Joshua Payne: Catalogue data in Spring Semester 2023 |
| Name | Dr. Joshua Payne |
| Department | Environmental Systems Science |
| Relationship | Assistant Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 636-0704-00L | Computational Biology and Bioinformatics Seminar  The seminar is addressed primarily at students enrolled in the MSc CBB programme. Students of other ETH study programmes interested in this course need to ask the lecturer for permission to enrol in the course. The Seminar will be offered in autumn semester in Basel (involving professors and lecturers from the University of Basel) and in spring semester in Zurich (involving professors and lecturers from the University of Zurich). Professors and lecturers from ETH Zurich are involved in both semesters. | 2 credits | 2S | J. Stelling, D. Iber, M. H. Khammash, J. Payne | |
| Abstract | Computational Biology und Bioinformatik analysieren lebende Systeme mit Methoden der Informatik. Das Seminar kombiniert Präsentationen von Studierenden und Forschenden, um das sich schnell entwickelnde Gebiet aus der Informatikperspektive zu skizzieren. Themenbereiche sind Sequenzanalyse, Proteomics, Optimierung und Bio-inspired computing, Systemmodellierung, -simulation und -analyse. | ||||
| Learning objective | Studying and presenting fundamental papers of Computational Biology and Bioinformatics. Learning how to make a scientific presentation and how classical methods are used or further developed in current research. | ||||
| Content | Computational biology and bioinformatics aim at advancing the understanding of living systems through computation. The complexity of these systems, however, provides challenges for software and algorithms, and often requires entirely novel approaches in computer science. The aim of the seminar is to give an overview of this rapidly developing field from a computer science perspective. In particular, it will focus on the areas of (i) DNA sequence analysis, sequence comparison and reconstruction of phylogenetic trees, (ii) protein identification from experimental data, (iii) optimization and bio-inspired computing, and (iv) systems analysis of complex biological networks. The seminar combines the discussion of selected research papers with a major impact in their domain by the students with the presentation of current active research projects / open challenges in computational biology and bioinformatics by the lecturers. Each week, the seminar will focus on a different topic related to ongoing research projects at ETHZ, thus giving the students the opportunity of obtaining knowledge about the basic research approaches and problems as well as of gaining insight into (and getting excited about) the latest developments in the field. | ||||
| Literature | Original papers to be presented by the students will be provided in the first week of the seminar. | ||||
| 701-1461-00L | Ecology and Evolution: Seminar Given that this course is a direct continuation of (and structurally entwined with) the "Ecology and Evolution: Term Paper" course of the preceding autumn semester, successful completion of the latter is a requirement for this course. | 3 credits | T. Städler, J. Alexander, S. Bonhoeffer, T. Crowther, A. Hall, J. Hille Ris Lambers, J. Jokela, J. Payne, G. Velicer, A. Widmer | ||
| Abstract | The organization and functioning of academic research as well as academic publishing are introduced and applied: students critically review two term papers written by their student colleagues. Based on the reviews, the authors of the papers write reply letters and revise their own term papers. They finally present their topic during an in-house "mini-conference" with a talk. | ||||
| Learning objective | • Students become familiar with the academic peer-review and publishing process • They learn to evaluate the quality of a manuscript and formulate constructive criticism • They learn to deal with criticism of their own work (by their student peers) • They practise oral presentations and discussions in English | ||||
| Content | The organization and functioning of academic research as well as academic publishing are introduced and applied: students critically review two term papers written by their student colleagues. Based on the reviews, the authors of the papers write reply letters and revise their own term papers. They finally present their topic during an in-house "mini-conference" with a talk. | ||||
| Lecture notes | none | ||||
| Prerequisites / Notice | Participation requires successful completion of "Ecology and Evolution: Term Paper" of the previous semester. | ||||
| 701-3001-00L | Environmental Systems Data Science: Data Processing Does not take place this semester. | 3 credits | 1.5G | L. Pellissier, E. J. Harris, J. Payne | |
| Abstract | Students are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses. | ||||
| Learning objective | The students are able to ● frame a data science problem and build a hypothesis ● describe the steps of a typical data science project workflow ● conduct selected steps of a workflow on specifically prepared datasets, with a focus on choosing, fitting and evaluating appropriate algorithms and models ● critically think about the limits and implications of a method ● visualise data and results throughout the workflow ● access online resources to keep up with the latest data science methodology and deepen their understanding | ||||
| Content | ● The data science workflow ● Access and handle (large) datasets ● Prepare and clean data ● Analysis: data exploratory steps ● Analysis: machine learning and computational methods ● Evaluate results and analyse uncertainty ● Visualisation and communication | ||||
| Prerequisites / Notice | 252-0840-02L Anwendungsnahes Programmieren mit Python 401-0624-00L Mathematik IV: Statistik 401-6215-00L Using R for Data Analysis and Graphics (Part I) 401-6217-00L Using R for Data Analysis and Graphics (Part II) 701-0105-00L Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften | ||||