Training and Research
PhD Programme Courses/classes - 2023/2024
Non monotonic reasoning
Credits: 3
Language: English
Teacher: Matteo Cristani
Sustainable Embodied Mechanical Intelligence
Credits: 3
Language: English
Teacher: Giovanni Gerardo Muscolo
Brain Computer Interfaces
Credits: 3
Language: English
Teacher: Silvia Francesca Storti
A practical interdisciplinary PhD course on exploratory data analysis
Credits: 4
Language: English
Teacher: Prof. Vincenzo Bonnici (Università di Parma)
Multimodal Learning and Applications
Credits: 5
Language: English
Teacher: Cigdem Beyan
Introduction to Blockchain
Credits: 3
Language: English
Teacher: Sara Migliorini
Autonomous Agents and Multi-Agent Systems
Credits: 5
Language: English
Teacher: Alessandro Farinelli
Cyber-physical systems security
Credits: 3
Language: English/Italian
Teacher: Massimo Merro
Foundations of quantum languages
Credits: 3
Language: English
Teacher: Margherita Zorzi
Advanced Data Structures for Textual Data
Credits: 3
Language: English
Teacher: Zsuzsanna Liptak
AI and explainable models
Credits: 5
Language: English
Teacher: Gloria Menegaz, Lorenza Brusini
Automated Software Testing
Credits: 4
Language: English
Teacher: Mariano Ceccato
Elements of Machine Teaching: Theory and Appl.
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
Introduction to Quantum Machine Learning
Credits: 4
Language: English
Teacher: Alessandra Di Pierro
Laboratory of quantum information in classical wave-optics analogy
Credits: 3
Language: English
Teacher: Claudia Daffara
Multimodal Learning and Applications (2023/2024)
Teacher
Referent
Credits
5
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
For intelligent systems, adeptly interpreting, reasoning, and fusing multimodal information is essential. One of the latest and most promising trends in machine/deep learning research is Multimodal Learning, a multi-disciplinary field focused on integrating and modeling multiple modalities, such as acoustics, linguistics and vision. This course explores fundamental concepts in multimodal learning, including alignment, fusion, joint learning, temporal learning, and representation
learning. Through an examination of recent state-of-the-art papers, the course emphasizes effective computational algorithms tailored for diverse applications. Various datasets, sensing approaches, and computational methodologies will be explored, with discussions on existing limitations and potential future directions. Course evaluation will involve a small project assigned to student groups.
Scheduled Lessons
When | Classroom | Teacher | topics |
---|---|---|---|
Monday 17 June 2024 14:00 - 18:00 Duration: 4:00 AM |
Ca' Vignal 2 - L [67 - 1°] | Cigdem Beyan | The definition of multimodality, multimodality versus multimedia, heterogeneous and interconnected data, modalities, common sensors, definitions of multimodal machine learning and multimodal artificial intelligence, research tasks: audio-visual speech recognition, affective computing, synthesis, human-human-robot interaction analysis, content understanding,...., multimedia information retrieval, Multimodal technical challenges: a) representation (joint, coordinated), contrastive learning, CLIP, b) Alignment (explicit, implicit), Dynamic time warping, self-attention, cross attention, transformers, why attention is important, Semantic alignment, visual grounding, text grounding, Referring Expression Segmentation. State of the art examples for each challenge. |
Tuesday 18 June 2024 14:00 - 18:00 Duration: 4:00 AM |
Ca' Vignal 2 - L [67 - 1°] | Cigdem Beyan | Multimodal learning challenges: c) Translation (example based, generative based), GAN based example, avatar creation, Dall-E, Dall-E 2, Stable diffusion, d) Fusion (late, early fusion), Multimodal kernel learning, graphical models, neural networks, e) co-learning definition, co-learning via representation, f) generation for summarization and creation, multimodal summarization and example approaches, creation evaluation metrics (IS, FID, SID) and their limitations, generation open challenges, g) learning and optimization (overfitting to generalization ratio), gradient blending, h) modality bias, i) fairness, explainability, interpretability. |
Wednesday 19 June 2024 14:00 - 18:00 Duration: 4:00 AM |
Ca' Vignal 2 - L [67 - 1°] | Cigdem Beyan | Applications: Intro to human behavior understanding. The definition of Social Signal Processing, social signals, verbal and nonverbal communication, and nonverbal cues (body activity, eye gaze, facial expressions, vocal behavior, physical appearance, proxemics), methodologies, toolboxes, libraries used to extract all these nonverbal cues. Types of interactions (joint focused, common focused,...), f-formations, example applications with references, into to open-face, mediapipe, openpose, opensmile. Human-human interaction datasets |
Thursday 20 June 2024 14:00 - 18:00 Duration: 4:00 AM |
Ca' Vignal 2 - L [67 - 1°] | Cigdem Beyan | SSP examples: a) Emergent leader detection in meeting environments: dataset creation, annotation, used nonverbal cues, results, future work. b) Gaze target detection: unimodal SOTA, multimodal SOTA with depth maps, multimodal SOTA with skeletons and deep maps, privacy-preserving gaze target detection, transformer-based gaze target detection, multi task gaze target detection, c) predicting gaze from egocentric social interactions (dataset creation, methodology, evaluation, future work), d) social group detection (methodology, evaluation). SSP challenges and future directions (privacy preserving, domain adaptation, unsupervised learning,....) |
Friday 21 June 2024 14:00 - 18:00 Duration: 4:00 AM |
Ca' Vignal 2 - L [67 - 1°] | Cigdem Beyan | Multimodal activity recognition (HAR): definition, possible sensors, importance, challenges, Approaches and datasets: HAR using RGB camera, HAR using RGB+depth, point-cloud based HAR, Egocentric action recognition datasets. Introducing EGO4D dataset, challenges, methodology: short term object interaction anticipation. Introducing Ego-Exo4 dataset, benchmarks, sensors, tasks. Multimodal emotion recognition: definition of emotions, discrete emotions, Russel theory, cues to represent and predict emotions automatically, datasets from unimodal to multimodal, open questions, rare applications, open research problem. Methodology: Zero-shot multimodal emotion recognition, disentanglement based multimodal emotion recognition. |
PhD school courses/classes - 2023/2024
Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).
1. PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.
More information regarding CFUs is found in the Handbook for PhD Students: https://www.univr.it/phd-vademecum
2. Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, no confirmation e-mail will be sent after signing up. Please do not enquiry: if you entered the requested information, then registration was silently successful.
3. When Zoom links are not explicitly indicated, courses are delivered in presence only.
4. All information we have is published here. Please do not enquiry for missing information or Zoom links: as soon as we get new information, we will promptly publish it on this page.
Teaching Activities ex DM 226/2021: Linguistic Activities
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Law and Economics]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
Teacher: Monica Antonello
ENGLISH FOR ACADEMIC WRITING SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Law and Economics]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
Teacher: Monica Antonello
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
Teaching Activities ex DM 226/2021: Research management and Enhancement
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Arts and Humanities]
Credits: 2,5
Language: Italian
Teacher: Donatella Boni
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Law and Economics]
Credits: 2,5
Language: Italian
Teacher: Luisella Zocca
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Scientific Area]
Credits: 2,5
Language: Italian
Teacher: Elena Scanferla
Teaching Activities ex DM 226/2021: Statistics and Computer Sciences
INTRODUCTION TO PROBABILITY (MODULE I)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO PROBABILITY (MODULE II)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO STATISTICAL INFERENCE
Credits: 1
Language: English
Validità e affidabilità delle misure e dei test diagnostici
Credits: 0,5
Language: English
Teacher: Alessandro Marcon
BASIC LEVEL STATISTICS
Credits: 2,5
Language: English
BASIC LEVEL STATISTICS
Credits: 2,5
Language: Italian
Statistical analysis with R - module I
Credits: 1
Language: Italian
Teacher: Erica Secchettin
GENERALIZED LINEAR MODELS: LOGISTIC REGRESSION, LOGLINEAR MODEL, POISSON MODEL
Credits: 2
Language: English
Teacher: Lucia Cazzoletti
Disegno dello studio nella ricerca osservazionale e sperimentale
Credits: 1,5
Language: English
Teacher: Alessandro Marcon
Calcolo della numerosità campionaria in funzione di una precisione o potenza statistica prefissata
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Introduzione alla meta-analisi per la ricerca biomedica (revisione della letteratura, raccolta dei dati, costruzione del database)
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Applicazioni della meta-analisi in campo epidemiologico e medico
Credits: 1
Language: Inglese
Teacher: Giuseppe Verlato
Survival analysis: log-rank test, Kaplan-Meier survival curves, Cox regression model
Credits: 1,5
Language: Inglese - English
Teacher: Simone Accordini
INTERMEDIATE STATISTICS [Recommended for Human Sciences]
Credits: 2,5
Language: English
INTERMEDIATE STATISTICS [Tutti i corsi di studio]
Credits: 2,5
Language: English
Statistical analysis with R - module II
Credits: 2
Language: Italian
Teacher: Erica Secchettin
Teaching Activities: Free choice
PROTECTING PSYCHOLOGICAL WELL-BEING IN THE PHD PROGRAM: WHAT DO WE NEED TO CONSIDER FOR BEING A GOOD SCIENTIST: BEST PRACTICE AND THE ETHICS OF SCIENCE
Credits: 1
Language: inglese
Teacher: Paola Cesari
QUANDO LA RICERCA SI FA ETICA (PERCORSO ORGANIZZATO E FINANZIATO DAL TEACHING AND LEARNING CENTER DI UNIVR)
Credits: 2
Language: Italian
Teacher: Roberta Silva
LA COMUNICAZIONE UMANISTICA: OPPORTUNITA' E RISCHI
Credits: 1
Language: Italiano
Italian Poetry abroad
Credits: 1
Language: Italiano
Teacher: Massimo Natale
BUSINESS MODEL CANVAS PILL
Credits: 1,5
Language: English
IMPARA IL MARKETING DIGITALE
Credits: 1,5
Language: English
APPROCCI E METODOLOGIE PARTECIPATIVE NELLA RICERCA CON GLI ATTORI DEL TERRITORIO
Credits: 1,5
Language: Italian
Teacher: Cristiana Zara
DOING INTERVIEWS IN QUALITATIVE RESEARCH
Credits: 1,5
Language: English
Teacher: Chiara Sità
DIFFERENTIAL DIAGNOSIS OF DEMYELINATING DISEASES OF THE CENTRAL NERVOUS SYSTEM
Credits: 2
Language: English
Teacher: Alberto Gajofatto
IL SONNO E I SUOI DISTURBI: FOCUS SULLE PARASONNIE E I DISTURBI DEL MOVIMENTO IN SONNO
Credits: 1
Language: English
Teacher: Elena Antelmi
IMAGING TECHNIQUES FOR BODY COMPOSITION ANALYSIS
Credits: 1
Language: English
Teacher: Carlo Zancanaro
OPEN SCIENCE: THE MIGHTY STICK AGAINST "BAD" SCIENCE
Credits: 2
Language: English
Teacher: Alberto Scandola
THE EMPIRICAL PHENOMENOLOGICAL METHOD (EPM): THEORETICAL FOUNDATION AND EMPIRICAL APPLICATION IN EDUCATIONAL AND HEALTHCARE FIELDS
Credits: 2
Language: English
THE PATHWAY OF OXYGEN: CAUSE OF HYPOXEMIA
Credits: 1
Language: English
Teacher: Carlo Capelli
PhD students
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Guidelines for PhD students
Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.
Documents
Title | Info File |
---|---|
Dottorandi: linee guida generali (2024/2025) | pdf, it, 104 KB, 29/10/24 |
PhD students: general guidelines (2024/2025) | pdf, en, 107 KB, 29/10/24 |