physycom · AldoCanfora · Jun 26, 2025 · Jun 5, 2025 · Jun 5, 2025 · Jun 5, 2025
diff --git a/_data/projects.yaml b/_data/projects.yaml
@@ -16,6 +16,15 @@
   tags:
     - resource
 
+- title: TALEA
+  subtitle: (PN Metro Plus e Città Medie Sud 2021-2027)
+  group: highlighted
+  image: images/talea.png
+  link: https://www.fondazioneinnovazioneurbana.it/45-uncategorised/3454-il-progetto-talea-bologna-sperimenta-i-rifugi-climatici
+  description: TALEA is an urban climate adaptation project in Bologna that develops “Green Cells” — modular nature-based solutions — to reduce urban heat islands, enhance green infrastructure, and foster citizen participation.
+  tags:
+    - resource
+
 
 - title: S.LI.DES.
   subtitle: (IT-HR 2021-2027)

diff --git a/_members/aldo-canfora.md b/_members/aldo-canfora.md
@@ -2,12 +2,15 @@
 name: Aldo Canfora
 image: https://github.com/AldoCanfora.png
 role: phd
+order: 4
 affiliation: University of Bologna - CINECA
 links:
   home-page: https://www.unibo.it/sitoweb/aldo.canfora2/en
   github: AldoCanfora
   email: [email protected]
 ---
 
+<p style="font-family: 'Georgia', serif; font-size: 18px;">
 PhD student in Applied Physics and Complex Systems. 
 I am working on the Digital Twin of Bologna, focusing on Urban Building Energy Modeling (UBEM), Urban Heat Island (UHI) analysis and Urban Vegetation Segmentation. I specialize in geometric data analysis, leveraging GIS, computational modeling, and machine learning to optimize energy efficiency and environmental impact at the city level.
+</p>
diff --git a/_members/armando-bazzani.md b/_members/armando-bazzani.md
@@ -2,6 +2,7 @@
 name: Armando Bazzani
 image: images/armando_bazzani.png
 role: associate-professor
+order: 1
 affiliation: University of Bologna - INFN Bologna
 aliases:
   - A. Bazzani

diff --git a/_members/daniele-pucci.md b/_members/daniele-pucci.md
@@ -2,11 +2,11 @@
 name: Daniele Pucci
 image: https://github.com/puccj.png
 role: phd
+order: 7
 affiliation: University of Bologna
 links:
   home-page: https://www.unibo.it/sitoweb/daniele.pucci4/en
   github: puccj
   email: [email protected]
 ---
 
-PhD student
diff --git a/_members/dragosdumitru-ioan.md b/_members/dragosdumitru-ioan.md
@@ -2,10 +2,10 @@
 name: Dragos Dumitru Ioan
 image: images/dragos_ioan.png
 role: researcher
+order: 8
 affiliation: University of Bologna - INFN Bologna
 links:
   home-page: https://www.unibo.it/sitoweb/dragosdumitru.ioan/en
   email: [email protected]
 ---
 
-Research Fellow
diff --git a/_members/filippo-dalla.md b/_members/filippo-dalla.md
@@ -2,11 +2,14 @@
 name: Filippo Dalla
 image: images/filippo_dalla.png
 role: phd
+order: 2
 affiliation: University of Bologna - INFN Bologna
 links:
   home-page: https://www.unibo.it/sitoweb/filippo.dalla3/en
   github: filippodll
   email: [email protected]
 ---
 
+<p style="font-family: 'Georgia', serif; font-size: 18px;">
 PhD student in Applied Physics and Complex Systems
+</p>
diff --git a/_members/giulio-colombini.md b/_members/giulio-colombini.md
@@ -1,12 +1,11 @@
 ---
 name: Giulio Colombini
 image: https://github.com/GColom.png
-role: researcher
+role: postdoc
+order: 1
 affiliation: University of Bologna - INFN Bologna
 links:
   home-page: https://www.unibo.it/sitoweb/giulio.colombini2/en
   github: GColom
   email: [email protected]
 ---
-
-Research Fellow
diff --git a/_members/gregorio-berselli.md b/_members/gregorio-berselli.md
@@ -2,11 +2,14 @@
 name: Gregorio Berselli
 image: https://github.com/Grufoony.png
 role: phd
+order: 5
 affiliation: University of Bologna - INFN Bologna
 links:
   home-page: https://www.unibo.it/sitoweb/gregorio.berselli2/en
   github: Grufoony
   email: [email protected]
 ---
 
+<p style="font-family: 'Georgia', serif; font-size: 18px;">
 PhD student in Applied Physics and Complex Systems
+</p>
diff --git a/_members/lorenzo-dimeco.md b/_members/lorenzo-dimeco.md
@@ -1,11 +1,12 @@
 ---
-name: Lorenzo di Meco
+name: Lorenzo Di Meco
 image: https://github.com/lorenzodimeco3.png
 role: phd
+order: 3
 affiliation: University of Bologna - INFN Bologna
 links:
   home-page: https://www.unibo.it/sitoweb/lorenzo.dimeco3/en
   email: [email protected]
 ---
 
-PhD student
+
diff --git a/_members/matteo-falcioni.md b/_members/matteo-falcioni.md
@@ -2,10 +2,13 @@
 name: Matteo Falcioni
 image: images/matteo_falcioni.png
 role: researcher
+order: 9
 affiliation: University of Bologna
 links:
   github: MatteoFalcioni
   email: [email protected]
 ---
 
+<p style="font-family: 'Georgia', serif; font-size: 18px;">
 I am Matteo Falcioni, a research fellow at the University of Bologna. I deal with artificial intelligence and machine learning applied to geospatial data analysis, with a focus on the use of deep learning models to support urban applications. My interests include neural networks for spatial data classification and generative models for the simulation and optimization of urban scenarios.
+</p>
diff --git a/_members/mirko-degliesposti.md b/_members/mirko-degliesposti.md
@@ -2,6 +2,7 @@
 name: Mirko degli Esposti
 image: images/mirko_degkliesposti.png
 role: full-professor
+order: 2
 affiliation: University of Bologna
 links:
   home-page: https://www.unibo.it/sitoweb/mirko.degliesposti/en

diff --git a/_members/tommaso-rondini.md b/_members/tommaso-rondini.md
@@ -2,11 +2,12 @@
 name: Tommaso Rondini
 image: images/tommaso_rondini.png
 role: phd
+order: 6
 affiliation: University of Bologna - INFN Bologna
 links:
   home-page: https://www.unibo.it/sitoweb/tommaso.rondini2/en
   github: AlphaKelpie
   email: [email protected]
 ---
 
-PhD student
+
diff --git a/contact/index.md b/contact/index.md
@@ -7,7 +7,9 @@ nav:
 
 # {% include icon.html icon="fa-regular fa-envelope" %}Contact
 
+<p style="font-family: 'Georgia', serif; font-size: 18px;">
 Contact us 
+</p>
 
 {%
   include button.html

diff --git a/images/talea.png b/images/talea.png
diff --git a/index.md b/index.md
@@ -1,16 +1,12 @@
 ---
 ---
 
-# City Science Laboratory's Website
-
-The City Science Laboratory is a multidisciplinary research group within the Department of Physics and Astronomy at the University of Bologna.
-The Laboratory leverages expertise in physics, mathematics, and computer science to explore and understand the complexity of urban systems.
-With a focus on mobility, climate, and energy, the Laboratory aims to contribute to the development of sustainable and resilient cities by advancing scientific methodologies and computational tools.
-The Laboratory collaborates closely with CINECA, one of the largest high-performance computing centers in Europe, on several urban Digital Twin initiatives.
-A notable example is the Digital Twin of the City of Bologna, which integrates diverse datasets and advanced modeling techniques to provide actionable insights for urban planning, climate adaptation, and energy efficiency.
-The Laboratory offers the expertise in the fields, Urban LiDAR and Orthophoto Analysis, Infrastructure Morphology and Indicators, Vegetation and Climate Vulnerability Indices, Building Energy Simulations, Urban Microclimate Simulations, Vehicular Mobility Modeling, Pedestrian Dynamics and Crowd Analysis.
-The City Science Laboratory combines advanced computational methods with real-world applications, fostering collaborations with
-stakeholders to address critical urban challenges and promote evidence-based decision-making.
+# City Science Laboratory
+
+The City Science Laboratory is a multidisciplinary research group within the Department of Physics and Astronomy at the University of Bologna. The Laboratory brings together expertise in physics, mathematics, and computer science to investigate the complexity of urban systems. With a focus on mobility, climate, and energy, it aims to advance sustainable and resilient cities through scientific methodologies, computational tools, and the application of artificial intelligence and deep learning algorithms, which are increasingly transforming the analysis and management of urban environments.
+The Laboratory collaborates with CINECA, one of Europe’s leading high- performance computing centers, on several urban Digital Twin initiatives. Among these, the Digital Twin of the City of Bologna integrates heterogeneous datasets and advanced modeling techniques to generate insights for urban planning, climate adaptation, and energy efficiency.
+Key research areas include the analysis of Urban LiDAR, Orthophotos, Satellite Data, and other Remote Sensing sources; the study of Infrastructure Morphology and Indicators; the development of Vegetation and Climate Vulnerability Indices; Building Energy Simulations; Urban Microclimate Modeling; Vehicular Mobility Modeling; and Pedestrian Dynamics and Crowd Analysis.
+By combining advanced computational methods with real-world applications, the City Science Laboratory fosters collaboration with public and private stakeholders to address critical urban challenges and promote evidence-based decision-making.
 
 {% include section.html %}
 

diff --git a/projects/index.md b/projects/index.md
@@ -2,12 +2,14 @@
 title: Projects
 nav:
   order: 3
-  tooltip: Software, datasets, and more
+  tooltip: Projects in which we work and posters
 ---
 
 # {% include icon.html icon="fa-solid fa-wrench" %}Projects
 
+<p style="font-family: 'Georgia', serif; font-size: 18px;">
 The City Science Laboratory engages in research projects that enhance urban sustainability, resilience, and data-driven decision-making. We collaborate across disciplines to develop innovative solutions in mobility, climate adaptation, and cultural heritage preservation. In particular, the projects focus on smart strategies for sustainable tourism, an observatory for early warning and risk assessment of emerging infectious diseases, and the developing of nature-based solutions to mitigate hydro-meteorological risks. Through these initiatives, we contribute to advancing scientific methodologies and computational tools for urban and environmental challenges. 
+</p>
 
 {% include tags.html tags="publication, resource, website" %}
 

diff --git a/publications/index.md b/publications/index.md
@@ -1,7 +1,7 @@
 ---
 title: Publications
 nav:
-  order: 1
+  order: 3
   tooltip: Published works
 ---
 
@@ -32,6 +32,8 @@ nav:
 
 {% include citation.html lookup="A stochastic model of randomly accelerated walkers for human mobility" style="rich" %}
 
+{% include citation.html lookup="Congestion Transition on Random Walks on Graphs" style="rich" %}
+
 
 {% include section.html %}
 

diff --git a/research/index.md b/research/index.md
@@ -1,32 +1,45 @@
 ---
 title: Research
 nav:
-  order: 2
+  order: 1
   tooltip: research topics
 ---
-<h1 style="font-size: 3em;">Research Activities at the City Science Lab</h1>
+<h1 style="font-family: 'Georgia', serif; font-size: 40px;">Research Areas</h1>
 
-<h1 style="font-size: 1.5em;">Mobility</h1>
 
-- Network theory and random walks<br>
-We study the dynamics of complex networks using random walk models to explore node connectivity, community structure, and diffusion processes. Applications include urban infrastructure, mobility systems, and energy distribution.
+<h2 style="color: blue; text-align: left;">Mathematical Foundations of Urban Complexity</h2>
 
-- Urban vehicular mobility modeling<br>
-We develop large-scale simulations of traffic flow across various cities, e.g. Bologna, to forecast congestion patterns and optimize traffic lights and routing strategies. The model integrates real traffic data from heterogeneous sources and adaptive control mechanisms to improve urban mobility efficiency.
+We explore cities as complex systems, integrating theoretical and computational perspectives from mathematical physics. Our work investigates the multiscale spatial-temporal dynamics of urban processes, the physical underpinnings of digital twins, and the structural principles of urban phenomena. Using tools from multilayer network theory, dynamical systems, statistical mechanics, and both data-driven and analytical modeling, we aim to build a rigorous foundation for understanding and simulating urban complexity.
 
-- Railway delay prediction using Italian train data<br>
-We analyze national train schedules and historical delay records to build a simple model able to replicate the empirical distribution of delays. This model can be used to predict future delays and improve the reliability of the railway system.
 
-<h1 style="font-size: 1.5em;">Urban energy and vegetation</h1>
+<h2 style="color: blue; text-align: left;">Mobility and Network Dynamics</h2>
 
-- Building energy simulation<br>
-We simulate energy consumption for the buildings in Bologna based on their geometry and age. The output is used to map the city’s most energy-demanding and polluting zones, supporting decarbonization strategies and policymaking.
+<h3 style="color: black; text-align: left;">Network Theory and Random Walks</h3>
+We investigate the dynamics of complex networks through random walk models to analyze node centrality, community structure, and diffusion processes. Applications include modeling of urban infrastructure, mobility systems, and energy networks.
 
-- Urban vegetation analysis<br>
-We extract information on urban trees using airborne LiDAR, including segmentation of individual trees and updates of tree features such as height and crown radius. This helps maintain accurate vegetation inventories and assess urban green coverage.
+<h3 style="color: black; text-align: left;">Urban Vehicular Mobility Modeling</h3>
+We develop large-scale simulations of vehicular traffic in cities such as Bologna to predict congestion patterns, optimize routing, and improve traffic light coordination. Our models integrate real-time data from multiple sources and use adaptive mechanisms for control and forecasting.
 
-- Urban Heat Island (UHI) analysis<br>
-We study the urban heat island effect using spatial variables like Sky View Factor (SVF). The aim is to understand microclimate variation across urban zones and identify heat-vulnerable areas for climate adaptation planning.
+<h3 style="color: black; text-align: left;">Railway Delay Prediction from Italian Train Data</h3>
+Using national railway data, we construct models to reproduce and predict train delay distributions. The aim is to understand systemic inefficiencies and provide data-driven insights for enhancing network reliability and punctuality.
 
-<h1 style="font-size: 1.5em;">GIULIO, PUCCI, DRAGOS AGGIUNGETE LA VOSTRA SEZIONE !!!</h1>
-[nota importante](https://www.youtube.com/watch?v=SXHMnicI6Pg)
+
+<h2 style="color: blue; text-align: left;">Urban Energy and Vegetation</h2>
+
+<h3 style="color: black; text-align: left;">Building Energy Simulation</h3>
+We simulate the energy performance of buildings based on their morphology, construction period, and usage. This enables the identification of energy- intensive areas and supports urban decarbonization strategies through informed policy recommendations.
+
+<h3 style="color: black; text-align: left;">Urban Vegetation Analysis</h3>
+Using airborne LiDAR, orthophotos, satellite imagery, and remote sensing, we detect and classify urban trees, estimate structural attributes like height and crown width, and monitor vegetation changes over time. These analyses support green infrastructure planning and biodiversity management.
+
+<h3 style="color: black; text-align: left;">Urban Heat Island (UHI) Analysis</h3>
+We study the UHI effect by integrating spatial indicators such as Sky View Factor (SVF), land cover, and built morphology. The goal is to assess microclimate variability and identify zones most vulnerable to heat, enabling targeted climate adaptation measures.
+
+
+<h2 style="color: blue; text-align: left;">AI and Urban Modeling</h2>
+
+<h3 style="color: black; text-align: left;">Urban Language Models and Vision-Aware Agent</h3>
+We explore how large language models (LLMs) can be trained or fine-tuned to understand spatio-temporal urban processes and respond to complex urban queries. Our research investigates generalization capabilities of LLMs, their integration with vision transformers, and their role as autonomous agents navigating urban data.
+
+<h3 style="color: black; text-align: left;">Physics-Informed Neural Networks (PINNs) for Urban Modeling</h3>
+To ensure that AI models reflect underlying physical laws, we use physics- informed neural networks that embed dynamical constraints into learning architectures. These models allow for more accurate and interpretable simulations of urban processes, from energy flow to climate dynamics.
diff --git a/team/index.md b/team/index.md
@@ -7,11 +7,41 @@ nav:
 
 # {% include icon.html icon="fa-solid fa-users" %}Team
 
+<p style="font-family: 'Georgia', serif; font-size: 18px;">
 The City Science Laboratory brings together a multidisciplinary team of researchers with expertise in physics, mathematics, and computer science. United by a common goal of understanding urban complexity, we develop innovative methodologies and computational tools to address challenges in mobility, climate resilience, and energy efficiency.
+</p>
 
 {% include section.html %}
 
-{% include list.html data="members" component="portrait" filter="role == 'pi'" %}
-{% include list.html data="members" component="portrait" filter="role != 'pi'" %}
+{% assign professors = site.members | where_exp: "m", "m.role contains 'professor'" | sort: "order" %}
+
+{% for member in professors %}
+  {% include portrait.html 
+    name=member.name
+    image=member.image
+    role=member.role
+    affiliation=member.affiliation
+    links=member.links
+    order=member.order
+    description=member.description
+    slug=member.slug
+  %}
+{% endfor %}
+
+{% assign others = site.members | sort: "order" %}
+{% for member in others %}
+  {% unless member.role contains 'professor' %}
+    {% include portrait.html 
+      name=member.name
+      image=member.image
+      role=member.role
+      affiliation=member.affiliation
+      links=member.links
+      order=member.order
+      description=member.description
+      slug=member.slug
+    %}
+  {% endunless %}
+{% endfor %}