Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Mohsen Ghaffari: Catalogue data in Autumn Semester 2018

Name Prof. Dr. Mohsen Ghaffari
FieldComputer Science
Address
Professur für Informatik
ETH Zürich, CAB G 32.1
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telephone+41 44 632 72 47
E-mailghaffari@inf.ethz.ch
URLhttps://people.inf.ethz.ch/gmohsen/
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
252-0209-00LAlgorithms, Probability, and Computing Information 8 credits4V + 2U + 1AE. Welzl, M. Ghaffari, A. Steger, D. Steurer, P. Widmayer
AbstractAdvanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).
ObjectiveStudying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory.
Lecture notesWill be handed out.
LiteratureIntroduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest;
Randomized Algorithms by R. Motwani und P. Raghavan;
Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf.
263-4500-00LAdvanced Algorithms Information 6 credits2V + 2U + 1AM. Ghaffari, A. Krause
AbstractThis is an advanced course on the design and analysis of algorithms, covering a range of topics and techniques not studied in typical introductory courses on algorithms.
ObjectiveThis course is intended to familiarize students with (some of) the main tools and techniques developed over the last 15-20 years in algorithm design, which are by now among the key ingredients used in developing efficient algorithms.
Contentthe lectures will cover a range of topics, including the following: graph sparsifications while preserving cuts or distances, various approximation algorithms techniques and concepts, metric embeddings and probabilistic tree embeddings, online algorithms, multiplicative weight updates, streaming algorithms, sketching algorithms, and a bried glance at MapReduce algorithms.
Prerequisites / NoticeThis course is designed for masters and doctoral students and it especially targets those interested in theoretical computer science, but it should also be accessible to last-year bachelor students.

Sufficient comfort with both (A) Algorithm Design & Analysis and (B) Probability & Concentrations. E.g., having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, though not required formally. If you are not sure whether you're ready for this class or not, please consulte the instructor.
263-4500-10LAdvanced Algorithms (with Project) Information Restricted registration - show details
Only for Data Science MSc.
8 credits2V + 2U + 2P + 1AM. Ghaffari, A. Krause
AbstractThis is an advanced course on the design and analysis of algorithms, covering a range of topics and techniques not studied in typical introductory courses on algorithms.
ObjectiveThis course is intended to familiarize students with (some of) the main tools and techniques developed over the last 15-20 years in algorithm design, which are by now among the key ingredients used in developing efficient algorithms.
Contentthe lectures will cover a range of topics, including the following: graph sparsifications while preserving cuts or distances, various approximation algorithms techniques and concepts, metric embeddings and probabilistic tree embeddings, online algorithms, multiplicative weight updates, streaming algorithms, sketching algorithms, and a bried glance at MapReduce algorithms.
Prerequisites / NoticeThis course is designed for masters and doctoral students and it especially targets those interested in theoretical computer science, but it should also be accessible to last-year bachelor students.

Sufficient comfort with both (A) Algorithm Design & Analysis and (B) Probability & Concentrations. E.g., having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, though not required formally. If you are not sure whether you're ready for this class or not, please consulte the instructor.
263-4505-00LAlgorithms for Large-Scale Graph Processing Information
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SM. Ghaffari
AbstractThis is a theory seminar, where we present and discuss recent algorithmic developments for
processing large-scale graphs. In particular, we focus on Massively Parallel Computation (MPC)
algorithms. MPC is a clean and general theoretical framework that captures the essential aspects
of computational problems in large-scale processing settings such as MapReduce, Hadoop,
Spark, Dryad, etc.
ObjectiveThis seminar familiarizes students with foundational aspects of large-scale graph processing,
and especially the related algorithmic tools and techniques. In particular, we discuss recent
developments in the area of Massively Parallel Computation. This is a mathematical abstraction
of practical large-scale processing settings such as MapReduce, and it has been receiving
significant attention over the past few years.

The seminar assumes no particular familiarity with parallel computation. However, we expect
that all the students are comfortable with basics of algorithms design and analysis, as well as
probability theory.

In the course of the seminar, the students learn how to structure a scientific presentation
(in English) which covers the key ideas of a paper, while omitting the less significant details.
ContentThe seminar will cover a number of the recent papers on Massively Parallel Computation.
As mentioned above, no familiarity with parallel computation is needed and all the relevant
background information will be explain by the instructor in the first lecture.
LiteratureThe papers will be presented in the first session of the seminar.