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IACM Colloquium (Hybrid mode) - Sofia Triantafillou , 15:00 7th April 2022- Payatakes Room (main building)



ΘΕΜΑ: IACM Colloquium (Hybrid mode) - Sofia Triantafillou , 15:00 7th April 2022- Payatakes Room (main building)

ΑΠΟΣΤΟΛΕΑΣ: Aikaterini Papadoulaki <kpapad@xxxxxx>

 

 

 

 

 

CAUTION: New zoom link & new room!

 

Speaker:

Professor Sofia Triantafillou

 

Affiliation:

Department of Mathematics & Applied Mathematics, University of Crete

 

Title:

Learning treatment effects from observational and experimental data.

 

Abstract:

Much of intelligent behavior involves predicting the causal effects of actions. Observational data are plentiful in many domains, such as electronic health record (EHR) data in the healthcare domain.

However, the use of observational data to estimate causal effects may produce highly biased estimates. Experimental data, such as randomized controlled trials (RCTs) in healthcare, provide relatively unbiased estimates of causal effects, but are much scarcer. This talk will describe a method that uses experimental data to determine whether and how to use observational data to estimate causal effects. One potential application of the method is personalized patient-outcome prediction from EHR and RCT data.

 

Short Bio:

Sofia Triantafillou is an Assistant Professor in the Department of Mathematics and Applied Mathematics  in the University of Crete.

She has studied Applied Mathematics at NTUA and has a PhD in Computer Science from the University of Crete. Before joining the University of Crete, she was an Assistant Professor in the University of Pittsburgh.

Her research involves causal discovery and inference from multiple sources of data, and applications of causal inference. She is currently working on tuning causal discovery algorithms, and on integrating observational and experimental data to improve causal effect estimation.

 

Time, Date & Location:

15:00, Thursday 7th April 2022 @ Payatakes Room (main building)

 

Zoom Info:

https://us06web.zoom.us/j/7761040361?pwd=dEs4TFZxck01Vk5PQWR1TUxKdndwUT09

Meeting ID: 776 104 0361

Passcode: 787479

 

 



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