Global Arc

Attitudes matter. Throughout the history of the world, people have taken extraordinary steps to support a set of attitudes and beliefs that helped to bring about a better world. Mahatma Gandhi, Nelson Mandela and Martin Luther King led societies to new views of human dignity by their written words and their behaviors. Every day, people advocate for their ideals. They persuade and organize in the service of bringing about a world that is closer to the paragon in which they believe. One three-hour seminar.

This course will explore a wide range of mind-machine interactions. Are search engines changing the structure of human memory? Is your laptop or smartphone part of your mind? Are human brains flexible enough to update motor and sensory systems, expanding the self to include artificial limbs, exoskeletons, remote-control devices, night vision, wearable computing, etc.? How do experiences in virtual reality impact psychology? As technology advances we are all becoming cyborgs. Now is an exciting time to study the interactive interface of technology and mind. One three-hour seminar.

In this course, we will examine how the nervous system controls movements, how the brain handles enormous computational complexities of movement, how motor skills are learned and consolidated, and how the motor system influences cognition.

This course will explore how people believe and how what they believe impacts their behavior. How do people change their beliefs? What factors facilitate the endorsement of conspiracy theories? How do people influence each other's beliefs during communication? How do beliefs propagate through social networks? This course will also explore a multidisciplinary framework to understand the endorsement and propagation of true and false beliefs through social networks. One three-hour seminar.

This course will provide an overview of several theoretical orientations to psychotherapy and critically evaluate how the effects of therapies are measured and studied. Application of research findings to clinical practice will be examined closely, including issues related to psychotherapy integration and the treatment of diverse populations in various settings. The course will also include reviews of the current state of psychotherapy research for a number of psychological disorders and consider current controversies in the area of treatment outcome research. One three-hour seminar.

This seminar explores parallels between human cognition and ideas in probability and statistics, with an emphasis on statistical machine learning. Minds and machines face similar computational problems, meaning that we can develop new hypotheses about human cognition by seeing how those problems are solved in computer science and statistics and find new challenges for AI and machine learning by studying human cognition.

An overview of research topics and methods in quantitative biology through reading and discussion of primary literature. Students read two papers weekly, each showcasing how modern experimental and analytical techniques are applied to address basic questions in biology with a strong focus on big data. Students examine the achievements and impact of each study, present context and background, dissect experimental and analytical approaches, and highlight remaining challenges. Topics range from gene regulation and organellar dynamics to virology and cancer genomics. Prereqs: MOL 214 or equivalent or permission of the instructors.

Advances in molecular biology and computation have propelled the study of genomics forward, including how genes are organized and how their regulation manifests complex phenotypes. A hallmark of genomics is the production and analysis of large data sets. This course will pair an overview of genomics with practical instruction in the analytical techniques required to use it in research and medicine. We will start with a primer on genetics and an introduction to programming using Python. The goal of this course is to provide a foundation for understanding the data heavy experiments that are increasingly common in biomedical research.

This course establishes a foundation in statistical modeling of genomic data. There is an emphasis on applications in population genetics, gene expression, and human genomics. There is also an emphasis on careful consideration and development of statistical models. Statistical topics may include probabilistic and theoretical models, likelihood based inference, Bayesian inference, principal components analysis, multiple hypothesis testing, and causality. The statistical programming language R is utilized to explore methods and analyze data.

This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple testing correction, performance evaluation), and machine learning methods which have been applied to biological problems (e.g., classification techniques, hidden Markov models, clustering).