PhD in Modeling and Data Science

• Data Science seminars  

Laura Sacerdote (University of Turin, Italy)

Location: All lectures will be held in Palazzo Campana (Via Carlo Alberto 10, Torino), Sala Orsi

Timetable: from 16:30 to 19:00 on 16/1; 30/1; 20/2; 27/2; 5/3; 13/3; 20/3; 27/3; 9/4 

Contents: The course skeleton is made up of doctoral student seminars. The seminars of the doctoral students aim to increase and motivate the interdisciplinary approach by pushing the doctoral students to a broad vision of data science and to understand its different branches. In addition, the students are motivated to improve their skills in presenting their research topic. Each session includes two seminars of doctoral students while two other students ask them questions. The teacher, together with the students who ask the questions, stimulates the discussion, points out any lack of clarity and suggests further developments of the research. The seminar topics range from computer science to statistics or mathematics based on the research topic of the speaker. The course also aims to foster the emergence of interdisciplinary research collaborations among the doctoral students

• Time series analysis  

Elvira Di Nardo (University of Turin, Italy)

Luis Alberiko Gil-Alaña (University of Navarra, Spain) 

Location: Palazzo Campana (Via Carlo Alberto 10, Torino) and Corso Unione Sovietica. 

Timetable: 31/03, 7/04, 14/04, 16/04, 23/04, 28/04 (from 11.15), 6/05, 8/05, 13/05, 15/05, 20/05, 22/05, 28/05, 30/05 (from 11.15) for further information contact the teacher

Contents: With this course, the Phd student should be able to transform a real problem into a statistical one and interpret results in an effective way for phenomena evolving during the time.  Moreover it is expected that the student is able to employ mathematical/statistical models for a better identification of the dependence and for forecasting the behaviour of the stochastic dynamic system under observation. Syllabus: Strong and weak stationary time series, IID sequence. Time average and almost sure convergence for strong stationary t.s. Mean-ergodic property and stationary time series.  Estimation of ACF's functions: statistical properties .Ergodicity covariance property in L^2. Ljung-Box test. Transformations of data and difference operators.  Additive models: decomposition in trend and seasonal components and remainder term. Fitting the trend component. Global and local trend: linear trend, regression and approximation. Filtering: two sided moving average, asymmetric filter. Seasonal component: Periodogram. Analysis of the residuals. Wold's decomposition. Linear time series: time invariant linear filter. MA of order infinite. M(q) and AR(p) models. ADF test. Yule-Walker's equations for the covariance function of AR(p) models. ARMA models. Parameter redundancy. Identifiability of a model: causal and invertible representation of an ARMA model. Partial autocorrelation function. Estimation methods for the parameters of ARMA models. the AIC index. Forecasting. Validate the fitting.  

• Complementing big data with small data 

Stefano Ferraris (Politecnico di Torino, University of Turin, Italy)

Lorenza Fontana (Politecnico di Torino, University of Turin, Italy)

Location: tba

Timetable: tba (9 hours +  6 hours)

Contents: tba

• Spatial Analysis and Modeling

Rossano Schifanella (University of Turin, Italy)

Location: Computer Science Department, Via Pessinetto 12

Timetable: 4 classes of 4 hours between June-July 2025

Contents: This course explores spatial analysis and geospatial modeling techniques, emphasizing practical applications and the use of modern computational tools. Through theoretical foundations and hands-on practice, students will learn to gather, preprocess, and analyze spatial data while utilizing advanced modeling techniques to address real-world challenges in areas such as urban planning, mobility, and environmental monitoring.  The course is designed to equip students with the knowledge and skills needed to model spatial relationships, identify patterns, and predict outcomes using geospatial data. By the end of the course, students will be able to create, evaluate, and implement spatial models to inform decision-making processes. Course Objectives: Understand the fundamental principles of geospatial data structures and spatial relationships, Master techniques for preprocessing, visualizing, and analyzing spatial data, Develop advanced spatial models for predictive and exploratory purposes. Apply spatial modeling techniques to real-world scenarios, including urban planning and human mobility. Syllabus: Foundations of Spatial Analysis: Introduction to Spatial Analysis, Understanding spatial data and relationships. Theoretical principles of spatial analysis. Geospatial Data Fundamentals: Data structures in GIS and map projections. Gathering and preprocessing large-scale geospatial data. Handling spatial datasets with Python and open-source tools. Data Visualization Techniques: Choropleth mapping and other cartographic tools. Web-based mapping technologies and interactive visualizations. Exploratory Spatial Data Analysis (ESDA):  Identifying spatial autocorrelation and patterns. Using spatial weights and distance-based relationships. Case Studies in Visualization: Applications in urban planning, human mobility, and environmental monitoring. Predictive Spatial Models: Developing regression models for spatial data. Spatial clustering. Point patterns. Spatial Network Analysis: Modeling transportation and mobility networks. Measuring connectivity and accessibility within cities.
Hands-On Modeling: Applying advanced modeling tools (e.g., PySAL, GeoPandas) to real datasets.

• Deep learning: an introduction and some mathematical results

Elena Issoglio (University of Turin, Italy)

Location: Palazzo Campana (Via Carlo Alberto 10, Torino)

Timetable: 6h in the week starting on 26.05 (by E. Issoglio), 6h in the week starting on 02.06 (by E. Issoglio), 4h in the week starting on 09.06 (by guest lecturer Dr A. Ocello -- TBC), for further information contact the teacher

Contents: The course is divided into two main parts. Part I offers a (crash) introduction to machine learning, starting from some classical statistical techniques (regression, classification, optimization, etc) and then moving onto the basics of neural networks (some history, architectures, universal approximation theorems, backpropagation algorithm, stochastic gradient descend (SDG), etc). Part II will cover more advanced topics (independent of each other) which are currently topic of research by mathematicians and statisticians in the framework of the mathematical foundations of neural network training. Examples could include central limit theorem-like results for Gaussian neural networks and their explicit rate of convergence to Gaussian processes, score-based generative models and their links to stochastic differential equations, mean-field approximations of NN and link to SDG.

• Advanced Methods for Data Science

Luca Maria Aiello

Location: Palazzo Campana

Timetable: Meetings are scheduled at 9:30. Here the list of the days of lessons: February 20, 22, 23 (room 5), February 27 (room 2), February 29 (room Monod), March 1 (room S) 

• Extreme Value Theory

Amir Khorrami Chokami (University of Turin, Italy)

Location: TBA

Timetable: 8 hours - TBA

• Data Science seminars  

Laura Sacerdote (University of Turin, Italy)

Location: Rooms may change from lesson to lesson according with the room availability at Mathematics Department. Weekly, students receive an email with the information about the room and the link for those who are abroad.

Timetable: Meetings are scheduled at 16:30. Here the list of the days of lessons: January 18, February 8,11,22, March 7,14,21, April 11 (room Orsi, 4:30pm)

Contents: Each student presents his/her recent results. The teacher stimulates interdisciplinary discussions about the presented subject. Suggestions about alternative approaches are encouraged. Furthermore, participants are motivated to ask questions and suggest improvements to the presentation. The improvement of communication skills and the development of interdisciplinary interactions are important goals of this course, beside the learning of new topics.

• Time series analysis (a.a. 23/24)

Elvira Di Nardo (University of Turin, Italy)

Luis Alberiko Gil-Alaña (University of Navarra, Spain) 

Location: Palazzo Campana (PC), Corso Unione Sovietica (CUS)

Timetable:  Meetings are scheduled at 11.15 in March 26 (room 2, PC), April 5 (room 1, PC), April 9,16,23 (room 2, PC), May 7 (room 2, PC). Meetings are scheduled at 11:15 in May 14, 16, 21, 23, 28, 30 (room 12, CUS). Meetings are scheduled at 9:15 in May 17, 24 (room 12, CUS). 

Contents: Weak and strong stationarity. Estimation: elements of ergodic theory. Transformation of time series. ARMA models Forecasting. ARIMA and SARIMA models. Spectral representation of simple processes. Simulation and statistical analysis of time series with R. Diagnostic tools

• Spatial analysis and modeling  

Rossano Schifanella (University of Turin, Italy)

Location: TBA

Period: 16 hours - TBA

Contents

• Hydrological modeling and Data Analysis  

Stefano Ferraris (University of Turin, Italy)

Location: TBA

Period: 15 hours - TBA

• Elements of Stochastic Processes

Giuseppe D'Onofrio (University of Turin, Italy)

Location: TBA

Period: 18  hours - TBA

• Extreme Value Theory

Amir Khorrami Chokami (University of Turin, Italy)

Location: All lectures will be held in Palazzo Campana (Via Carlo Alberto 10, Torino)

Timetable (10:12am): January 12, 18 (room 3), January 26 (room Lagrange), February 2 (room Lagrange) 2023 

• Data Science seminars  

Laura Sacerdote (University of Turin, Italy)

Location: Rooms may change from lesson to lesson according with the room availability at Mathematics Department. Weekly, students receive an email with the information about the room and the link for those who are abroad.

Timetable: Meetings are scheduled on Thursday at 15:30.  Here the list of the days of lessons: February 16, 23 - March 9, 16, 10 - April 20 - May 4, 11, 18

Contents: Each student presents his/her recent results. The teacher stimulates interdisciplinary discussions about the presented subject. Suggestions about alternative approaches are encouraged. Furthermore, participants are motivated to ask questions and suggest improvements to the presentation. The improvement of communication skills and the development of interdisciplinary interactions are important goals of this course, beside the learning of new topics.

• Time series analysis  

Elvira Di Nardo (University of Turin, Italy)

Luis Alberiko Gil-Alaña (University of Navarra, Spain) 

Location: Corso Unione Sovietica

Timetable: from February 21 to May 24

Contents: Weak and strong stationarity. Estimation: elements of ergodic theory. Transformation of time series. ARMA models Forecasting. ARIMA and SARIMA models. Spectral representation of simple processes. Simulation and statistical analysis of time series with R. Diagnostic tools

• Predictive uncertainty in machine learning with conformal inference  

Matteo Sesia (University of South California, USA)

Location: Corso Unione Sovietica

Timetable: from May 8 to May 26

         Contents: Pdf file

• Soil water and related hydrological topics

Stefano Ferraris (University of Turin, Italy)

Location: Castello Valentino

Period: September 2022

Contents: What measured data we have and at what scale? Water balance with end members mixing and splitting. Catchment scale: residence and transit times, Storage Selection Functions (Tutorial 2: Matlab TranSAS code). Flow modelling in porous media: Darcy and Richards equation (and application of the latter in the Community Land Model) (Tutorial 3a: HYDRUS code). Transport modelling in porous media: Convection - Dispersion model (Tutorial 3b: HYDRUS code) Continuous Time Random Walk model (Tutorial 4: Matlab CTRW code)

• Data Science seminars  

Laura Sacerdote (University of Turin, Italy)

Location: Palazzo Campana, Torino

Period (2022) 

• Time series analysis (12+16 hours)

Elvira Di Nardo (University of Turin, Italy)

Luis Alberiko Gil-Alaña (University of Navarra, Spain) 

Location: Corso Unione Sovietica, Torino

Period: March-April-May 2022 

Contents: Weak and strong stationarity. Estimation: elements of ergodic theory. Transformation of time series. ARMA models Forecasting. ARIMA and SARIMA models. Spectral representation of simple processes. Simulation and statistical analysis of time series with R. Diagnostic tools

• Topics in high-dimensional statistics

Larry Goldstein (University of South California, USA)

Location: Corso Unione Sovietica, Torino

Period: May 2022

Contents: Basic tail and concentration bounds, Random matrices and covariance estimation, Sparse linear regression in high dimension, Reproducing kernel Hilbert spaces, Minimax lower bounds

• Spatial analysis and modeling  

Rossano Schifanella (University of Turin, Italy)

Location: Room B, Computer Science Department (UniTo)

Period (2021): May

Contents

• Time series analysis (12+16 hours)

Elvira Di Nardo (University of Turin, Italy)

Luis Alberiko Gil-Alaña (University of Navarra, Spain) 

Period(2021): April-May 2021 

Contents: Weak and strong stationarity. Estimation: elements of ergodic theory. Transformation of time series. ARMA models Forecasting. ARIMA and SARIMA models. Spectral representation of simple processes. Simulation and statistical analysis of time series with R. Diagnostic tools

• Introduction to large deviations and random graphs (8 hours)

Dr. Luisa Andreis  (Weierstrass Institute of Berlin, Germany)

Period (2021): January 14, 18, 22, 25 at 4:30 pm.

Contents:  Introduction to large deviation theory, rare events and large deviation principle, Dense random graphs and Graphons, Sparse random Graphs, Mathematical analysis of the models treated if the course and connection between random graphs and coagulation processes

• Privacy and Data Protection  

Ruggero Gaetano Pensa (University of Turin, Italy)

Period (2020): November 17, 18, 24, 25  at 9:00 (until 1:00pm), December 1 at 9:00am (until 1:00pm)

Contents

• Topics in Information theory  

Yosef Rinott (Hebrew University of Jerusalem, Israel) 

Re-Scheduled (due to COVID-19): tba 

• Advances in time series analysis  

Luis Alberiko Gil-Alaña (University of Navarra, Spain) 

Re-Scheduled (due to COVID-19) 

• Ideas and Tools for data Scientists  

Schedule:

• 14:30 (March 1, Room Lignea, Collegio Carlo Alberto)   
  Prof. Marco Aldinucci (Dept of Computer Science, Torino University) 
• 14:30 (March 8, Room 3, Collegio Carlo Alberto)             
  Prof. Marco Aldinucci 
• 15:00 (March 15, Room 4 at Math. Dept)                             
  Prof. Matteo Semplice  (Dept of Math, Torino University)
• 14:30 (May 10, Room 4 at Math. Dept)                                   
  Prof. Matteo Semplice

These lessons are mandatory for Ph D students of Modeling and Data Science program.