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Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - prof. Brambilla Marco

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Marco Brambilla
Marco Brambilla

The document outlines various thesis topics proposed by Marco Brambilla in areas such as explainable AI, data science, gamification, and using ChatGPT for software engineering tasks. Specific proposals include using gamification for data collection and evaluation of AI explanations, applying data science to societal challenges, and exploring the use of ChatGPT for generating UML and BPMN models.

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SCUOLA DI INGEGNERIA INDUSTRIALE E DELL’INFORMAZIONE Computer Science and Engineering Programme THESIS TOPICS AND PROPOSALS Marco Brambilla marco.brambilla@polimi.it
2 Generalities 1. General topics and macro-areas 2. Any topic open for works at the level of thesis or tesina. Tentative type agreed upon at the beginning, actual type assessed towards the end 3. Suggested timing for thesis: last semester of studies, possibly in parallel with (very few) exams 4. Duration: in the range of 6 months, with a big dependency on effort and results
POLITECNICO DI MILANO Explainable AI
4 Explainable AI: context The final aim of the Explainable Artificial Intelligence (XAI) research field can be summarised as “Developing inherently explainable systems and explainability techniques that faithfully explicit the behaviour of complex machine learning models tailoring their explanation in an understandable way for humans.”
5 Global Explanations of Image Classification Tasks by Means of Local Explanations Thesis by Antonio De Santis and Matteo Bianchi
6 Gamified Data Collection for NLP Explainability Tasks Development of a gamified platform to collect structured human knowledge for multiple, different Natural Language Processing tasks. NLP Task Selection Gamified Activity Task #1 Gamified Activity Task #N Data Structuring Data Storing
7 Global Explanations of NLP Tasks by Means of Local Explanations Development of a pipeline to collect human knowledge to explain the behaviour of a neural network performing NLP tasks (e.g., sentiment analysis). Input Layer Layer 1 … Layer N Output Layer Gamified Explainability Task Human Interpreter Layer-wise Explanation Global Explanation
8 Gamified Approaches to Evaluate the Understandability of Explanations Development of gamified approaches to evaluate the understandability of models’ explanations produced by explainability approaches through crowdsourcing. Human Interpreter Gamified Understandability Task Understandability Comparison Explanations from Explainability Algorithms
POLITECNICO DI MILANO Data Science
10 Data science applications ► Combining big data, data engineering, and data science (ML, dataviz, NLP) for: – User profiling – Preferences – Behaviour SCENARIOS: ► Societal challenges: – discrimination (race, gender, economics) in sports, workplace, study, – immigration, criminality ► Socio-political settings ► Economy, ethics ► News and disinformation ► Some examples follow
11 US Midterm ► Antonio Lopardo
12 Brexit ► Emre Calisir, now @ MIT Media Lab 12
13 Brexit
14 News and News Sharing ► Understanding how and when people share pieces of news on social network ► Profiling users against possible risks (fake news, superficial behaviour)
Geographic Distribution of sources
Not only digital …
18 Approach City-scale: mobile telephone and (gross-grain geo-located) social media data Street/square: people counting & profiling IoT sensors Point of Interest: people counting sensor, WiFi log analysis, beacons and (fine grain geo- located) social media Descriptive, predictive, privacy-preserving and, when needed, real-time analysis of a variety of (fused) data sources
Dashboards… People counting and profiling via Mobile Data 24.512 People present 41% 71% 63% 59% tourists citizens 29% female male 37% private business 10 20 30 40 50 60 70 <<more?>> age More people than usual
Measuring People counting via 3D camera
Inference People tracking via WiFi and 2D camera 9 6 1 2 5 8 3 4 7 Outside traffic Visit duration Trajectories
Integration Personalized information/offers, city loyalty cards, digital coupons, and polling Proximity detection via NFC or BLE/Beacons
Dashboards.. Why people is there CrowdInsights
Dashboards.. Why people is there CrowdInsights
7 1 6 2 3 4 5 7 Areas 1. Città murata 2. Lago sponda Viale Geno 3. Lago 4. Lago sponda di Villa Olmo 5. Zona industriale 6. Brunate 7. Business e università Phone data
August
Saturday and Sunday
Where do the foreign visitors come from? Provenance • Insight: Switzerland, Germany, Netherlands, Spain and United Kingdom are the top origins • Insight: Outside Europe, there is a relevant evidence from Brazil, Japan, Korea Republic and US
Which locations do people visit from where? Statistics about nationality
Event location per cluster «Food & Drinks» «Art»
31 User Cluster Analysis • Apply clustering algorithms over Topic Probabilities Matrix to cluster users • Multiple data slices • Multiple algorithms o K-means o Hierarchical o DBSCAN Topic 1 Topic 3 Topic 2
32 Travel Lovers Art Lovers Internet & Tech Lovers Users’ Biography Word Clouds Cluster Labeling
33 Collaboration networks ► Software development ► Cross-project collaborations ► Networking ► Rich club problem
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874724 Studying the exposome for a healthier future for all children ● Exposome is defined as all factors that can affect the quality of human health ● The Equal-Life project comprises 22 partners from 11 European countries. ● Specialists dealing with children's health, who come from different fields (scientific, educational, social), were involved outside the project ● One of the methods by which they have been asked to participate is through targeted interviews. ● The problem of this new field (the exposome) lies mainly in the definition of the terms used to define the factors, which are often inconsistent between different disciplines and which do not allow an objective classification of the answers. ● The purpose of this thesis is to find a method that is able to classify the definitions provided by the domain experts ● expand the definition where possible ● extract the categories obtained from the answers in the interviews. www.Equal-Life.eu https://www.humanexposome.eu/
POLITECNICO DI MILANO Gamification
36 Exploration and sharing of perceptions for COVID-19 ►Explore Visions ►New Vision Shortcut ►Challenge Shortcut ►Filter Visions
37
38
POLITECNICO DI MILANO ChatGPT for Sw. Eng. and Modeling
40 Exploration of ChatGPT use for software design ► Analyze the use, configuration, and features of ChatGPT ► Understand the strength and weaknesses when generating answers to software questions ► Implement and analyze ChatGPT as generator of UML / IFML or BPMN models
41 references ► https://marco-brambilla.com/blog/ ► Big data and data science ► https://marco-brambilla.com/2022/11/04/exploring-the-bi-verse-a-trip-across-the- digital-and-physical-ecospheres/ ► Explainability ► https://marco-brambilla.com/2022/07/11/the-role-of-human-knowledge-in- explainable-ai/ ► https://marco-brambilla.com/2022/06/01/exp-crowd-gamified-crowdsourcing-for-ai- explainability/
THESIS TOPICS AND PROPOSALS Marco Brambilla Data Science Lab marco.brambilla@polimi.it

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Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - prof. Brambilla Marco

  • 1. SCUOLA DI INGEGNERIA INDUSTRIALE E DELL’INFORMAZIONE Computer Science and Engineering Programme THESIS TOPICS AND PROPOSALS Marco Brambilla marco.brambilla@polimi.it
  • 2. 2 Generalities 1. General topics and macro-areas 2. Any topic open for works at the level of thesis or tesina. Tentative type agreed upon at the beginning, actual type assessed towards the end 3. Suggested timing for thesis: last semester of studies, possibly in parallel with (very few) exams 4. Duration: in the range of 6 months, with a big dependency on effort and results
  • 4. 4 Explainable AI: context The final aim of the Explainable Artificial Intelligence (XAI) research field can be summarised as “Developing inherently explainable systems and explainability techniques that faithfully explicit the behaviour of complex machine learning models tailoring their explanation in an understandable way for humans.”
  • 5. 5 Global Explanations of Image Classification Tasks by Means of Local Explanations Thesis by Antonio De Santis and Matteo Bianchi
  • 6. 6 Gamified Data Collection for NLP Explainability Tasks Development of a gamified platform to collect structured human knowledge for multiple, different Natural Language Processing tasks. NLP Task Selection Gamified Activity Task #1 Gamified Activity Task #N Data Structuring Data Storing
  • 7. 7 Global Explanations of NLP Tasks by Means of Local Explanations Development of a pipeline to collect human knowledge to explain the behaviour of a neural network performing NLP tasks (e.g., sentiment analysis). Input Layer Layer 1 … Layer N Output Layer Gamified Explainability Task Human Interpreter Layer-wise Explanation Global Explanation
  • 8. 8 Gamified Approaches to Evaluate the Understandability of Explanations Development of gamified approaches to evaluate the understandability of models’ explanations produced by explainability approaches through crowdsourcing. Human Interpreter Gamified Understandability Task Understandability Comparison Explanations from Explainability Algorithms
  • 10. 10 Data science applications ► Combining big data, data engineering, and data science (ML, dataviz, NLP) for: – User profiling – Preferences – Behaviour SCENARIOS: ► Societal challenges: – discrimination (race, gender, economics) in sports, workplace, study, – immigration, criminality ► Socio-political settings ► Economy, ethics ► News and disinformation ► Some examples follow
  • 12. 12 Brexit ► Emre Calisir, now @ MIT Media Lab 12
  • 14. 14 News and News Sharing ► Understanding how and when people share pieces of news on social network ► Profiling users against possible risks (fake news, superficial behaviour)
  • 17.
  • 18. 18 Approach City-scale: mobile telephone and (gross-grain geo-located) social media data Street/square: people counting & profiling IoT sensors Point of Interest: people counting sensor, WiFi log analysis, beacons and (fine grain geo- located) social media Descriptive, predictive, privacy-preserving and, when needed, real-time analysis of a variety of (fused) data sources
  • 19. Dashboards… People counting and profiling via Mobile Data 24.512 People present 41% 71% 63% 59% tourists citizens 29% female male 37% private business 10 20 30 40 50 60 70 <<more?>> age More people than usual
  • 21. Inference People tracking via WiFi and 2D camera 9 6 1 2 5 8 3 4 7 Outside traffic Visit duration Trajectories
  • 22. Integration Personalized information/offers, city loyalty cards, digital coupons, and polling Proximity detection via NFC or BLE/Beacons
  • 23. Dashboards.. Why people is there CrowdInsights
  • 24. Dashboards.. Why people is there CrowdInsights
  • 25. 7 1 6 2 3 4 5 7 Areas 1. Città murata 2. Lago sponda Viale Geno 3. Lago 4. Lago sponda di Villa Olmo 5. Zona industriale 6. Brunate 7. Business e università Phone data
  • 28. Where do the foreign visitors come from? Provenance • Insight: Switzerland, Germany, Netherlands, Spain and United Kingdom are the top origins • Insight: Outside Europe, there is a relevant evidence from Brazil, Japan, Korea Republic and US
  • 29. Which locations do people visit from where? Statistics about nationality
  • 30. Event location per cluster «Food & Drinks» «Art»
  • 31. 31 User Cluster Analysis • Apply clustering algorithms over Topic Probabilities Matrix to cluster users • Multiple data slices • Multiple algorithms o K-means o Hierarchical o DBSCAN Topic 1 Topic 3 Topic 2
  • 32. 32 Travel Lovers Art Lovers Internet & Tech Lovers Users’ Biography Word Clouds Cluster Labeling
  • 33. 33 Collaboration networks ► Software development ► Cross-project collaborations ► Networking ► Rich club problem
  • 34. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874724 Studying the exposome for a healthier future for all children ● Exposome is defined as all factors that can affect the quality of human health ● The Equal-Life project comprises 22 partners from 11 European countries. ● Specialists dealing with children's health, who come from different fields (scientific, educational, social), were involved outside the project ● One of the methods by which they have been asked to participate is through targeted interviews. ● The problem of this new field (the exposome) lies mainly in the definition of the terms used to define the factors, which are often inconsistent between different disciplines and which do not allow an objective classification of the answers. ● The purpose of this thesis is to find a method that is able to classify the definitions provided by the domain experts ● expand the definition where possible ● extract the categories obtained from the answers in the interviews. www.Equal-Life.eu https://www.humanexposome.eu/
  • 36. 36 Exploration and sharing of perceptions for COVID-19 ►Explore Visions ►New Vision Shortcut ►Challenge Shortcut ►Filter Visions
  • 37. 37
  • 38. 38
  • 39. POLITECNICO DI MILANO ChatGPT for Sw. Eng. and Modeling
  • 40. 40 Exploration of ChatGPT use for software design ► Analyze the use, configuration, and features of ChatGPT ► Understand the strength and weaknesses when generating answers to software questions ► Implement and analyze ChatGPT as generator of UML / IFML or BPMN models
  • 41. 41 references ► https://marco-brambilla.com/blog/ ► Big data and data science ► https://marco-brambilla.com/2022/11/04/exploring-the-bi-verse-a-trip-across-the- digital-and-physical-ecospheres/ ► Explainability ► https://marco-brambilla.com/2022/07/11/the-role-of-human-knowledge-in- explainable-ai/ ► https://marco-brambilla.com/2022/06/01/exp-crowd-gamified-crowdsourcing-for-ai- explainability/
  • 42. THESIS TOPICS AND PROPOSALS Marco Brambilla Data Science Lab marco.brambilla@polimi.it