Access profile

There cannot be a specific profile for applicants to a programme in a discipline that is so horizontal, interdisciplinary and diverse as artificial intelligence. Areas of research and application range from biology and medicine to computational linguistics, artificial vision and robotic intelligence, to name a few of the extremes.

However, to provide an identifiable base of basic knowledge, applicants’ prior education and training should preferably have provided skills and abilities in research equivalent to those provided by the master’s degree programme in Artificial Intelligence, currently offered by the UPC, or to those provided by specialisations in Advanced Computation and Computer Graphics and Virtual Reality in the master’s degree programme in Innovation and Research in Informatics, which stems from the restructuring of master’s-level studies in ICT at the UPC.

The master’s degree programme in Artificial Intelligence also accepts students from a wide range of other scientific disciplines.

Output profile

Doctoral candidates who complete a doctoral degree will have acquired the following competencies, which are needed to carry out quality research (Royal Decree 99/2011, of 28 January, which regulates official doctoral studies):

a) A systematic understanding of the field of study and a mastery of the research skills and methods related to the field.
b) An ability to conceive, design or create, put into practice and adopt a substantial process of research or creation.
c) An ability to contribute to pushing back the frontiers of knowledge through original research.
d) A capacity for critical analysis and an ability to assess and summarise new and complex ideas.
e) An ability to communicate with the academic and scientific community and with society in general as regards their fields of knowledge in the manner and languages that are typical of the international scientific community to which they belong.
f) An ability to foster scientific, technological, social, artistic and cultural progress in academic and professional contexts within a knowledge-based society.

The award of a doctoral degree must equip the graduate for work in a variety of settings, especially those requiring creativity and innovation. Doctoral graduates must have at least acquired the personal skills needed to:

a) Develop in contexts in which there is little specific information.
b) Find the key questions that must be answered to solve a complex problem.
c) Design, create, develop and undertake original, innovative projects in their field.
d) Work as part of a team and independently in an international or multidisciplinary context.
e) Integrate knowledge, deal with complexity and make judgements with limited information.
f) Offer criticism on and intellectually defend solutions.

Finally, with respect to competencies, doctoral students must:
a) have acquired advanced knowledge at the frontier of their discipline and demonstrated, in the context of internationally recognised scientific research, a deep, detailed and well-grounded understanding of theoretical and practical issues and scientific methodology in one or more research fields;
b) have made an original and significant contribution to scientific research in their field of expertise that has been recognised as such by the international scientific community;
c) have demonstrated that they are capable of designing a research project that serves as a framework for carrying out a critical analysis and assessment of imprecise situations, in which they are able to apply their contributions, expertise and working method to synthesise new and complex ideas that yield a deeper knowledge of the research context in which they work;
d) have developed sufficient autonomy to set up, manage and lead innovative research teams and projects and scientific collaborations (both national and international) within their subject area, in multidisciplinary contexts and, where appropriate, with a substantial element of knowledge transfer;
e) have demonstrated that they are able to carry out their research activity in a socially responsible manner and with scientific integrity;
f) have demonstrated, within their specific scientific context, that they are able to make cultural, social or technological advances and promote innovation in all areas within a knowledge-based society;
g) have demonstrated that they are able to participate in scientific discussions at the international level in their field of expertise and disseminate the results of their research activity to audiences of all kinds.

Number of places

10

Duration of studies and dedication regime

Duration
The maximum period of study for full-time doctoral studies is three years, counted from the date of admission to the programme to the date of submission of the doctoral thesis. The academic committee of the doctoral programme may authorise a doctoral candidate to pursue doctoral studies on a part-time basis. In this case, the maximum period of study is five years, counting from the date of admission to the programme to the date of submission of the doctoral thesis. For calculating these periods, the date of admission is considered to be the date of the first enrolment for tutorials, and the date of submission the moment in which the Doctoral School officially deposits the doctoral thesis.

For full-time doctoral candidates, the minimum period of study is two years, counted from the date of an applicant's admission to the programme until the date on which the doctoral thesis is deposited; for part-time doctoral candidates it is four years. When there are justified grounds for doing so, and the thesis supervisor and academic tutor have given their authorisation, doctoral candidates may request that the academic committee of their doctoral programme exempt them from the minimum period of study requirement.

The calculation of periods of study will not include periods of absence due to illness, pregnancy or any other reason provided for in the regulations in force. Students who find themselves in any of these circumstances must notify the academic committee of the doctoral programme, which, where appropriate, must inform the Doctoral School. Doctoral candidates may also temporarily withdraw from the programme for up to one year, and this period may be extended for an additional year. Doctoral candidates who wish to interrupt their studies must submit a justified request to the academic committee of the doctoral programme, which will decide whether or not to approve the request. Each programme will establish conditions for readmission to doctoral studies.

Extension
If full-time doctoral candidates have not applied to deposit their thesis by the end of the three-year period of study, the academic committee of the programme may authorise an extension of up to one year. In exceptional circumstances, a further one-year extension may be granted, subject to the conditions established by the corresponding doctoral programme. In the case of part-time doctoral candidates, an extension of two years may be authorised. In both cases, in exceptional circumstances a further one-year extension may be granted by the Doctoral School's Standing Committee, upon the submission of a reasoned application by the academic committee of the doctoral programme.

Dismissal from the doctoral programme
A doctoral candidate may be dismissed from a doctoral programme for the following reasons:

• The doctoral candidate submitting a justified application to withdraw from the programme.
• The maximum period of study and of extensions thereof ending.
• The doctoral candidate not having enrolled every academic year (unless he or she has been authorised to temporarily withdraw).
• The doctoral candidate failing two consecutive assessments.
• The doctoral candidate having disciplinary proceedings filed against him or her that rule that he or she must be dismissed from the UPC.

Dismissal from the programme implies that doctoral candidates cannot continue studying at the UPC and the closing of their academic record. This notwithstanding, they may apply to the academic committee of the programme for readmission and the committee must reevaluate them in accordance with the criteria established in the regulations.

Organization

• Larrosa Bondia, Francisco Javier

• Cortes Garcia, Ulises
• Larrosa Bondia, Francisco Javier
• Nebot Castells, Maria Angela
• Padro Cirera, Lluis
• Sanchez Marre, Miquel

• Department of Computer Science (PROMOTORA)

Agreements with other institutions

Access profile

There cannot be a specific profile for applicants to a programme in a discipline that is so horizontal, interdisciplinary and diverse as artificial intelligence. Areas of research and application range from biology and medicine to computational linguistics, artificial vision and robotic intelligence, to name a few of the extremes.

However, to provide an identifiable base of basic knowledge, applicants’ prior education and training should preferably have provided skills and abilities in research equivalent to those provided by the master’s degree programme in Artificial Intelligence, currently offered by the UPC, or to those provided by specialisations in Advanced Computation and Computer Graphics and Virtual Reality in the master’s degree programme in Innovation and Research in Informatics, which stems from the restructuring of master’s-level studies in ICT at the UPC.

The master’s degree programme in Artificial Intelligence also accepts students from a wide range of other scientific disciplines.

Access requirements

Applicants must hold a Spanish bachelor’s degree or equivalent and a Spanish master’s degree or equivalent, provided they have completed a minimum of 300 ECTS credits on the two degrees (Royal Decree 43/2015, of 2 February)

In addition, the following may apply:

• Holders of an official degree awarded by a university in Spain or any other country in the European Higher Education Area, pursuant to the provisions of Article 16 of Royal Decree 1393/2007, of 29 October, which establishes official university course regulations, who have completed a minimum of 300 ECTS credits on official university degrees, of which at least 60 must be at the master's degree level.
• Holders of an official Spanish bachelor’s degree comprising at least 300 credits, as provided for by EU regulations. Holder of degrees of this kind must complete bridging courses unless the curriculum of the bachelor’s degree in question included research training credits equivalent in value to those which would be earned on a master's degree.
• Holders of an official university qualification who, having passed the entrance examination for specialised medical training, have completed at least two years of a training course leading to an official degree in a health-sciences specialisation.
• Holders of a degree issued under a foreign education system. In these cases, homologation is not required, but the UPC must verify that the degree certifies a level of training equivalent to an official Spanish master's degree and qualifies the holder for admission to doctoral studies in the country where it was issued. Admission on this basis does not imply homologation of the foreign degree or its recognition for any purpose other than admission to doctoral studies.
• Holders of a Spanish doctoral qualification issued under previous university regulations.

Note 1: Doctoral studies entrance regulations for holders of an undergraduate degree awarded before the introduction of the EHEA (CG 47/02 2014)

Note 2: Governing Council Decision 64/2014, which approves the procedure and criteria for assessing the fulfilment of academic admission requirements for doctoral studies by holders of non-homologated foreign degrees (CG 25/03 2014)

Admission criteria and merits assessment

Aspiring doctors in Artificial Intelligence should have academic (master’s) and professional backgrounds that show that they have the academic and intellectual capacities and the consistency needed to ensure that they can successfully complete their studies in a timely fashion.

The programme will assess candidates on the following criteria:

 • Academic transcripts for all university degree programmes taken.
 • Publications (if applicable).
 • Research experience (if applicable).
 • CV.
 • Appropriateness of research interests with the programmes lines of research and the applicant’s background.
 • Other merits.

All of those who are admitted to the doctoral programme will be expected to be able to understand technical texts written in English and easily follow courses and conferences in English. Candidates will have to demonstrate that they possess a knowledge of the English language equal to a B1 level as per the Common European Framework of Reference for Languages, both spoken and written, in the timeframes and manners determined by the Academic Committee.

Weighting of criteria:
The Academic Committee of the doctoral programme will be in charge of the process of selecting and admitting candidates. This selection process will be based on a scale that will give priority to the following aspects:

- applicants’ academic transcripts (60%)
- verifiable knowledge of the English language (20%).
- previous experience (20%).

Training complements

The academic committee for the programme may require that doctoral students pass specific bridging courses. In this case, the Committee will monitor the bridging courses and set up appropriate criteria to limit their length.

The courses could involve training in research or transversal education, but in no case will candidates be required to enrol in 60 or more ECTS credits.

Depending on the activities completed by the candidates, the programme’s Academic Committee may propose measures, in addition to those established by current regulations, that would disassociate candidates who do not meet the established requisites.

If necessary, the bridging courses required will be determined by the academic tutors following the recommendations given to each student during the admission process. Recommended courses will be those offered through the joint master’s degree programme in Artificial Intelligence.

Core subjects:

Based on their previous studies, candidates with non-scientific backgrounds will have to pass these courses, in addition to one subject from the following blocks, depending on their chosen line of research.

• Computational Intelligence (CI)
• Computational Vision (CV)
• Intelligent Data Analysis Applications in Business
• Introduction to MultiAgent Systems (IMAS)
• Introduction to Machine Learning (IML)
• Introduction to Natural Language Processing (INLP)
• Planning and Approximate Reasoning (PAR)

Subjects on more specific topics:

For candidates with scientific backgrounds, 1 or 2 blocks of subjects, depending on their chosen line of research.

MultiAgent Systems:
• Multi-Agent Systems Design (MASD)
• Normative and Dynamic Virtual Worlds (NDVW)
• Self-Organising Agent Systems (SOAS)

Human-Computer Interaction:
• Advanced Natural Language Processing (ANLP)
• Cognitive Interaction with Robots (CIR)
• Human-Computer Interaction (HCI)

Advanced Computational Intelligence:
• Advanced Topics in Computational Intelligence (ATCI)
• Complex Networks (CN)
• Intelligent Data Analysis and Data Mining (IDADM)

Knowledge Engineering and Machine Learning:
• Advanced Machine Learning Techniques (AMLT)
• Intelligent Decision Support Systems (IDSS)
• Knowledge Representation and Engineering (KRE)
• Multi-Criteria Decision Support Systems (MCDSS)

Modelling, Reasoning and Problem Solving:
• Constraint Processing and Programming (CPP)
• Logics for Artificial Intelligence (LAI)
• Minds, Brains and Machines (MBM)
• Probabilistic Graphical Models (PGM)

Vision, Perception and Robotics:
• Advanced Artificial Vision (AAV)
• Cooperative Robotics (CR)
• Object Recognition (OR)

Enrolment period for new doctoral students

Students enrolling in the doctoral programme for the first time must do so by the deadline specified in the admission decision.
Unless otherwise expressly indicated, enrolments corresponding to admission decisions issued from the second half of April on must be completed within the ordinary enrolment period for the current academic year.

More information at the registration section for new doctoral students

Enrolment period

Ordinary period for second and successive enrolments: first half of October.

More information at the general registration section

Procedure for the preparation and defense of the research plan

Doctoral candidates must submit a research plan, which will be included in their doctoral student activity report, before the end of the first year. The plan may be improved over the course of the doctoral degree. It must be endorsed by the tutor and the supervisor, and it must include the method that is to be followed and the aims of the research.

At least one of these annual assessments will include a public presentation and defence of the research plan and work done before a committee composed of three doctoral degree holders, which will be conducted in the manner determined by each academic committee. The examination committee awards a Pass or Fail mark. A Pass mark is a prerequisite for continuing on the doctoral programme. Doctoral candidates awarded a Fail mark must submit a new research plan for assessment by the academic committee of the doctoral programme within six months.

The committee assesses the research plan every year, in addition to all of the other activities in the doctoral student activity report. Doctoral candidates who are awarded two consecutive Fail marks for the research plan will be obliged to definitely withdraw from the programme.

If they change the subject of their thesis, they must submit a new research plan.

Formation activities

1. Tutorials (meetings with the supervisor), approximately 288 hours (the exact number and frequency will be defined by the supervisor), mandatory.

2. Artificial Intelligence Seminar, approximately 30 hours (depending on participants’ availability), optional.

3. Theoretical-Scientific Seminar, approximately 30 hours (depending on participants’ availability), optional.

4. Intensive courses, approximately 30 hours (depending on professors’ availability), optional.

5. International scientific meetings (conferences, workshops, schools, etc.), approximately 24 hours, optional.

Procedure for assignment of tutor and thesis director

The academic committee of the doctoral programme assigns a thesis supervisor to each doctoral candidate when they are admitted or enrol for the first time, taking account of the thesis supervision commitment referred to in the admission decision.

The thesis supervisor will ensure that training activities carried out by the doctoral candidate are coherent and suitable, and that the topic of the candidate’s doctoral thesis will have an impact and make a novel contribution to knowledge in the relevant field. The thesis supervisor will also guide the doctoral candidate in planning the thesis and, if necessary, tailoring it to any other projects or activities undertaken. The thesis supervisor will generally be a UPC professor or researcher who holds a doctoral degree and has documented research experience. This includes PhD-holding staff at associated schools (as determined by the Governing Council) and UPC-affiliated research institutes (in accordance with corresponding collaboration and affiliation agreements). When thesis supervisors are UPC staff members, they also act as the doctoral candidate’s tutor.

PhD holders who do not meet these criteria (as a result of their contractual relationship or the nature of the institution to which they are attached) must be approved by the UPC Doctoral School's Standing Committee in order to participate in a doctoral programme as researchers with documented research experience.

The academic committee of the doctoral programme may approve the appointment of a PhD-holding expert who is not a UPC staff member as a candidate’s thesis supervisor. In such cases, the prior authorisation of the UPC Doctoral School's Standing Committee is required. A UPC staff member who holds a doctoral degree and has documented research experience must also be proposed to act as a co-supervisor, or as the doctoral candidate’s tutor if one has not been assigned.

A thesis supervisor may step down from this role if there are justified reasons (recognised as valid by the committee) for doing so. If this occurs, the academic committee of the doctoral programme will assign the doctoral candidate a new thesis supervisor.

Provided there are justified reasons for doing so, and after hearing any relevant input from the doctoral candidate, the academic committee of the doctoral programme may assign a new thesis supervisor at any time during the period of doctoral study.

If there are academic reasons for doing so (an interdisciplinary topic, joint or international programmes, etc.) and the academic committee of the programme gives its approval, an additional thesis supervisor may be assigned. Supervisors and co-supervisors have the same responsibilities and academic recognition.

The maximum number of supervisors of a doctoral thesis is two: a supervisor and a co-supervisor.

For theses carried out under a cotutelle agreement or as part of an Industrial Doctorate, if necessary and if the agreement foresees it this maximum number of supervisors may not apply. This notwithstanding, the maximum number of supervisors belonging to the UPC is two.

More information at the PhD theses section

Permanence

The academic committee of the programme may authorise an extension of up to one year for full-time doctoral candidates who have not applied to deposit their thesis by the end of the three-year period of study, in the terms outlined in the Academic Regulations for Doctoral Studies of the Universitat Politècnica de Catalunya. In the case of part-time candidates, an extension of two years may be authorised. In both cases, in exceptional circumstances a further one-year extension may be granted by the Doctoral School's Standing Committee, upon the submission of a reasoned application by the academic committee of the doctoral programme.

A doctoral candidate may be dismissed from a doctoral programme for the following reasons:

• The doctoral candidate submitting a justified application to withdraw from the programme.
• The maximum period of study and of extensions thereof ending.
• The doctoral candidate not having enrolled every academic year (unless he or she has been authorised to temporarily withdraw).
• The doctoral candidate failing two consecutive assessments.
• The doctoral candidate having disciplinary proceedings filed against him or her that rule that he or she must be dismissed from the UPC.

Dismissal from the programme implies that doctoral candidates cannot continue studying at the UPC and the closing of their academic record. This notwithstanding, they may apply to the academic committee of the programme for readmission and the committee must reevaluate them in accordance with the criteria established in the regulations.

International Mention

The doctoral degree certificate may include International Doctorate mention. In this case, the doctoral candidate must meet the following requirements:

a) During the period of study leading to the award of the doctoral degree, the doctoral candidate must have spent at least three months at a respected higher education institution or research centre outside Spain to complete courses or do research work. The stays and activities carried out must be endorsed by the thesis supervisor and authorised by the academic committee of the programme. The candidate must provide a certifying document issued by the person responsible for the research group of the body or bodies where the stay or activity was completed. This information will be added to the doctoral student’s activity report.
b) Part of the thesis (at least the summary and conclusions) must be written and presented in one of the languages commonly used for science communication in the relevant field of knowledge, which must not be an official language of Spain. This rule does not apply to stays and reports in Spanish or to experts from Spanish-speaking countries.
c) At least two PhD-holding experts belonging to a higher education institution or research centre outside Spain must have issued officially certified reports on the thesis.
d) The thesis examination committee must have included at least one PhD-holding expert from a higher education or research institution outside Spain who was not responsible for the candidate’s stay abroad (point a) above).
e) The thesis defence must have taken place on UPC premises or, in the case of joint programmes, at the location specified in the collaboration agreement.

List of authorized thesis for defense

Last update: 23/09/2023 04:45:29.

List of lodged theses

Last update: 23/09/2023 04:30:25.

List of defended theses by year

• BLONDEL, GILLES JACQUES FORTUNATO: Causal discovery and prediction: methods and algorithms
  
  Author: BLONDEL, GILLES JACQUES FORTUNATO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE
  Department: Department of Computer Science (CS)
  Mode: Normal
  Reading date: 07/06/2023
  Thesis director: GAVALDÀ MESTRE, RICARD | ARIAS VICENTE, MARTA
  

  Committee:
       PRESIDENT: EBERHARDT, FREDERICK
       SECRETARI: ARRATIA QUESADA, ARGIMIRO ALEJANDRO
       VOCAL: LÓPEZ IBÁÑEZ, BEATRIZ
  Thesis abstract: This thesis focuses on the discovery of causal relations and on the prediction of causal effects. Regarding causal discovery, this thesis introduces a novel and generic method to learn causal graphs by performing a sequence of interventions, where each intervention is applied on a single value of the intervened variables, while minimizing the overall cost of the sequence of intervened and observed variables during the discovery process. Regarding causal effect prediction, this thesis introduces a comprehensive causal reasoning method for models recurrent in time. In this thesis, all causal models are assumed to contain hidden confounders that have an influence on observed variables in the causal model, except when explicitly referring to causal models without hidden confounders as a sub-case. Also all variables are assumed to be in a finite domain. Contributions to the Discovery of Causal RelationsOur method for the discovery of causal relations introduces several novelties. Firstly, we use interventions on a single value of the intervened variables. All previous methods require interventions on several values of the intervened variables in order to measure correlation or conditional independence among variables. By seeing do-calculus as a tool to predict systematically and numerically the effect of all the interventions that are possible, without having to actually perform them, we have moved the search space out of the real world, and eliminated the need for systematic correlation and independence testing in the real world. We assume that computational cost is not a concern, if we compare it with the cost of actually experimenting in the real world. Secondly, we accept any set of candidate graphs as input to our method. Previous knowledge may or may not be in the form of an equivalence class of graphs, and the set of candidate graphs may or may not have any particular parametrical characteristic. Some candidate graphs may have been discarded previously based on analyzing the available observational data, however no algorithm based on observational data only can identify the true graph of a causal model. As such, our method accepts any set of causal graph candidates, with the only assumption that the true graph, the solution to the problem, is included in the set of candidates. Thirdly, all causal graphs are assumed to contain hidden confounders, whereas most previous work focused on causal graphs withouthidden confounders.Contributions to the Prediction of Causal EffectsThe second subject of this thesis is causal prediction. The thesis focus is on dynamic causal reasoning: given the formal description of a dynamic causal system and a set of assumptions, we propose methods to evaluate the modified trajectory of the system over time, after an experiment or intervention. We assume that the observation time-scale is sufficiently small compared to the system dynamics, and that the causal model includes both the non-equilibrium causal relations and those under equilibrium states. We assume that a stable set of causal dependencies exist which generate the system evolution along time. Our proposed algorithms take such models as an input and predict their evolution over time, after an intervention. We also introduce transportability algorithms in the dynamic setting, where causal knowledge in source time-recurrent domains may be used for prediction in target time-recurrent domains.
  

Last update: 23/09/2023 05:01:38.

Research groups

• KEMLG-Knowledge Engineering and Machine Learning Group
• LOGPROG-Logic and Programming
• SOCO-Soft Computing
• TALP-Centre for Language and Speech Technologies and Applications

Teachers

• Alquezar Mancho, René
• Angulo Bahon, Cecilio
• Arias Vicente, Marta
• Bejar Alonso, Javier
• Cortes Garcia, Ulises
• Gibert Oliveras, Karina
• Larrosa Bondia, Francisco Javier
• Martin Muñoz, Mario
• Mugica Alvarez, Francisco José
• Nebot Castells, Maria Angela
• Padro Cirera, Lluis
• Sanchez Marre, Miquel
• Vazquez Salceda, Javier
• Vellido Alcacena, Alfredo

• Andre Sanchez, Michel
• Gavalda Mestre, Ricard
• Sanfeliu Cortes, Alberto
• Velasco Garcia, Manel