Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I sem. Oct 2, 2017 Jan 31, 2018
II sem. Mar 1, 2018 Jun 15, 2018
Exam sessions
Session From To
Sessione invernale d'esami Feb 1, 2018 Feb 28, 2018
Sessione estiva d'esame Jun 18, 2018 Jul 31, 2018
Sessione autunnale d'esame Sep 3, 2018 Sep 28, 2018
Degree sessions
Session From To
Sessione Estiva Lauree Magistrali Jul 19, 2018 Jul 19, 2018
Sessione Autunnale Lauree Magistrali Oct 18, 2018 Oct 18, 2018
Sessione Invernale Lauree Magistrali Mar 21, 2019 Mar 21, 2019
Holidays
Period From To
Christmas break Dec 22, 2017 Jan 7, 2018
Easter break Mar 30, 2018 Apr 3, 2018
Patron Saint Day May 21, 2018 May 21, 2018
Vacanze estive Aug 6, 2018 Aug 19, 2018

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

A B C D G L M P S

Accordini Simone

simone.accordini@univr.it +39 045 8027657

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980

Bicego Manuele

manuele.bicego@univr.it +39 045 802 7072

Bombieri Cristina

cristina.bombieri@univr.it 045-8027284

Bombieri Nicola

nicola.bombieri@univr.it +39 045 802 7094

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Cicalese Ferdinando

ferdinando.cicalese@univr.it +39 045 802 7969

Combi Carlo

carlo.combi@univr.it 045 802 7985

Constantin Gabriela

gabriela.constantin@univr.it 045-8027102

Daducci Alessandro

alessandro.daducci@univr.it +39 045 8027025

Dall'Alba Diego

diego.dallalba@univr.it +39 045 802 7074

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

Giugno Rosalba

rosalba.giugno@univr.it 0458027066

Laudanna Carlo

carlo.laudanna@univr.it 045-8027689

Liptak Zsuzsanna

zsuzsanna.liptak@univr.it +39 045 802 7032

Malerba Giovanni

giovanni.malerba@univr.it 045/8027685

Marcon Alessandro

alessandro.marcon@univr.it +39 045 802 7668

Maris Bogdan Mihai

bogdan.maris@univr.it +39 045 802 7074

Perduca Massimiliano

massimiliano.perduca@univr.it +39 045 802 7984

Sala Pietro

pietro.sala@univr.it 0458027850

Salvagno Gian Luca

gianluca.salvagno@univr.it 045 8124308-0456449264

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activities
2
F
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S004556

Coordinatore

Zsuzsanna Liptak

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

I semestre dal Oct 1, 2018 al Jan 31, 2019.

Learning outcomes

In this course we study advanced data structures for the analysis of genomic sequences, and in general, of textual data.

Knowledge and understanding: The course provides an understanding of the basic challenges and fundamental issues in processing textual data such as genomic sequences; knowledge of some of the most common computational problems on strings and sequences in genomic data analysis and other applications; familiarity with the most important text indices and their use in solving these problems, including complexity analysis (time and space).

Applying knowledge and understanding: At the end of the course the student will be able to translate typical problems of genomic sequence analysis in operations and algorithms on textual data and evaluate the computational cost incurred.

Making judgements: At the end of the course the student will be able to judge whether a given algorithm or data structure is appropriate for the problem at hand, including the evaluation of the computational cost incurred.

Communication: At the end of the course the student will be able to correctly formalize algorithms on sequences with or without the use of advanced text data structures.

Lifelong learning skills: At the end of the course the student will be able to read and understand independently scientific articles and specialized texts which use advanced string data structures for the analysis of genomic sequences or other textual data.

Program

In recent years, we have seen vast progress of research in computational biology, in which the use of dedicated data structures for genomic sequences, and other types of biological sequence data, has been decisive. At the same time, these methods can be, and are being, applied to all other kinds of textual data.

The recent explosion of the amounts of data available ("big data") is one of the major challenges for computer science today. Much of this data is in form of text (or can be easily rendered in textual form): genomic sequences and other biological sequences, webpages, emails, scanned books, musical data, and many others. In order to be able to efficiently store, process, and extract information from this data, we need dedicated data structures and algorithms, i.e. data structures specifically developed for strings, also referred to as text indices.

Program of the course:

1) introduction to strings (sequences), their basic properties and fundamental issues: alphabet size, character comparison, string sorting

2) classical exact pattern matching algorithms (non index-based): Knuth-Morris-Pratt, Boyer-Moore, Rabin-Karp, Aho-Corasick

3) Text indices I:
- tries
- suffix trees

4) Text indices II:
- suffix arrays, enhanced suffix arrays
- Burrows-Wheeler Transform (BWT)

For each of these text indices, we will study their properties, efficient construction, and applications to specific string problems.

No previous knowledge of biology is necessary for following the course.

Reference texts
Author Title Publishing house Year ISBN Notes
Dan Gusfield Algorithms on Strings, Trees, and Sequences Cambridge University Press 1997 0 521 58519 8
Enno Ohlebusch Bioinformatics Algorithms 2013 978-3-00-041316-2
Maxime Crochemore and Wojciech Rytter Jewels of Stringology World Scientific 2003 981-02-4782-6

Examination Methods

Final exam: written and oral. In the written exam, both theoretical questions will be asked (running times and storage space of algorithms, properties of the data structures studied), and concrete examples will have to be solved (compute the suffix tree, suffix array, BWT etc. of a given string, apply certain algorithms). In the oral exam, the student will have the opportunity to explain in detail his/her solution and show to what extent he/she has understood the topics studied.

The exam will show that the student
- has acquired sufficient understanding of the most important issues with respect to handling large textual data (alphabet type, comparison of strings, string sorting, size of textual data)
- can apply, explain, and analyze the algorithms studied for non-index based pattern matching
- can apply, explain, and analyze the data structures studied, in particular construction algorithms for and storage space required by these data structures (inverted index, trie, suffix tree, suffix array, BWT)
- can apply, explain, and analyze some applications of these data structures to problems on strings, such as pattern matching, matching statistics, palindromes, etc.

The exam is the same for all students (whether or not they followed the lectures).

Type D and Type F activities

Modules not yet included

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Graduation


Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Career management


Area riservata studenti