# Fall 2012 – Spring 2013

## Spring 2013

### Biology

#### Statistical Phylogenetics

**Jonathan Bollback**

How can we address questions about the evolutionary history of life? This problem – often referred to as the phylogeny problem – has generated interest since Darwin's publication of On the Origin of Species. Phylogenies, genealogies, and networks are utilized in nearly all fields of biology. They are used for a variety of purposes other than understanding the evolutionary relationships of life. This course provides an advanced introduction to statistical approaches in phylogenetics. Topics covered include alignments, trees, probability distributions, models, hypothesis testing, the comparative method, and other aspects of statistical phylogenetics.

Course type | Breadth |

ECTS credits | 3 |

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### Biology

#### Molecular Cell Biology II

**Daria Siekhaus, Mark Bollenbach**

Students will learn about eukaryotic cell biology through lectures and dissection of the primary literature. Topics included will be the structures of cells and cellular specialization, the cytoskeleton, vesicular trafficking, cell signaling, cell migration, and biological pattern formation with a special emphasis on how these cellular capacities are integrated to permit developmental processes. Quantitative and interdisciplinary perspectives on the topics will be highlighted. Students will further develop their independent thinking, knowledge of the techniques utilized in these areas, and ability to critically assess the literature.

Course type | Breadth |

ECTS credits | 3 |

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### Biology

#### Quantitative Biology

**Carl-Philipp Heisenberg, Harald Janovjak**

This course will introduce students of all disciplines to the methodological principles and recent key discoveries in the emerging field of Quantitative Biology. The methodological principles section will cover the technological developments (e.g. advanced fluorescence microscopy, scanning probe microscopy, molecular modeling and computer simulations, and modern genetic techniques), while recent key discoveries will highlight the trajectory in which the field is developing.

Course type | Breadth |

ECTS credits | 3 |

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### Computer Science

#### Introduction to Game Theory and Distributed Algorithms

**Krishnendu Chatterjee**

Game theoretic methods provide the mathematical background for formal analysis of several important problems in computer science, such as formally proving correctness of dynamic systems, automatically designing a correct system from specifications, auction theory, controller synthesis, etc. Game theoretic methods are central in formal verification of open reactive systems (systems that interact with an environment), embedded systems, and many other applications in formal verification. In this course we will study the basic game models in formal verification with all the commonly used specifications in verification, such as safety, reachability, liveness and fairness specifications. Fault-tolerant distributed algorithms are at the heart of any distributed system for critical applications and implement low-level services like clock synchronization, group membership and consensus. Suitable algorithms must work as specified in the presence of the inherent uncertainty in network- or shared-memory-coupled distributed systems, which is caused by varying/unknown communication delays, computing speeds and, in particular, subsystem failures. Due to combinatorial explosion, it is typically impossible to verify the correct operation of such algorithms by means of model checking (or exhaustive testing). Correctness proofs and performance analysis based on formal-mathematical modeling are a feasible alternative here.

Course type | General |

ECTS credits | 6 |

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### Computer Science

#### Algorithms II

**Krzysztof Pietrzak, Vladimir Kolmogorov**

This course is a continuation of Algorithms I. The Algorithms I course is not a prerequisite for this course, but some background in basic concepts in algorithm design is recommended. Topics will include basics of linear programming and more advanced algorithms such as a maximum flow algorithm. We will also discuss the concept of NP-completeness, and see how to deal with "hard" algorithmic problems for which no efficient algorithms are known to exist. This includes approximation algorithms, parameterized complexity and exponential time algorithms.

Course type | Breadth |

ECTS credits | 3 |

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### Computer Science

#### Computational Complexity

**Krishnendu Chatterjee, Krzysztof Pietrzak**

Complexity theory is a field on the border of mathematics and computer science with a remarkable list of celebrated achievements as well as vibrant present research activity. Complexity theory is the basic foundation of computer science, and is concerned with classifying computational problems according to the resources (ie. computation time, space) that are needed to solve them. This course is aimed at exposing students to the basic models of computation and the basic results and notion of complexity theory. The topics to be covered in the course will include: Automata theory and regular languages Turing machine model of computation Diagonalization and undecidability Non-determinism in computation and the class of NP problems Reductions and completeness

Course type | Breadth |

ECTS credits | 3 |

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### Computer Science

#### Cryptography

**Krzysztof Pietrzak**

This course offers an introduction to the theory and practice of modern cryptography, the science of computer security. We will cover basic topics such as encryption (secret-key and public-key), message authentication, digital signatures, identification, cryptographic hashing and zero-knowledge proofs.

Course type | General |

ECTS credits | 3 |

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### Computer Science

#### Machine Learning

**Christoph Lampert**

This course provides an introduction to modern statistical machine learning, in particular probabilistic models and kernel methods. Emphasis lies on supervised learning, in particular classification. The course will be held as a 1-week block course, consisting of classroom lectures in the morning and exercise sessions in the afternoon. Additionally, daily exercise sheets will be given out to be solved by the participants in small groups. Syllabus: - Introduction to Machine Learning - Decision Theory, Generative Probabilistic Models - Discriminative Probabilistic Models (generative/discriminative) - Kernel Methods, Model Selection, Statistical Learning Theory

Course type | General |

ECTS credits | 3 |

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### Mathematics

#### Computational Geometry and Topology

**Herbert Edelsbrunner**

Geometry and Topology are old fields in mathematics which have been turned into computational disciplines relatively recently. In this course, we teach a small but important subset of topics in the field of Computational Geometry and Topology that are connected to applications in science and engineering more than others: I. Tessellations II. Complexes III. Homology IV. Persistence

Course type | Breadth |

ECTS credits | 3 |

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### Mathematics

#### Statistics

**Caroline Uhler**

This course provides an overview of methods and techniques from applied statistics with the aim to be able to identify and apply appropriate statistical methods to solve real data problems using R. Topics include exploratory data analysis (graphical data summaries, detection of outliers, principal component analysis), hypothesis testing (p-value, different test statistics), classification (discriminant analysis, logistic regression) and model formulation, fitting, and validation (linear regression). In order to take this course, students should have a basic knowledge of linear algebra (matrix multiplication, rank, orthogonality, determinant, eigenvalue, positive semidefiniteness, singular value decomposition) as well as a basic knowledge of probability theory (random variable, mean, covariance, histogram, pdf, cdf, basic discrete and continuous distributions such as Uniform, Bernoulli, Binomial, Gaussian). A brief refresher course will be offered in advance of the course (date TBA on the course website).

Course type | Breadth |

ECTS credits | 3 |

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### Mathematics

#### Random Matrices

**László Erdös**

Random matrices were first introduced in statistics in the 1920's, but they were made famous by Eugene Wigner's revolutionary vision. He predicted that spectral lines of heavy nuclei can be modelled by the eigenvalues of random symmetric matrices with independent entries (Wigner matrices). In particular, he conjectured that the statistics of energy gaps is given by a universal distribution that is independent of the detailed physical parameters. While the proof of this conjecture for realistic physical models is still beyond reach, it has recently been shown that the gap statistics of Wigner matrices is independent of the distribution of the matrix elements. Students will be introduced to the fascinating world of random matrices and presented with some of the basic tools for their mathematical analysis in this course. No physics background is necessary, but some basic probability theory is useful.

Course type | Breadth |

ECTS credits | 3 |

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### Mathematics

#### Differential Equations

**Christopher Wojtan**

This course will focus on building an intuitive, high-level understanding of calculus, differential equations, and dynamical systems, with applications to biology, neuroscience, and physics. We begin with a review of basic tools for differentiation and integration, and then we introduce essential vector calculus concepts of the gradient, divergence, and Laplacian. Next, we draw attention to the special topics of critical points and Taylor series. Afterward, we spend a few lectures introducing the concepts of differential equations, with an emphasis on understanding their general behavior instead of drilling specific methods for solving them. In the last leg of this course, we introduce strategies for solving differential equations in a computer.

Course type | Breadth |

ECTS credits | 3 |

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### Neuroscience

#### Introduction to Neuroscience II

**Peter Jonas**

In Introduction to Neuroscience II, molecular and cellular neuroscience is covered. We will address properties of ion channels, neurons and other excitable cells, subcellular processes (presynaptic terminals and dendrites), synaptic signaling in different microcircuits (hippocampus, cerebellum, basal ganglia, olfactory bulb), and synaptic plasticity.

Course type | Breadth |

ECTS credits | 3 |

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### Other

#### Basic Microscopy

**Ekaterina Papusheva**

This course will give an introduction to contemporary microscopy techniques, with a focus on the methods that are available at IST Austria. Lectures will cover microscopy-related principles of optic, principles of fluorescence, anatomy of the microscope, methods of optical sectioning, image acquisition methods, and the basics of data analysis. Hands-on sessions will introduce students to Widefield, TIRF, Confocal, Multiphoton and Spinning disc microscopy. Two hands-on sessions will be dedicated to image analysis methods. While lectures are open to everyone, hands-on sessions will be limited to the registered participants. A maximum of 4 students per group can register for the hands-on sessions, and 4-5 groups will be organized. Both beginners and experienced users are welcome to participate.

Course type | General |

ECTS credits | 2 |

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### Other

#### Scientific Presentation and Conduct

**Sylvia Cremer, Thomas Henzinger**

This course introduces students to the basic concepts that apply to good scientific practice and a successful scientific career. Required for 1st year IST Austria PhD students.

Course type | Required |

ECTS credits | 2 |

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## Fall 2012/2013

### Biology

#### Molecular Cell Biology I

**Michael Sixt**

Molecular Cell Biology I is an introductory course on all molecular concepts from biology. We will discuss the structure and function of the cell's building blocks, proteins, lipids and nucleic acids, from a chemical and physical point of view. This course introduces basic knowledge for MCB II and Introduction to Neuroscience I and II.

Course type | Breadth |

ECTS credits | 3 |

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### Biology

#### Microbial Genetics

**Calin Guet, Jonathan Bollback**

Microbial species represent more than 90% of the species diversity, have been estimated to represent nearly half of all the biomass on the planet, are the causal agents of the majority of human diseases, and have played a pivotal role in our understanding of genetics, historically and even today. This advanced course focuses on all aspects of microbial genetics. Some key topics covered are: 1. Structure of the cell, 2. Membrane transport, 3. Biosynthesis, 4. Growth and replication, 5. Genetic architecture, gene expression and regulation, 6. Plasmids, mobile elements, and phages, 7. Evolution and ecology, and 8. Genetic tools.

Course type | Breadth |

ECTS credits | 6 |

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### Biology

#### Principles of Evolution

**Jonathan Bollback, Nick Barton, Sylvia Cremer**

This course will give a broad introduction to evolutionary biology. We will discuss six topics, showing the variety of methods that can be applied: History of life - Adaptation - Deleterious mutation - Social evolution - Consequences of sexual reproduction - Disease and evolution

Course type | Breadth |

ECTS credits | 3 |

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### Computer Science

#### Formal Methods

**Krishnendu Chatterjee, Thomas Henzinger**

We present formal modeling languages and analysis tools for discrete-event dynamical systems, with special emphasis on applications from computer science and biology. The languages we discuss are based on mathematical logic, rewrite rules, automata, circuits, and programming constructs. The analysis methods include model checking, theorem proving, and abstract interpretation. We give brief introductions to advanced models incorporating time, probabilities, game-theoretic aspects, and continuous behavior.

Course type | Breadth |

ECTS credits | 3 |

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### Computer Science

#### Algorithms I

**Krzysztof Pietrzak, Vladimir Kolmogorov**

This course covers techniques for the design and analysis of algorithms, with a special emphasis on methods useful in bioinformatics. The topics covered include: basic data structures; algorithms on graphs, trees and strings; techniques like dynamic programming, divide-and-conquer and amortized analysis. This course is a prerequisite for the course Computational Complexity.

Course type | Breadth |

ECTS credits | 3 |

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### Mathematics

#### Discrete Mathematics & Probability

**Herbert Edelsbrunner, Nick Barton**

Discrete mathematics and probability provide concepts that are fundamental to computer science, biology, and other disciplines. This course emphasizes the connection to computer science and biology through the selection and motivation of topics: I. Fundamentals: counting, logic. II. Discrete Probability: in-exclusion, random variables. III. Processes: graphs, matrices, Markov, branching. IV. Continuous Probability: distributions, central limit, diffusion.

Course type | Breadth |

ECTS credits | 3 |

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### Mathematics

#### Linear Algebra

**Christoph Lampert**

Linear Algebra (from a Data Analysis point of view) introduces the most important concepts of Linear Algebra: vectors, matrices, linear functions, eigenvalues/-vectors, and how to solve linear equations. For this, it takes a route motivated by the problems that occur in scientific data analysis, such as linear regression and dimensionality reduction. Linear Algebra is a prerequisite for the follow-up courses "Differential equations" and "Geometry and Topology".

Course type | Breadth |

ECTS credits | 3 |

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### Neuroscience

#### Introduction to Neuroscience I

**Gasper Tkacik, Jozsef Csicsvari, Simon Hippenmeyer**

Neuroscience I is a basic introduction to neuroscience. It involves a basic introduction to Neuroanatomy and Neurophysiology, information coding in neuronal circuits, analysis of complex neuronal activity patterns, experimental techniques to analyze brain function (e.g. Ca2+ imaging, voltage imaging, superresolution techniques, and FRET), and computational techniques to model signaling in the brain (modeling action potential patterns, receptor kinetics, and dendritic integration).

Course type | Breadth |

ECTS credits | 3 |

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### Other

#### Introduction to Research at IST Austria

** **

This course gives an introduction to the research of the IST faculty and is required of all new PhD students. The intent is to foster the interdisciplinary spirit at IST Austria, provide students with a scientific overview to aid them in choosing labs for rotations, and to help students in choosing a doctoral advisor. The course has two components: 1) Attending the faculty symposium; and 2) Attending at least five of the IST Colloquium talks. Of the five Colloquium talks students are required to write three summary essays (1000 words) and two critical scientific reviews of one paper cited in the talks. More details can be found on the course website.

Course type | Required |

ECTS credits | 1 |

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### Other

#### Introduction to Programming with Python

**Jonathan Bollback**

The complexity and volume of data being collected in science today requires researchers to have, at minimum, a solid understanding of how to write a computer program. Often you may want to perform what are seemingly simple tasks, such as parse or extract information from extremely large data files, simulate complex biological processes, perform statistical analyses on complex data, and many other common non-trivial unique tasks. Often, there are off the shelf programs to perform these tasks however more often this is not the case. The purpose of this course, Introduction to Python, is to teach you how to program in Python, a very useful programming language, and to introduce you to universal programming concepts that are not specific to a single language but shared by all computer languages. This is not an exhaustive programming, or Python, course but will set you on the path to becoming a self-reliant programmer. The course is divided into roughly two parts. The first consists of basic programming concepts (e.g., variable types, flow control, logical operators, objects, etc.) and the syntax, use, and implementation of programming tasks in Python. The second half of the course focuses on applications in biology (e.g., genomics, interacting with the R statistical package, simulating evolution, etc.) and neuroscience (e.g., analysis of signals, neural models, simulating cells and networks, etc.). The course is fairly intense course, consisting of six classes (two hours of lecture followed by one hour of practical programming exercises), concentrated into two weeks of teaching.

Course type | General |

ECTS credits | 2 |

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