offs resulting from them. Based on our understanding of the incentives driving innovation, we will summarize theory and evidence on the efficiency of public policy interventions designed to address the key market failures in this space: taxes, public funding of research, intellectual property rights, competition policy. We will consider political incentives of investing in innovation, and show how political incentives drive the results of the innovation policies.

Students get free access to short courses on DataCamp to prepare themselves for the coding part of this course. Link to DataCamp for UTD students

TEACHING EXPERIENCE

Georgia Institute of Technology, Sam Nunn School of International Affairs

  • Economics of International Security (Fall 2020, Teaching award)

  • Political Economy of Post-Communism (Spring 2021, Fall 2021, Teaching Award)

University of California, Los Angeles, Anderson Business School

  • Technology Analytics, with Prof. Keith Chen (2019)

University of California, Los Angeles, Department of Political Science

University of Mayland, College Park, Department of Economics

Kazan Natinal Research University, Kazan, Department of Economics

  • Economics of Innovation, Lecturer (2012)

  • Economics of Intellectual Property Rights, Lecturer (2012)