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 semestre Oct 1, 2019 Jan 31, 2020
II semestre Mar 2, 2020 Jun 12, 2020
Exam sessions
Session From To
Sessione invernale d'esame Feb 3, 2020 Feb 28, 2020
Sessione estiva d'esame Jun 15, 2020 Jul 31, 2020
Sessione autunnale d'esame Sep 1, 2020 Sep 30, 2020
Degree sessions
Session From To
Sessione Estiva. Jul 16, 2020 Jul 16, 2020
Sessione Autunnale. Oct 15, 2020 Oct 15, 2020
Sessione Invernale. Mar 18, 2021 Mar 18, 2021
Period From To
Festa di Ognissanti Nov 1, 2019 Nov 1, 2019
Festa dell'Immacolata Dec 8, 2019 Dec 8, 2019
Vacanze di Natale Dec 23, 2019 Jan 6, 2020
Vacanze di Pasqua Apr 10, 2020 Apr 14, 2020
Festa della Liberazione Apr 25, 2020 Apr 25, 2020
Festa del lavoro May 1, 2020 May 1, 2020
Festa del Santo Patrono May 21, 2020 May 21, 2020
Festa della Repubblica Jun 2, 2020 Jun 2, 2020
Vacanze estive Aug 10, 2020 Aug 23, 2020

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


Accordini Simone +39 045 8027657

Baruffi Maria Caterina

Bicego Manuele +39 045 802 7072

Bombieri Cristina 045-8027284

Bombieri Nicola +39 045 802 7094

Cicalese Ferdinando +39 045 802 7969

Combi Carlo 045 802 7985

Constantin Gabriela 045-8027102

Cozza Vittoria

Daducci Alessandro +39 045 8027025

Delledonne Massimo 045 802 7962; Lab: 045 802 7058

Franco Giuditta +39 045 802 7045

Fummi Franco 045 802 7994

Giacobazzi Roberto +39 045 802 7995

Giugno Rosalba 0458027066

Laudanna Carlo 045-8027689

Liptak Zsuzsanna +39 045 802 7032

Malerba Giovanni 045/8027685

Marcon Alessandro +39 045 802 7668

Maris Bogdan Mihai +39 045 802 7074

Perduca Massimiliano +39 045 802 7984

Sala Pietro 0458027850

Salvagno Gian Luca 045 8124308-0456449264

Spoto Nicola Fausto +39 045 8027940

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°
English B2
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activities

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



Zsuzsanna Liptak




English en

Scientific Disciplinary Sector (SSD)



I semestre dal Oct 1, 2020 al Jan 29, 2021.

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.


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 indexes.

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 indexes I:
- tries
- suffix trees

4) Text indexes II:
- suffix arrays, enhanced suffix arrays
- Burrows-Wheeler Transform (BWT), FM-index

For each of these text indexes, 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
V. Mäkinen, D. Belazzougui, F. Cunial, and A.I. Tomescu Genome Scale Algorithm Design (Edizione 1) Cambridge University Press 2015 ISBN 978-1-107-07853-6
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

1° periodo di lezioni From 9/30/19 To 12/14/19
years Modules TAF Teacher
1° 2° The fashion lab (1 ECTS) D Not yet assigned
I semestre From 10/1/19 To 1/31/20
years Modules TAF Teacher
1° 2° Python programming language D Maurizio Boscaini (Coordinatore)
II semestre From 3/2/20 To 6/12/20
years Modules TAF Teacher
1° 2° CyberPhysical Laboratory D Andrea Calanca (Coordinatore)
1° 2° C++ Programming Language D Federico Busato (Coordinatore)
1° 2° Matlab-Simulink programming D Bogdan Mihai Maris (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° Corso Europrogettazione D Not yet assigned
1° 2° The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. D Matteo Cristani

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.

Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.



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