Schedule
AstroStat School · Rome · 27–31 July 2026
Monday27 July
Tuesday28 July
Wednesday29 July
Thursday30 July
Friday31 July
Morning9:30 – 11:00
Classical StatisticsKonstantinos Kovlakas
Bayesian IGiorgos Vernardos
Gaussian ProcessesPaolo Bonfini
ML Best PracticesGrigoris Maravelias
SBIGiorgos Vernardos
Coffee break · 15 min
Morning11:15 – 12:45
Hypothesis TestingPaolo Bonfini
Bayesian IIGiorgos Vernardos
Time SeriesNikolas Vasilas
DL Intro + RegressionGrigoris Maravelias
Diffusion ModelsGrigoris Maravelias
Lunch break · 1 h 30 min
Afternoon14:15 – 15:45
Maximum LikelihoodKonstantinos Kovlakas
MCMCKonstantinos Kovlakas
Networking
Erika Korb
Konstantinos Kovlakas
Paolo Bonfinicoordination: Giorgos Vernardos
Konstantinos Kovlakas
Paolo Bonfinicoordination: Giorgos Vernardos
In this session, our lecturers will share real-life professional experiences outside academia. Students will have the opportunity to learn about expectations in the broader job market, how to apply their data science skills in different professional contexts, which requirements are commonly expected, and how to improve their chances of application success.
Attendance to this session is optional.
GPUGiorgos Vernardos
Intro to AgentsPaolo Bonfini
Coffee break · 15 min
Coffee break · 15 min
Afternoon16:00 – 17:30
ML Intro + ClusteringNikolas Vasilas
ML ClassificationNikolas Vasilas
NN ExplainabilityArnab Lahiry
Closing Remarks
Evening
Social Dinner
Details will be communicated.
Monday · 27 July
Morning9:30–11:00
Classical StatisticsKonstantinos Kovlakas
Morning11:15–12:45
Hypothesis TestingPaolo Bonfini
Afternoon14:15–15:45
Maximum LikelihoodKonstantinos Kovlakas
Afternoon16:00–17:30
ML Intro + ClusteringNikolas Vasilas
Tuesday · 28 July
Morning9:30–11:00
Bayesian IGiorgos Vernardos
Morning11:15–12:45
Bayesian IIGiorgos Vernardos
Afternoon14:15–15:45
MCMCKonstantinos Kovlakas
Afternoon16:00–17:30
ML ClassificationNikolas Vasilas
Wednesday · 29 July
Morning9:30–11:00
Gaussian ProcessesPaolo Bonfini
Morning11:15–12:45
Time SeriesNikolas Vasilas
Afternoon14:15–15:45
NetworkingErika Korb
Konstantinos Kovlakas
Paolo Bonfinicoordination: Giorgos VernardosIn this session, our lecturers will share real-life professional experiences outside academia. Students will have the opportunity to learn about expectations in the broader job market, how to apply their data science skills in different professional contexts, which requirements are commonly expected, and how to improve their chances of application success.
Attendance to this session is optional.
Konstantinos Kovlakas
Paolo Bonfinicoordination: Giorgos VernardosIn this session, our lecturers will share real-life professional experiences outside academia. Students will have the opportunity to learn about expectations in the broader job market, how to apply their data science skills in different professional contexts, which requirements are commonly expected, and how to improve their chances of application success.
Attendance to this session is optional.
Evening
Social DinnerDetails will be communicated.
Thursday · 30 July
Morning9:30–11:00
ML Best PracticesGrigoris Maravelias
Morning11:15–12:45
DL Intro + RegressionGrigoris Maravelias
Afternoon14:15–15:45
GPUGiorgos Vernardos
Afternoon16:00–17:30
NN ExplainabilityArnab Lahiry
Friday · 31 July
Morning9:30–11:00
SBIGiorgos Vernardos
Morning11:15–12:45
Diffusion ModelsGrigoris Maravelias
Afternoon14:15–15:45
Intro to AgentsPaolo Bonfini
Afternoon16:00–17:30
Closing Remarks